experimental and statistical investigation of … · experimental and statistical investigation of...

EXPERIMENTAL AND STATISTICAL

INVESTIGATION OF AUSTRALIAN NATIVE

PLANTS FOR SECOND-GENERATION

BIODIESEL PRODUCTION

Md Jahirul Islam

B.Sc., M.Sc.

Supervisors: Dr Wijitha Senadeera, A/Prof Richard Brown, Prof Zoran Ristovski, A/Prof Ian O’Hara

A thesis submitted in fulfilment of the requirements for the degree of

Doctor of Philosophy (PhD)

School of Chemistry, Physics and Mechanical Engineering

Faculty of Science and Engineering

Queensland University of Technology

2015

To my parents for their love, support and encouragement

Keywords

Alternative energy, Artificial neural networks (ANN), Australian native plants, Biofuel,

Biodiesel, First-generation biodiesel, Multi criteria decision analysis (MCDA),

PROMETHEE-GAIA, Renewable energy, Second-Generation biodiesel, Fuel properties,

Principle component analysis (PCA), Response surface methodology (RSM), Analysis of

variance (ANOVA), Beauty leaf (Calophyllum inophyllum), Candle nut (Aleurites

Moluccana), Blue berry lily (Dianella Caerula), Queen palm (Syagrus Romanzoffiana),

Castor (Ricinus Communis), Bidwilli (Brachychiton Bidwilli), Karanja (Pongamia Pinnata),

Whitewood (Atalaya Hemiglauca), Cordyline (Cordyline Manners – Suttoniae), Flame tree

(Brachychiton Acerifolius), Chinese rain (Koelreuteria Formosana). Fatty acid methyl ester

(FAME), Chemical composition, Bio-oil extraction, physico-chemical properties, Fuel,

Transesterification , Pre-estrification, Nitrogen oxides (NOX), Diesel particulate matter

(DPM), Diesel engines, Particulate matter (PM), Performance, Exhaust emission

Abstract

The world is now facing a dual crisis of the rapid depletion of fossil fuels coupled with

environmental degradation. At present, the worlds’ energy supply largely depends on

petroleum based fuels, which have a finite supply and a restricted geographical availability.

Furthermore, the demand for energy is increasing rapidly due to the growing world

population and industrialisation. Therefore, development of a sustainable, long term

alternative fuel source has become critical. As a consequence, biodiesel made from various

crops, as well as animal fat, is receiving much attention of late and is emerging as an

alternative to conventional petroleum based fuels. The socio-economic advantages of using

biodiesel are many, including renewability, bio-degradability and low-toxicity compared with

petroleum fuels. The biodiesels which are commercially available today are mainly obtained

from edible vegetable oil feedstocks and are referred as first-generation biodiesels. Although

offering many advantages over conventional petroleum based fuels, the use of these types of

biodiesels are receiving serious condemnation due to their pressure on food sources and it is

this debate on "food versus fuel" which brings into question the sustainability of first-

generation biodiesels. Biodiesels produced from non-edible vegetable oils are considered a

possible alternative to overcome the socio-economic disadvantages of current biodiesel

technology. These types of biodiesel are called second-generation biodiesel, however they are

not commercially available today, mainly due to concerns in relation to secure feedstock

supply and a lack of technological assessment.

This study aimed to explore the potential of Australian native plants as a source of second-

generation biodiesel. The aim was achieved through several experimental measurements and

the numerical investigation of first-generation and second-generation biodiesel. While

conducting the research, necessary data were obtained from two sources: through

experimental investigation and data collected from literature published in peer reviewed

journals and conferences. A multivariate data analysis was conducted using principal

component analysis (PCA) to establish a correlation between important properties and the

chemical composition of biodiesel. The fuel properties investigated in this study were

kinematic viscosity, density, higher heating value, oxidation stability, cold filter plugging

point temperature, flash point temperature and iodine value. Using the data obtained, together

with results of the correlation study, a set of artificial neural network (ANN) models were

developed to estimate the above mentioned fuel properties of biodiesel. MatLab R2012a

software was used to train, validate and simulate the ANN model on a personal computer.

The network architecture was optimised using a trial and error methodology, in order to

obtain the best performance of the model. The ANN models were development in such a way

that they would be able to estimate the fuel properties for biodiesel obtained from Australian

native plants according to their respective chemical composition. For this purpose, biodiesels

were produced on a laboratory scale from eleven non-edible oil seed plants, as follows:

Beauty leaf (Calophyllum inophyllum), Candle nut (Aleurites Moluccana), Blue berry lily

(Dianella Caerula), Queen palm (Syagrus Romanzoffiana), Castor (Ricinus Communis),

Bidwilli (Brachychiton Bidwilli), Karanja (Pongamia Pinnata), Whitewood (Atalaya

Hemiglauca), Cordyline (Cordyline Manners – Suttoniae), Flame tree (Brachychiton

Acerifolius), Chinese rain (Koelreuteria Formosana). These plants are Australian natives and

naturally grow in Queensland. During the production of biodiesels from these native oil

seeds, the level of difficulty of seed processing, oil content in the seed kernels and free fatty

acid (FFA) content were also measured and reported. The chemical composition of the

obtained biodiesel samples were also measured in terms of fatty acid methyl ester (FAME)

profiles. The fuel properties of biodiesel were estimated by using chemical composition as the

input variable to developed ANN models. Based on physico-chemical properties, the native

species feedstock were then evaluated, and their quality was compared with each other using

a multi-criteria decision method (MCDM) software PORMETHEE-GAIA. In addition,

sensitivity analysis of native plant ranking was investigated by changing the weighting of

three important criteria: oil yield; oxidation stability and cold filter plugging point

temperature. This study found that among the native plant feedstock investigated, Beauty leaf

was the top ranked candidate for second-generation biodiesel production followed by Queen

palm, Castor and Karanja. Furthermore, Beauty leaf and Queen palm biodiesels were found

to be the better choice for tropical/sub-tropical regions, however the opposite was true for

cold weather conditions, where Castor, Cordyline and Flame trees were the best candidates

for biodiesel production.

In the next phase of the study, biodiesel from Beauty leaf oil seeds was produced on a pilot

scale and experimental evaluation of the fuel properties, emission and performance were

tested in a multi-cylinder diesel engine. While extracting bio-oil from the Beauty leaf oil

seeds, the effect of seed preparation, moisture content and oil extraction methods on bio-oil

yield were experimentally investigated. The oil extraction methods studied in this work were:

(1) mechanical oil extraction using a screw press, (2) static chemical oil extraction under

atmospheric conditions and (3) accelerated chemical oil extraction using high pressure and

temperature. In both chemical extraction methods, n-Hexane was used as the oil solvent. The

physico-chemical properties of Beauty leaf oil and biodiesel were determined experimentally

and compared with that of commercially available biodiesel. Due to the high concentration of

free fatty acid contained in Beauty leaf, a two-step biodiesel conversion method consisting of

acid catalysed pre-esterification and alkali catalysed transesterification was used. The

performance of biodiesel conversion and esterification reaction parameters were investigated

using response surface methodology (RSM) based on a Box-Behnken experimental design.

This study found that seed preparation and moisture content in the oil bearing seed kernels

had a significant impact on oil yields, especially in terms of the mechanical oil extraction

method. High temperature and pressure during the extraction process was also found to

increase the oil extraction performance. A clear difference was found in the physical

properties of Beauty leaf oils obtained using different oil extraction methods, which

ultimately affected the oil to biodiesel conversion process. However, Beauty leaf oil methyl

esters (biodiesel) were very consistent in terms of their physico-chemical properties and were

able to compete with commercially available biodiesel in terms of fuel quality. The reaction

conditions for the largest reduction in FFA concentration for acid catalysed pre-esterification

was 30:1 methanol to oil molar ratio, 10% (w/w) sulphuric acid catalyst loading and 75 °C

reaction temperature. In the alkali catalysed transesterification process a 7.5:1 methanol to oil

molar ratio, 1% (w/w) sodium methoxide catalyst loading and 55 °C reaction temperature

were found to result in the highest FAME conversion. The performance and emissions of a

four-cylinder common rail diesel engine were experimentally investigated using neat diesel

and biodiesel produced from Beauty leaf oil. Results indicated that 5% and 10% blends of

Beauty leaf oil biodiesel with diesel fuel can be used in conventional diesel engines without

engine modification. Beauty leaf biodiesel reduced the engine power, brake thermal

efficiency, cylinder peak pressure and specific nitrogen oxide (NOx) particle mass (PM)

emissions. At the same time, brake specific fuel consumption and particle number emissions

were found to be higher from Beauty leaf biodiesel compared with that of conventional diesel.

However, this variation is not unusual and is commonly found in conventional biodiesels,

mainly due to variations in physico-chemical properties between biodiesel and conventional

diesel.

This thesis advances knowledge in the field biofuel technology, by delivering an extensive

database of the properties of second-generation biodiesel and its application in a modern

diesel engine. The research methodology and numerical model developed in this study can be

used for a broad range of biodiesel feedstock and will facilitate further biodiesel research in

the future. The experimental study on modern automobile engines using Beauty leaf biodiesel

indicated the suitability of Australian native plants for use as fuel for modern automobile

diesel engines without engine modification. Therefore, the findings of this study are expected

to serve as the basis for further developments in the use of Beauty leaf as a feedstock for

industrial scale biodiesel production.

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Table of Contents

Table of Contents ..................................................................................................................................... i

List of Figures ......................................................................................................................................... v

List of Tables ...................................................................................................................................... viii

List of Abbreviations ............................................................................................................................... x

List of Publication ................................................................................................................................ xii

Published journal papers: ..................................................................................................................... xii

Conference papers: ............................................................................................................................... xii

Statement of Original Authorship ........................................................................................................ xiv

Acknowledgements ............................................................................................................................... xv

CHAPTER 1: INTRODUCTION ....................................................................................................... 1

1.1 Background .................................................................................................................................. 1

1.2 Research questions ....................................................................................................................... 4

1.3 Aims and objectives ..................................................................................................................... 4 1.3.1 Aims ................................................................................................................................. 4 1.3.2 Specific study objectives .................................................................................................. 4

1.4 Significantce of this project ......................................................................................................... 5

1.5 Thesis outline ............................................................................................................................... 6

CHAPTER 2: LITERATURE REVIEW ......................................................................................... 13

1 Introduction ................................................................................................................................ 15

2.1 Biodiesel .................................................................................................................................... 19 2.1.1 Biodiesel feedstock ......................................................................................................... 20 2.1.2 First and second-generation biodiesel ............................................................................. 21 2.1.3 Potential second-generation biodiesel feedstock ............................................................ 23 2.1.4 Production of biodiesel ................................................................................................... 26 2.1.5 Chemical composition of biodiesel ................................................................................. 29 2.1.6 Biodiesel standards ......................................................................................................... 31 2.1.7 Fuel properties ................................................................................................................ 32 2.1.7.1 Kinematic viscosity ........................................................................................................ 33 2.1.7.2 Density ............................................................................................................................ 35 2.1.7.3 Cetane number (CN) ....................................................................................................... 36 2.1.7.4 Heating (calorific) Value ................................................................................................ 37 2.1.7.5 Flash point ...................................................................................................................... 37 2.1.7.6 Oxidation stability .......................................................................................................... 38 2.1.7.7 Cold temperature properties ........................................................................................... 38 2.1.7.8 Lubricity ......................................................................................................................... 40 2.1.7.9 Iodine value .................................................................................................................... 41

2.2 Biodiesel as a diesel Engine Fuel ............................................................................................... 41 2.2.1 Engine performance ........................................................................................................ 42 2.2.2 Exhaust emissions ........................................................................................................... 44

2.3 Artificial neural networks .......................................................................................................... 47 2.3.1 ANN in predicting engine emission and performance .................................................... 51 2.3.2 ANN in predicting fuel properties .................................................................................. 53

2.4 ANN modeling of second-generation biodiesel ......................................................................... 57

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2.5 Conclusions ............................................................................................................................... 59

CHAPTER 3: ARTIFICIAL NEURAL NETWORK (ANN) MODEL DEVELOPMENT ......... 61

3.1 Introduction ............................................................................................................................... 62

3.2 Data Collection .......................................................................................................................... 65

3.3 Results and discussion ............................................................................................................... 71 3.3.1 Chemical composition .................................................................................................... 71 3.3.2 Fuel properties ................................................................................................................ 74 3.3.3 Correlation of chemical composition and fuel properties ............................................... 78 3.3.4 Principle component analysis ......................................................................................... 82 3.3.5 ANN model development ............................................................................................... 84 3.3.6 Evaluation of ANN model performance ......................................................................... 88

3.4 Conclusion ................................................................................................................................. 91

CHAPTER 4: BIODIESEL FROM AUSTRALIAN NATIVE PLANTS ...................................... 93

4.1 Introduction ............................................................................................................................... 93

4.2 Potential native oil seed plants ................................................................................................... 94 4.2.1 Beauty leaf (Calophyllum inophyllum) ........................................................................... 94 4.2.2 Candle nut (Aleurites Moluccana) .................................................................................. 95 4.2.3 Blue berry lily (Dianella Caerula) ................................................................................. 96 4.2.4 Queen palm (Syagrus Romanzoffiana) ........................................................................... 97 4.2.5 Castor (Ricinus Communis) ............................................................................................ 98 4.2.6 Bidwilli (Brachychiton Bidwilli) .................................................................................... 99 4.2.7 Karanja (Pongamia Pinnata) ........................................................................................ 100 4.2.8 Whitewood (Atalaya Hemiglauca) ............................................................................... 101 4.2.9 Cordyline (Cordyline Manners – Suttoniae) ................................................................ 102 4.2.10 Flame tree (Brachychiton Acerifolius) .......................................................................... 102 4.2.11 Chinese rain (Koelreuteria Formosana) ....................................................................... 103

4.3 Seed Collection And Preparation ............................................................................................. 104 4.3.1 Kernel extraction .......................................................................................................... 104 4.3.2 Kernel grinding ............................................................................................................. 105 4.3.3 Kernel drying ................................................................................................................ 105

4.4 Oil extraction ........................................................................................................................... 106

4.5 Chemical composition ............................................................................................................. 109

4.6 fuel properties .......................................................................................................................... 113

4.7 Evaluation of native plant methyle ester .................................................................................. 116

4.8 Conclusion ............................................................................................................................... 120

CHAPTER 5: PILOT SCALE BEAUTY LEAF (CALOPHYLLUM INOPHYLLUM) BIODIESEL PRODUCTION .......................................................................................................... 123

5.1 Introduction ............................................................................................................................. 125

5.2 Seed preparation ...................................................................................................................... 127 5.2.1 Seeds collection ............................................................................................................ 128 5.2.2 Kernel extraction .......................................................................................................... 128 5.2.3 Kernel drying ................................................................................................................ 129 5.2.4 Kernel grinding ............................................................................................................. 129

5.3 Oil extraction ........................................................................................................................... 129 5.3.1 Mechanical oil extraction using oil press (OP) ............................................................. 130 5.3.2 Chemical oil extraction using n-Hexane (nHX) ........................................................... 131 5.3.3 Accelerated solvent extraction (ASE) .......................................................................... 131

5.4 Oil Yield .................................................................................................................................. 132

5.5 Comparison of oil extraction methods ..................................................................................... 133

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5.6 Oil Analysis ............................................................................................................................. 135

5.7 Biodiesel production ................................................................................................................ 136

5.8 Biodiesel analysis .................................................................................................................... 140

5.9 Fuel properties ......................................................................................................................... 143 5.9.1 Kinematic viscosity ...................................................................................................... 144 5.9.2 Density .......................................................................................................................... 144 5.9.3 Higher heating value ..................................................................................................... 145 5.9.4 Acid number ................................................................................................................. 145 5.9.5 Oxidation stability ........................................................................................................ 146 5.9.6 Iodine value .................................................................................................................. 146 5.9.7 Cetane Number ............................................................................................................. 147 5.9.8 Flash point temperature ................................................................................................ 147 5.9.9 Cold filter plug point (CFPP)........................................................................................ 148

5.10 Validation of beauty leaf biodiesel .......................................................................................... 150

5.11 Conclusion ............................................................................................................................... 153

CHAPTER 6: PRODUCTION PROCESS OPTIMISATION OF BIODIESEL ........................ 157

6.1 Introduction .............................................................................................................................. 159

6.2 Materials and method ............................................................................................................... 163 6.2.1 Beauty leaf oil extraction method ................................................................................. 163 6.2.2 Analysis methods .......................................................................................................... 163 6.2.3 Pre-esterification and transesterification methods ........................................................ 164

6.3 Results and discussion ............................................................................................................. 167 6.3.1 Beauty leaf oil characterisations ................................................................................... 167 6.3.2 Acid-catalysed pre-esterification .................................................................................. 168 6.3.3 Base-catalysed transesterification of pre-esterified Beauty leaf oil .............................. 172

6.4 Conclusions .............................................................................................................................. 176

CHAPTER 7: DIESEL ENGINE TESTING WITH BIODIESEL OF CONTROLLED CHEMICAL COMPOSITION ........................................................................................................ 177

7.1 Introduction: ............................................................................................................................. 179

7.2 Materials and methods ............................................................................................................. 181 7.2.1 Engine and fuel specification ........................................................................................ 181 7.2.2 Exhaust sampling and measurement system ................................................................. 183

7.3 Results and Discussion ............................................................................................................ 186 7.3.1 Specific PM emissions .................................................................................................. 186 7.3.2 Specific PN emissions .................................................................................................. 187 7.3.3 Particle number size distribution .................................................................................. 189 7.3.4 Particle median size ...................................................................................................... 189 7.3.5 NOx emissions .............................................................................................................. 190 7.3.6 Influence of fuel physical properties and chemical composition on particle

emissions ...................................................................................................................... 192 7.3.7 Comparison of engine performance and particle emissions among used

biodiesels ...................................................................................................................... 194

7.4 Conclusions .............................................................................................................................. 195

CHAPTER 8: AUTOMOBILE DIESEL ENGINE TESTING WITH BEAUTY LEAF BIODIESEL 205

8.1 Introduction .............................................................................................................................. 205

8.2 Instrumentation and methodology ........................................................................................... 206

8.3 ResultS and discussion ............................................................................................................. 208 8.3.1 Engine power ................................................................................................................ 208 8.3.2 BTE and BSFC ............................................................................................................. 209

iv

8.3.3 Cylinder pressure .......................................................................................................... 210 8.3.4 Nitrogen oxide (NOx) emission .................................................................................... 212 8.3.5 Particle mass (PM) and particle number (PN) .............................................................. 213

8.4 Conclusion ............................................................................................................................... 214

CHAPTER 9: CONCLUSIONS ...................................................................................................... 217

9.1 ConclusionS arising from this thesis ........................................................................................ 217

9.2 Limitations and Recommendations for future work ................................................................ 223

6 BIBLIOGRAPHY .......................................................................................................................... 227

APPENDICES ................................................................................................................................... 266 APPENDIX A: MatLab code for ANN models training......................................................... 266 APPENDIX B: The eigenvalue for each of the PCs ............................................................. 270

v

List of Figures

Figure 1-1: Outline on the thesis ................................................................................ 11

Figure 2-1. Biodiesel feedstocks around the world .................................................... 21

Figure 2-2. Transeterification reaction ....................................................................... 28

Figure 2-3: Soap formation ........................................................................................ 29

Figure 2-4: Acid pre-esterification ............................................................................. 29

Figure 2-5: Fatty acid profile of various biodiesel fuels ............................................ 31

Figure 2-6: Variation in fuel properties of various biodiesel ..................................... 33

Figure 2-7: Schematic diagram of a typical diesel engine fuel system ...................... 42

Figure 2-8: Biological neuron .................................................................................... 48

Figure 2-9: Multi-layer ANN model .......................................................................... 49

Figure 2-10: Working principle of ANN ................................................................... 49

Figure 2-11: Comparison of the performance of between ANN and various linear and non-linear prediction techniques ................................................ 50

Figure 2-12: Proposed structure of ANN model ........................................................ 58

Figure 3-1: Number and average weight in percentages of fatty acid methyl esters found in the samples .......................................................................... 74

Figure 3-2: Correlation of (a) C18:2 with oxidation stability; (b) H2 with CN ......... 77

Figure 3-3: Correlation of (a) ANDB with CN; (b) ANBD with IV ......................... 79

Figure 3-4: Effect of ACL on biodiesel (a) kinematic viscosity (KV) and (b) higher heating value (HHV) ......................................................................... 81

Figure 3-5: Principle component analysis and correlation of biodiesel properties with chemical composition: ........................................................ 84

Figure 3-6: Proposed flow chart of ANN prediction model development ................. 85

Figure 3-7: Structure of ANN .................................................................................... 86

Figure 3-8: Analysis of influence between chemical composition and fuel properties ...................................................................................................... 87

Figure 3-9: Biodiesel properties estimation accuracy of developed ANN models .......................................................................................................... 91

Figure 4-1: Beauty leaf tree growing along a beach front, and in a park and its distribution in Australia ............................................................................... 95

Figure 4-2: The tree and kernels of Candle nut ......................................................... 96

Figure 4-3: Blue berry lily plant and seeds. ............................................................... 97

Figure 4-4: Tree and kernels of Queen palm ............................................................. 98

Figure 4-5: Shrub and seeds of Castor ....................................................................... 99

vi

Figure 4-6: Bidwilli plant and seeds ......................................................................... 100

Figure 4-7: Karanja fruit and seeds ......................................................................... 101

Figure 4-8: Tree and fruit of Whitewood .................................................................. 101

Figure 4-9: The tree and fruit of Cordyline .............................................................. 102

Figure 4-10: Flame tree, fruit and seeds .................................................................. 103

Figure 4-11: Chinese rain tree and fruits ................................................................. 104

Figure 4-12: Kernel extraction ................................................................................. 105

Figure 4-13: Ground kernels .................................................................................... 106

Figure 4-14: ASE 350 cell loading process .............................................................. 107

Figure 4-15: n-hexan removing using DionexTM SETM 400 ..................................... 108

Figure 4-16: Oil yield of native plant seed kernels .................................................. 109

Figure 4-17: Extracted bio-oil sample from native plants ........................................ 109

Figure 4-18: (a) Graphical Analysis for Interactive Assistance (GAIA) plot for eight biodiesel showing 11 criteria and decision vector. (b) Corresponding complete ranking and Phi value of biodiesel from native plants ............................................................................................... 118

Figure 5-5-1: Flow chart of Beauty leaf seeds preparation ...................................... 128

Figure 5-5-2: Mechanical oil extraction through a screw press ............................... 130

Figure 5-5-3: Chemical oil extraction ...................................................................... 131

Figure 5-5-4: ASE oil extraction (a) Dionex™ ASE 350® (b) solvent removal with flow of nitrogen .................................................................................. 132

Figure 5-5-5: Beauty leaf oil yield from three different extraction methods. .......... 133

Figure 5-5-6: Soap formation in oils contains high FFA ......................................... 137

Figure 5-5-7: Acid pre-esterification ........................................................................ 137

Figure 5-5-8: Two step bio-diesel production process from Beauty leaf oil ............ 139

Figure 5-5-9: Beauty leaf oil esterification .............................................................. 139

Figure 5-5-10: (a) Graphical Analysis for Interactive Assistance (GAIA) plot for eight biodiesel showing 10 criteria and decision vector. (b) Corresponding ranking of biodiesel on their outranking flow. .................. 151

Figure 6-6-1: (a) Esterification and transesterification reactor; (b) Layer of Methanol-Water (top) and oil (bottom) after acid-catalysed pre-esterification; (c) Layer of Beauty leaf oil methyl ester (top) and glycerol (bottom) after base-catalysed Trans-esterification. ...................... 165

Figure 6-6-2: Scatter diagram of experimental FFA (%) and predicted FFA (%) of a linear model. ....................................................................................... 170

Figure 6-6-3: Response surface of FFA content against .......................................... 171

Figure 6-6-4: Scatter diagram of experimental and calculated FAME (%) of full quadratic model. .................................................................................. 174

vii

Figure 6-6-5: Response surface ester content against catalyst concentration vs. (a) methanol to oil molar ratio at 55 °C; (b) reaction temperature at 7.5:1 methanol to oil molar ratio. ............................................................... 175

Figure 7-7-1: Schematic diagram of used engine exhaust measurement system ..... 185

Figure 7-7-2: Brake specific PM emission at ........................................................... 187

Figure 7-7-3: Brake specific PN emissions at 1500 rpm 100% load (a) and 2000 rpm 100% load (b). ........................................................................... 188

Figure 7-7-4: Variations in particle median size among used fuels at 1500 rpm 100% load (a) and 2000 rpm 100% load (b), while (c) shows particle median size variation with total number concentration. ............................ 190

Figure 7-7-5: Brake specific NOx emission at (a) 1500 rpm 100% load and (b) 2000 rpm 100% load. ................................................................................. 192

Figure 7-7-6: Variation in specific PM and PN emissions with used fuel surface tension, viscosity and oxygen content ........................................... 193

Figure 7-7-7: Comparison of engine performance (power, BSFC) and particle emissions (PM, PN) among biodiesels and their blends where petroleum diesel was used as a reference fuel. .......................................... 195

Figure 8-8-1: Experimental setup............................................................................. 207

Figure 8-8-2: Variation of power output for neat diesel and biodiesel blends (a) brake power; (b) indicated power .............................................................. 209

Figure 8-8-3: (a) Brake thermal efficiency (BTE) and (b) Brake specific fuel consumption (BSFC) for neat diesel and biodiesel blends ........................ 210

Figure 8-8-4: Engine cylinder pressure for diesel and Beauty leaf biodiesel blends, (a) full load; (b) 75% load; (c) 50% load; and (d) 25% load ......... 211

Figure 8-8-5: NOX emission for diesel and BOME blend for different engine load conditions ........................................................................................... 213

Figure 8-8-6: Particle emission for diesel and BOME blend in different engine load condition (a) Brake specific particle mass (PM); (b) brake specific particle number ............................................................................. 214

viii

List of Tables

Table 2-1. Advances in biodiesel technology ............................................................ 22

Table 2-2: Second-generation biodiesel feedstock containing oil by dry weight ...... 26

Table 2-3: Reported optimum conditions for transesterification of oils for biodiesel production. .................................................................................... 28

Table 2-4: Chemical structure of common fatty acid in biodiesels ............................ 30

Table 2-5: International biodiesel standards .............................................................. 32

Table 2-6: Performance and emission of diesel engines with biodiesel .................... 43

Table 2-7: ANN used in automobile engine application ............................................ 55

Table 2-8: ANN in predicting fuel properties ............................................................ 56

Table 3-1 Biodiesel property test standard ................................................................. 66

Table 3-2: Biodiesel datasets investigated in this study ............................................. 68

Table 3-3: Structural formulae for fatty acids methyl ester found in biodiesel samples ......................................................................................................... 71

Table 3-4: Chemical composition of tested biodiesel ................................................ 72

Table 3-5: BREF experimental results of biodiesel properties .................................. 73

Table 3-6: Summary of the secondary data for biodiesel properties .......................... 76

Table 3-7: Number of input variables and optimised number of neuron in ANN model ............................................................................................................ 88

Table 4-1: Fatty acid profile and chemical composition of bio-oil produced from native plants ....................................................................................... 112

Table 4-2: Estimated biodiesel properties ................................................................ 115

Table 4-3: Variables and preference used in PROMETHEE-GAIA analysis .......... 117

Table 4-4: Comparative rank shift with different weighting for bio-oil yield ......... 119

Table 4-5: Comparative rank shift with different oxidation stability of biodiesel ... 119

Table 4-6: Comparative Rank shift with different cold filter plugging point temperature ................................................................................................. 120

Table 5-1: Advantages and disadvantages of the three extraction methods ............ 134

Table 5-2: Physical properties of Beauty leaf oil ..................................................... 136

Table 5-3: The fatty acid distributions of Beauty leaf and commercial biodiesels .................................................................................................... 142

Table 5-4: Fuel properties of Beauty leaf oil biodiesel and commercial biodiesels .................................................................................................... 149

Table 5-5: Variables and preference used in PROMETHEE-GAIA analysis .......... 150

Table 5-6: Comparative rank shift with different OS and CFPP weighting ............ 152

ix

Table 6-1: Experimental range and levels of independent variables ....................... 165

Table 6-2: Coded experimental design .................................................................... 165

Table 6-3: Fatty acid composition of Beauty leaf oil ............................................... 167

Table 6-4: Properties of Beauty leaf oil. .................................................................. 167

Table 6-5: Experimental conditions and results for acid-catalysed pre-esterification ............................................................................................... 169

Table 6-6: Regression coefficients for %FFA prediction ........................................ 170

Table 6-7: Experimental data for base-catalysed trans-esterification. ..................... 172

Table 6-8: Regression coefficients for FAME (%) prediction ................................. 173

Table 7-1: Test engine specification ........................................................................ 181

Table 7-2: Fatty acid profile of used biodiesels ....................................................... 183

Table 7-3: Important physicochemical properties of tested fuels ............................ 185

Table 8-1: Properties of Beauty leaf fatty acid methyl ester (BOME) and petroleum diesel ......................................................................................... 206

Table 8-2: Test engine specification ........................................................................ 208

x

List of Abbreviations

AFR air-fuel ratio ANN artificial neural network ANOVA analysis of variance BMEP brake mean effective pressure BSFC brake-specific fuel consumption BTE brake thermal efficiency C cylinder CB cylinder bore CFPP cold-filter plugging point CI compression ignition CME coconut methyl ester CN cetane number CNG compressed natural gas CO carbon monoxide CO2 carbon dioxide CP cloud point DU degree of unsaturation ECP Engine cylinder pressure CPO crude palm oil CR compression ratio EGR exhaust gas recirculation ES engine stock ET engine temperature FAME fatty acid methyl ester FAEE fatty acid ethyle ester FDR fuels blend ratio FFA free fatty acid FFR fuel flow rate GHG greenhouse gas H2 hydrogen HC hydrocarbon HV heating value HHV higher heating value ICE internal combustion engines IP injection pressure IT injection timing

IV iodine value KV kinematic viscosity L load Lb lubricity LHV lower heating value LPG liquid petroleum gas MLR multiple linear regression MRE mean relative error MSE mean square error N2 nitrogen NA naturally aspirated NOx nitrogen oxides O2 oxygen OS oxidation stability P power PCA principle component analysis PCR principle component regression PLS partial least square regression PM particulate matter PME palm oil methyl ester PP pour point R2 regression coefficient RME rapeseed methyl ester RPM rotation per minute S sulphur SI spark ignition SME soybean methyl ester SOx sulphur oxides T torque TC turbocharged Texh exhaust gas temperature TP throttle position UHC unburned hydrocarbons VT valve timing WCO waste cooking oil

xii

List of Publications

Published journal papers:

1. M. I. Jahirul, K. Wenyong, L Moghaddam, R. J. Brown, I. O'Hara, W. Senadeera, N. Ashwath. Biodiesel production from non-edible Beauty Leaf (Calophyllum inophyllum) oil: process optimization using response surface methodology (RSM), Energies 7(8), 5317-5331, 2014. I.F. 1.884.

2. M. M. Rahman, A. M. Pourkhesalian, M. I. Jahirul, S. Stevanovic, P. X. Pham, H. Wang, A.R. Masri, R. J. Brown and Z. D. Ristovski. Particle emissions from biodiesels with different physicochemical properties, Fuel 134, 201-208, 2014. IF. 3.357

3. M. I. Jahirul, R. J. Brown, W. Senadeera, Z Ristovski, I O'Hara. Artificial neural network approach in identifying sustainable future generation biofuel feedstock, Energies, Special issue: Alternative Fuels for the Internal Combustion Engines (ICE), 6, 3764-3806, 2013. I.F. 1.884.

4. M. I. Jahirul, J. R. Brown, W. Senadeera, N. Ashwath, C. Laing, J. Leski-Taylor, and M. G. Rasul. Optimisation of Bio-Oil Extraction Process from Beauty Leaf (Calophyllum Inophyllum) Oil Seed as a Second-Generation Biodiesel Source, Procedia Engineering, 56, 619-24, 2013.

Submitted Paper: 1. M. I. Jahirul, W. Senadeera, J. R. Brown, , N. Ashwath, M. G. Rasul, M. M.

Rahman, Muhammad Aminul Islam, and I. M. O’Hara. Physico-chemical Assessment of Beauty Leaf (Calophyllum Inophyllum) as Second-Generation Biodiesel Feedstock. Submitted to the journal of Energy Conversion and Management.

Conference papers:

1. M. I. Jahirul, W. Senadeera, R. J. Brown, L. Moghaddam. Estimation of Biodiesel Properties from Its Chemical Composition – An Artificial Neural Network (ANN) Approach. International Conference on Environment and Renewable Energy, Cité Internationale Universitaire de Paris, 17 Boulevard Jourdan, 75014 Paris – France, 7-8 May 2014.

2. M. I. Jahirul, W. Senadeera, P. Brooks, R.J. Browna, R. Situ, P.X. Pham and A.R. Masri. An Artificial Neutral Network (ANN) Model for Predicting Biodiesel Kinematic Viscosity as a Function of Temperature and Chemical Compositions. 20th International Congress on Modelling and Simulation, Adelaide, Australia, 1–6 December 2013, Paper no. 1021

xiii

3. Jahirul M. I., Brown R. J, Senadeera W, Z Ristovski. Influence of the physical properties of fractionated methyl ester on the ultra-fine particle emission of internal combustion engine. 8th Australia and New Zealand Aerosol Workshop, 26-27 November 2012. Canberra, Australia.

xiv

Statement of Original Authorship

The work contained in this thesis has not been previously submitted to meet

requirements for an award at this or any other higher education institution. To the

best of my knowledge and belief, the thesis contains no material previously

published or written by another person except where due reference is made.

Signature: QUT Verified Signature

Date: 17/04/2014

xv

Acknowledgements

First of all, I would like to express my praise to Almighty Allah Subhanawatala for

giving me the opportunity to finish the thesis successfully. I would like to express my

gratitude and profound respect to my supervisors Dr Wijitha Senadeera, A/Prof

Richard Brown, Prof Zoran Ristovski and A/Prof Ian O’Hara. Their supervision,

guidance and comments helped me finish the research work. I am grateful for their

generosity and amicable nature. I found them to be not only great supervisors and

scientists, but also caring and supportive men. Further, I should also express my

particular gratitude to A/Prof Richard Brown for his excellent support,

encouragement, guidance and care during my research journey.

My sincere appreciation goes to Queensland University of Technology for providing

me scholarships and access to various research instruments/facilities. Further, I

should thank the Science and Engineering Faculty and the School of Chemistry,

Physics and Mechanical Engineering at QUT for their continued administrative

support and financial assistance, particularly for conference and workshop

attendance. I would like to express thanks to the Biofuel Engine Research Facility

(BERF), QUT, for providing engine testing facilities. I would also like to extend my

thanks to all researchers and staff of this facility. Special thanks go to Mr Noel

Hartnett, Mr Scott Abbett, Mr. Tony Morris, Mr. Nathaniel Raup, Mr Shane Russel

and other technical staff of QUT, who also contributed to this work. I would also like

to extend my gratitude to Dr. Lalehvash Moghaddam, Centre for Tropical Crops and

Biocommodities (CTCB), QUT, for her significant contribution in this project.

I would like to thank my friends and colleagues at QUT for their continuous support

and encouragement, without which my PhD journey might be more difficult. Special

thanks to Chaminda Prasad Karunasena Helambage, M. Aminul Islam, Meisam

Babaie, Md Mostafizur Rahman, Prof. Nurun Nabi, Dr. Timothy Bodisco, Kabir

Suara, Farhad Hossain and many others, whom I always found beside me during this

project. I wish to commend the efforts of all undergraduate and postgraduate students

involved in this project for their assistance, especially, Wenyong Koh, Jakub L.

xvi

Taylor, Cameron Laing, Eerond Parez, Luke Asgill, Fadil Darmanto, Jagaddhita

Pandya Atmaja, Bodhimula Satyajati and Mohammad Hariz Bin Mokhtar.

I would like to thank A/Prof Nanajapa Ashwath and A/Prof Muhammad Rasul, as

well as all technical staff from the Centre for Plant and Water Sciences (CPWS),

CQU, for their sincere support during my Rockhampton bio-oil extraction visit. I

would like to acknowledge my gratitude to Professor Henning Bockhorn and

Michael Stroebele from the Karlsruhe Institute of Technology (KIT), Germany, for

their immeasurable support and guidance during my visit to Germany for biodiesel

property testing. Further, I am very thankful to A/Prof Bo Feng and George Thomas

from the University of Queensland (UQ), for their excellent support during the

engine testing campaign with Australian native plants. I would also like to express

my thank to Dr. Peter Brooks, University of the Sunshine Coast (USC) for extending

his support to my project and helping to determine the chemical composition of

biodiesel using his lab facilities.

Further, I should be very thankful to my wife Mst. Halima Akhter for her unique

love, passion, encouragement and commitment. She has been so close to me all the

time, along with our little daughter Arisha Islam, who has tried her best to cheer her

father with lovely smiles or cheerful activities. I also would like to thank my

relatives, friends and their families who helped me throughout the journey in many

ways.

Last but not least, I would like to acknowledge with gratitude, the support and love

of my parents, Abdul Mannan Mozumder and Mst. Nojumer Nesa. Although their

life is full of up-and-down situations and financial hardship, they always try to keep

me on the right track and consistently encourage me for moving forward with my

study. I strongly believe that this thesis would not be possible without their sincere

effort and wishes.

Chapter 1: Introduction 1

Chapter 1: Introduction

1.1 BACKGROUND

The current global energy supply is based on petroleum fuels (oil, natural gas, coal)

of which the reserves are finite. Given the growing world population, the increasing

energy consumption per capita and global warming due to greenhouse gas emissions,

the necessity of identifying long-term alternative energy sources is well recognised.

In order to counter greenhouse gas emissions, the European Union ratified the Kyoto

Protocol in 2002 and emphasised the potential for scientific innovation, which

unfortunately has not yet been fully achieved. Atmospheric CO2 concentration has

already exceeded the allowable level 10 years earlier than had previously been

predicted (Stern 2008).

Although the transport sector occupies third place (after industry and the building

sector) when considering total global energy consumption and greenhouse gas

emissions, it is the fastest growing sector. By 2030, the energy consumption and CO2

emissions of this sector are predicted to be 80% above the levels seen today (Miller,

Schmidt and Shindell 2006). Besides, it is also the sector that most heavily depends

on petroleum fuel (through the oil-derived liquid products gasoline and diesel) and

currently consumes 30% of global petroleum oil, which is predicted to increase to

60% by 2030 (Luque et al. 2008). Furthermore, the availability of petroleum oil is

geographically restricted and the era of cheap and secure oil is almost over. These

facts have forced automobile researchers to look for alternative carbon neutral

transport fuels which promise an harmonious amalgamation of sustainable

development, energy conversion, energy efficiency and environmental preservation

(Jahirul et al. 2010). As yet, no such option for fuel has been fully developed for the

transportation sector. Moreover, cars which emit no greenhouse gases (electric, solar,

hydrogen etc.) are a long way from becoming mainstream vehicles. Therefore, the

development of a sustainable, long-term alternative fuel has become essential, with

2 Chapter 1: Introduction

biodiesel receiving much attention and presenting as a promising alternative to

conventional fossil fuel (Luque et al. 2008).

In recent years, biodiesels have received much attention as a sustainable alternative

for petroleum diesel. It is liquid fuel made from various oil seeds crops, as well as

animal fat. The socio-economic advantages of using biodiesel are many, including

the fact that it is renewable, bio-degradable, non-toxic and eco-friendly compared

with petroleum fuels. Biodiesels are now produced on an industrial scale around the

world, using edible oil feedstocks such as soybean oil, palm oil, sunflower oil, corn

oil, olive oil, mustard oil etc (Bannikov 2011; Benjumea, Agudelo and Agudelo

2008; Jahirul, Brown, Senadeera, O'Hara, et al. 2013). These are called first-

generation biodiesels. In general, biodiesel derived from these sources can be defined

as mono-alkyl esters of long chain fatty acids (Rashid and Anwar 2008a). The mono-

alkyl esters that are the main chemical species of biodiesel, have properties similar to

diesel fuel (Fernando et al. 2007) and it can be used in modern diesel engines in its

pure form (B100) or may be blended with petroleum diesel (Lebedevas and

Vaicekauskas 2006). Although the range of biodiesels available reveals the flexibility

and potential of the biodiesel industry, this potential has not been fully realised by

first-generation biodiesel systems due to serious social and environmental concerns

that it will lead to food price increases and creates pressure on the agricultural lands

usually used for food production. Those concerns make first-generation biodiesel

unlikely to be sustainable. Therefore, first-generation biodiesels are limited in their

ability to contribute to climate change mitigation, economic growth, and as substitute

for petroleum production (Bioenergy 2008). Consequently, there was a need to

develop new technologies for producing alternative feedstocks to overcome the

major short-comings of the supply of first-generation biodiesel fuels. The biodiesels

obtained from these new technologies have been defined as second-generation

biodiesels, which are generally produced from non-edible feedstocks (Posten and

Schaub 2009).

On the other hand, the sustainability of any new biodiesel fuels depends on their

quality and suitability for use in the internal combustion engine, with the majority of

current vehicle engines not optimised for the utilisation of biodiesel. Therefore, these


engines are unlikely to operate efficiently when using biodiesel and will often

experience technical problems such as carbon deposition, corrosion, high lubricating

oil contamination, poor low temperature performance, heavy gum and wax formation

compared to petroleum diesel (Jayed et al. 2009). The distinctions between

petroleum diesel and biodiesel may be attributed to variations in their physical

properties and chemical composition. Petroleum diesel is composed of hundreds of

compounds boiling at different temperatures (determined by the petroleum refining

process and crude oil raw material), whereas biodiesel contains compounds,

primarily C8 to 24 carbon chain length alkyl esters (determined entirely by the

feedstock) (Graboski et al. 2003). Besides the major fatty ester components, minor

constituents of biodiesel include intermediary mono- and di-glycerides and residual

triglycerides resulting from the transesterification reaction, as well as methanol, free

fatty acids etc (Knothe 2009). As engines are manufactured for use with petroleum

diesel, OEM’s (Original Equipment Manufacturers) and industry associations have

been cautious in their acceptance of biodiesel as a fuel source, especially those from

new sources and biodiesel blends because these cannot be easily verified (Haseeb et

al. 2011b)

Australia has a large land area on which oil seed crops can be cultivated for biodiesel

production. Therefore, Australia has the potential to become a leading biodiesel

producer. However, contrary to this opportunity, Australian energy supply has

consistently relied upon imported liquid fuels, and the market for biodiesel

production in Australia has not been expanded over the last few years. The factors

contributing to demand for biodiesel in Australia are mainly an inability to supply

consistent and reliable quality fuel, limited acceptance by consumers and concerns

regarding engine warranties and performance ((APAC) 2009). In order to enhance

sustainability and to position Australian biodiesel as an attractive and superior future

transport fuel, it is necessary to evaluate its suitability as a diesel engine fuel.

However, little is known about the optimal use of second-generation biodiesel in

modern engines. There is very little knowledge on modelling the combustion of

biodiesel in engines and at present, there are no comprehensive models for

optimising the benefits of second-generation biodiesel, in terms of fuel quality and

suitability for diesel engines.


1.2 RESEARCH QUESTIONS

Based on the background study of these topics, the key research questions were as

follows:

1. What are the potential sources of second-generation biodiesel feedstock?

2. What are the key parameters that influence biodiesel quality as a diesel

engine fuel?

3. What are the Australian native plants that are able to produce substantial

amounts of non-edible bio-oil?

4. Are the non-edible bio-oils produced from Australian native plants able to

meet biodiesel quality standards?

5. What are the optimum conditions for the production of biodiesel from

Australian native plants?

6. Which Australian native species are the most suitable candidates for future

generation biodiesel feedstocks?

7. Are biodiesels made from Australian native plants suitable for use in

conventional diesel engines?

1.3 AIMS AND OBJECTIVES

1.3.1 Aims

The aim of this study was explore the potential of Australian native plants as a source

of second-generation biodiesel. The aim was achieved by undertaking several

experimental and numerical measurements using these biodiesels.

1.3.2 Specific study objectives

In an effort to achieve the aim of this research, the below objectives were formulated

and satisfied, according to the following order:


1. Determine the correlation between chemical composition of biodiesel and

important fuel properties.

2. Develop a numerical model for the estimation of properties of the biodiesel.

3. Produce/obtain second-generation biodiesel from different Australian native

plants.

4. Apply the numerical model to investigate the fuel quality of second-generation

biodiesel produced from Australian native plants.

5. Production process optimisation of second-generation biodiesel from Australian

native plants.

6. Experimentally investigate the effect of biodiesel physico-chemical properties on

diesel engine performance and emission.

7. Experimentally investigate diesel engine performance and emissions using

second-generation biodiesel produced from Australian native plants.

1.4 SIGNIFICANTCE OF THIS PROJECT

Recognising the importance of developing sustainable energy sources for the future,

oil companies, governments and non-government organisations are beginning to

invest more research funds into the development of second-generation biodiesel fuels

((APAC) 2009). However, these types of biodiesel, particularly from Australian

native plants, have not been adequately evaluated as a diesel engine fuel and are yet

to start commercial production. The fuel quality of Australian second-generation

biodiesel and the suitability of its use in conventional diesel engines is still unknown.

In order to overcome these barriers and improve the feasibility of producing second-

generation biodiesel from native species, this project investigated the use of

Australian native plants as a second-generation biodiesel feedstock. The

comprehensive experimental study and numerical model was designed to yield a

sensible and simplified characterisation of biodiesel in terms of its physico-chemical

properties and its performance in modern diesel engines. Therefore, the results of this

study will facilitate a more rapid uptake of renewable energy systems, by

investigating new second-generation biodiesels for the public and industries. In this


work, the Australian native crops considered as potential feedstocks for biodiesel

production were non-edible, high oil yield, commercially unavailable and not yet

experimentally investigated. The outcomes of this project are expected to trigger

great interest around Australia in relation to the use of second-generation biodiesels.

Establishing the wide-spread use of second-generation biodiesel will reduce the

dependence on crude oil imports and therefore, increase the stability of Australia’s

fuel market and improve its balance of trade. The range of ecological benefits

include massive reductions in greenhouse gas emissions, as well as reductions in

sulphur dioxide emissions (which are one of the main causes of acid rain) and other

cancer causing emission such as benzene. There will also be potential benefits for

agricultural and rural development, including new jobs and income generation.

Therefore, this project is expected to generate new knowledge in relation to how

Australian native plants can significantly contribute to overcoming the global energy

and environmental crisis, and at the same time add value to the Australian economy

and its development.

1.5 THESIS OUTLINE

This section gives a brief outline of the remaining chapters of this thesis. There are a

total of nine chapters which systematically address the key topics of this thesis,

according to five stages. Figure 1.1 presents the relationship between each of the

chapters and stages presented in this thesis. Further discussions on how the outputs

of each chapter form a unified research program are provided below:

In stage one (Chapter 2), a comprehensive literature review of current biodiesel

technology and artificial neural network (ANN) modelling tools for selecting future

generation biodiesel was conducted. This chapter begins with an overview of

biodiesel feedstocks, production processes, chemical compositions, standards,

physico-chemical properties and performance as a diesel engine fuel. The limitations

of commercially available fist-generation biodiesel feedstocks over second-

generation biodiesel feedstock were discussed. Then, the application of ANN in

modelling key biodiesel quality parameters and combustion performance in


automobile engines was reviewed. This review found that all biodiesels are fatty acid

methyl esters (FAME), produced from raw vegetable oil and animal fat. The

chemical compositions of biodiesel, in terms of fatty acid profile, are different from

one feedstock to the next; even for the same biodiesel feedstock from a different

origin. Moreover, the fatty acid profile of biodiesel determines the important fuel

properties of biodiesel and hence, the quality of biodiesel as an internal combustion

engine fuel. Although, ANN modelling is most accurate approach for prediction

however, some literatures found that this technique better than many of the linear and

non-linear statistical techniques. Therefore, in the next stage of this study,

correlations between the chemical composition of biodiesel and fuel quality were

investigated. The findings of this study have been published in the journal of

Energies.

In the second stage of this study (Chapter 3), experimental and statistical

investigation of the important chemical composition and fuel properties of biodiesel

were conducted. Section 3.3 describes the data collection methods, while in Section

3.4.4, a correlation study between the properties of biodiesel and its chemical

composition are analysed using principal component analysis (PCA). The PCA

analysis indicated that individual biodiesel properties have complex correlation with

the parameters of chemical composition. The average member of double bonds

(ANBD) and poly-unsaturated fatty fraction (PUFA) are the most most-influential

parameters that affect all biodiesel properties. Figure 3-9 shows the relationship

between eight important biodiesel properties and chemical composition. Based on

these results, several artificial intelligence-based models were developed to predict

specific biodiesel properties based on its chemical composition. The developed

models were tested via simulation studies using an unknown data set (the results of

which are presented in Section 3.4.6), which demonstrated that ANN was able to

predict the relationship between biodiesel chemical composition and fuel properties.

Therefore, the ANN model developed in this study could be a useful tool in

estimating biodiesel fuel properties, instead of undertaking costly and time

consuming experimental tests. Such an attempt was made in the next stage of this

study, when estimating fuel properties of biodiesels obtained from several Australian

native oil seed plants.


In stage three (Chapter 4), eleven Australian native plants were investigated for the

potential production of second-generation biodiesel. The native plants investigated

were: Beauty leaf (Calophyllum inophyllum), Candle nut (Aleurites Moluccana),

Blue berry lily (Dianella Caerula), Queen palm (Syagrus Romanzoffiana), Castor

(Ricinus Communis), Bidwilli (Brachychiton Bidwilli), Karanja (Pongamia Pinnata),

Whitewood (Atalaya Hemiglauca), Cordyline (Cordyline Manners – Suttoniae),

Flame tree (Brachychiton Acerifolius), Chinese rain (Koelreuteria Formosana). This

chapter gives a brief description of the investigated native plants for biodiesel

production, followed by the oil extraction techniques from the dry seeds. During oil

extraction, oil yields were measured and the results were presented in Figure 4-16.

Section 4.5 presents the chemical composition of second-generation biodiesel

produced from the investigated feedstocks. Important fuel properties were estimated

using the ANN model developed in the previous chapter and the results are discussed

in Section 4.6. The native species feedstocks were then evaluated and their quality

was compared with each other using the multi-criteria decision method (MCDM)

software PORMETHEE-GAIA. In addition, sensitivity analysis of native plant

ranking was carried out by changing the weighting three important criteria, OY, OS

and CFPP. Results of this analysis indicated that Beauty leaf was one of the best

potential candidates for second-generation biodiesel production among the native

plants investigated. Therefore, in the next stage of this study, biodiesel was produced

form Beauty leaf oil seeds on a pilot scale, for in-depth experimental investigation.

In stage four of this study, the physico-chemical properties of biodiesel obtained

from Beauty leaf oil seeds were assessed experimentally (Chapter 5) and the

biodiesel production process was optimised (Chapter 6). Bio-oil was extracted from

dry Beauty leaf seeds through three different oil extraction methods. The oil

extraction methods and oil yield results are discusse+d in Section 5.3 and Section 5.4,

respectively. Overall, the highest average oil yield was found to be 51.5% for dry

kernels of Beauty leaf seeds. The important physical and chemical properties of the

extracted Beauty leaf oils were experimentally analysed and compared with

conventional edible vegetable oils (presented in the Section 5.6). In Section 5.7, a

brief description of the two-step transesterification method is provided for the


conversion from Beauty leaf oil into biodiesel. In the last part of this chapter, a

PROMETHEE-GAIA analysis was conducted, in order to assess the quality of

Beauty leaf biodiesel and compare its physical and chemical properties with that of

commercially available first-generation biodiesel. The results of the PROMETHEE-

GAIA analysis are presented in the Section 5.10. This investigation indicated that

Beauty leaf oil biodiesel may be a better internal combustion engine fuel compared to

most commercially available biodiesel. Findings of this analysis have been submitted

to the journal of Energy Conversion and Management. In Chapter 6, the three main

factors that drive biodiesel (fatty acid methyl ester (FAME)) conversion from

vegetable oil (triglycerides) have been studied using a response surface methodology

(RSM), based on a Box-Behnken experimental design. The factors considered in this

study were catalyst concentration, methanol to oil molar ratio and reaction

temperature. Linear and full quadratic regression models were developed to predict

FFA and FAME concentration, and to optimise the reaction conditions which are

shown in Equations 6-3 to 6-6. The significance of these factors and their interaction

was determined using analysis of variance (ANOVA). The good agreement between

model outputs and experimental results found in this chapter demonstrated that this

methodology may be useful for the optimisation of industrial biodiesel production

from Beauty leaf oil and possibly other industrial processes as well. This chapter has

been published to the journal Energies. Finally, the Beauty leaf biodiesel obtained in

this stage was used for testing the automobile diesel engine in the next stage of this

study.

In the last stage of this study (stage five), experimental investigations were

conducted on multi-cylinder automobile diesel engines using artificially prepared

biodiesel (methyl ester) with controlled chemical composition and second-generation

biodiesel produced from Beauty leaf oil. In Chapter 7, effects of the physico-

chemical properties of biodiesel on diesel engine performance and emissions were

experimentally investigated. Alongside neat diesel, four biodiesels with various

carbon chain lengths and degrees of unsaturation were used at three blending ratios

(B100, B50, B20) in a common rail engine. Experimental results of the chemical and

physical properties for the tested biodiesel samples are presented in Section 7.2. The

variation in diesel engine performance and emissions with changes in the physico-


chemical properties of biodiesel is presented in Section 7.3. The experimental results

indicated that particle emissions consistently decreased with reductions in fuel

oxygen content, regardless of the proportion of biodiesel in the blends; whereas it

increased with fuel viscosity, carbon chain length and unsaturation percentages of the

biodiesel. Overall, it is evident from the results presented in this chapter that the

chemical composition and physical properties of biodiesel is important for

determining diesel engine performance and emissions. Therefore, the results of this

chapter are deemed to provide a basis on which the diesel emissions from second-

generation biodiesel can be interpreted. This chapter has been published in the

journal Fuel. In Chapter 8, an investigation was conducted to experimentally

evaluate the suitability of Beauty leaf biodiesel as an automobile engine fuel. A four

cylinder common rail diesel engine was run with 5% and 10% blends of Beauty leaf

biodiesel with conventional diesel, following a standard engine testing equipment

and procedure. The important engine performance and emission indicators were

measured for both Beauty leaf biodiesel blends and petroleum diesel. Section 8.3

summarises the engine testing results and presents a discussion on individual engine

emission and performance indicators. This study found that Beauty leaf oil biodiesel

can be utilised in conventional automobile diesel engines without any engine

modification. Although variations in engine performance and emissions were

observed, due to differences in the physicochemical properties of commercial diesel

and biodiesel produced from Beauty leaf biodiesel, such variations are not unusual

when testing an engine dedicated for petroleum diesel only and are commonly found

during engine testing using only commercially available biodiesel. In Chapter 9, the

conclusions, limitations and recommendations for future work of this thesis are

summarised.


Chapter 1: IntroductionThesisAim:InvestigatethepotentialofAustraliannativeplantsasasourceof

second-generationbiodiesel

Chapter 2: Literature review

TheUseofArtificialNeuralNetworksforIdentifyingSustainableBiodieselFeedstock

Publication:Journalof Energies.Specialissue" AlternativeFuelsfortheInternalCombustionEngines(ICE)2013"Vol.6,pp.3764-3806.

Chapter 3: Artificial neural network (ANN) model development

Correlationbetweenchemicalcompositionandpropertiesofbiodiesel– aprincipalcomponentanalysis(PCA)andartificialneuralnetwork(ANN)

approach

Chapter 4: Second-generation biodiesel production from Australian native plants in laboratory scale

PreparedandtestthebiodieselfromAustraliannativeplants

PropertiesestimationusingANNmodels

PROMETHEE-GAIAanalysistorankthebiodiesels

Pilot scale beauty leaf biodiesel production

Chapter5: Physio-chemicalassessmentofbeautyleafbiodieselassecond-generationbiodiesel

feedstockPublication:SubmittedtoEnergy

andManagement

Chapter6: biodieselproductionfromnon-ediblebeautyleafoil:processoptimisationusingresponsesurface

methodology(RSM)Publication: Energies.Vol.7(8),pp.

5317-5331,2014.

Diesel engine testing with biodiesel

Chapter7: Particleemissionofbiodieselswithdifferentphysicochemicalproperties

Publication: JournalofFuel,Vol.134,pp.201-208,2014

Chapter 9: ConclusionConclusionarisefromthisthesisLimitationandRecommendationsforfuturework

Stage 1

Stage 2

Stage 3

Stage 4

Chapter8: Automobiledieselenginetestingwithbeautyleaf

biodiesel

Stage 5

Figure 1-1: Outline on the thesis

Chapter 1: Literature Review 13

Chapter 2: Literature Review

The Use of Artificial Neural Networks for Identifying

Sustainable Biodiesel Feedstock

Md I. Jahirul1,*, Richard J. Brown1, Wijitha Senadeera1, Ian O’Hara2 and Zoran

Ristovski1

1 Biofuel Engine Research Facility (BERF), Science and Engineering Faculty,

Queensland University of Technology (QUT), Brisbane, Australia

2 Centre for Tropical Crops and Biocommodities (CTCB), Queensland University

of Technology (QUT), Brisbane, Australia.

Publication: Journal of Energies. Special issue " Alternative Fuels for the Internal

Combustion Engines (ICE) 2013" Vol. 6, pp. 3764-3806.

Author Contribution

Contributor Statement of Contribution

Md I. Jahirul Analysed the literature and drafted the manuscript Signature

Richard J. Brown Supervised the project and revised the manuscript

Wijitha Senadeera Supervised the project and revised the manuscript

Ian O’Hara Supervised the project and revised the manuscript

Zoran Ristovski Supervised the project and revised the manuscript

Principal Supervisor Confirmation

I have sighted email or other correspondence from all co-authors confirming

their certifying authorship.

Name

Dr Wijitha Senadeera

Signature

Date

14

Abstract

Over the past few decades, biodiesel produced from oilseed crops and animal fat

is receiving much attention as a renewable and sustainable alternative for

automobile engine fuels, and particularly petroleum diesel. However, current

biodiesel production is heavily dependent on edible oil feedstocks which are

unlikely to be sustainable in the longer term due to the rising food prices and the

concerns about automobile engine durability. Therefore, there is an urgent need

for researchers to identify and develop sustainable biodiesel feedstocks which

overcome the disadvantages of current biodiesel feedstocks. On the other hand,

artificial neural network (ANN) modelling has been successfully used in recent

years to gain new knowledge in various disciplines. In this study, recent literature

has been reviewed to assess the state of the art on the use of ANN as a modelling

tool for future generation biodiesel feedstocks. Biodiesel feedstocks, production

processes, chemical compositions, standards, physico-chemical properties and in-

use performance are discussed. Limitations of current biodiesel feedstocks over

future generation biodiesel feedstock have been identified. The application of

ANN in modelling key biodiesel quality parameters and combustion performance

in automobile engines are also discussed. This review has determined that ANN

modelling has a high potential to contribute to the development of renewable

energy systems by accelerating biodiesel research.

Keywords: Renewable Energy, Biodiesel, Artificial Neural Networks (ANN),

Second-Generation Feedstock


1 INTRODUCTION

Since the beginning of the Industrial Revolution in the late 18th and early 19th

century, energy has become an indispensable tool for mankind, contributing to

economic growth and increased standards of living. World primary energy demand is

expected to grow by 1.6% per annum over the period 2010 to 2030, which will

require 39% additional energy by 2030 (BP 2012). There are many potential energy

sources around us from which energy can be converted for use and stored. These

sources can be classified as fossil, fissile and renewable. Fossil energy sources were

formed thousands of years ago, and are not renewable in a short time horizon. They

include liquid crude oil, coal, natural gas and tar sands. The major fissile energy

sources are uranium and thorium that are fissionable by neutrons with zero kinetic

energy. Renewable energy is generated from natural sources such as biomass, solar,

wind and geothermal resources.

Most of the primary energy used today comes from fossil-based resources,

predominantly crude oil (35%), coal (29%) and natural gas (24%), while nuclear and

renewable resources account for 7% and 5% of global energy consumption,

respectively (BP 2012). Fossil-based resources are therefore the single largest source

of energy, representing 88% of the total World consumption. However, fossil

resources are being consumed rapidly. Based on current production scenarios, it is

expected that a peak of global oil production will occur between 2015 and 2030

(Demirbas 2007). Therefore, fossil resources have practical limitations in their

capacity to supply future global energy requirements in which there are currently few

large scale alternatives available. Moreover, combustion of fossil fuels results in

greenhouse gas emissions and contributes to anthropogenic climate change. Despite

global measures such as the Kyoto Protocol and scientific innovation, atmospheric

CO2 concentration continues to increases and is exceeding benchmark levels much

earlier than had previously been predicted (Weitzman 2007).

16 Chapter 2: Literature Review

The transportation sector globally is the third largest energy consumer after the

industry and the building sectors which is the fastest growing sector. By 2030, the

energy consumption and CO2 emissions from this sector are predicted to be 80%

above the levels of today (Metz 2007). The transportation sector is also the sector

most reliant upon petroleum fuels (primarily through the crude oil-derived liquid

products gasoline and diesel). The sector currently consumes 30% of crude oil

globally and this is predicted to increase to 60% by 2030 (Luque et al. 2008).

Furthermore, the availability of conventional crude oil is geographically restricted

impacting on the security and cost of supply.

These issues have forced researchers to seek alternative carbon neutral transport fuels

which promote sustainable development, energy efficiency and environmental

preservation(Jahirul et al. 2010). As of yet, few large scale commercially viable

options exist for the transportation sector. Moreover, cars with no tailpipe

greenhouse gas emissions (e.g. electric, solar, hydrogen) are a long way from being

viable across the sector. Therefore, the development of sustainable long-term

solutions using alternative fuels is essential (Lapuerta et al. 2008; Jahirul et al. 2010)

As a consequence, biodiesel produced from oil crops and animal fats, is receiving

much attention as an alternative to conventional petroleum fuels. In particular, fuels

produced from biomass feedstocks have emerged as one of the more promising and

environmentally sustainable renewable energy options. Fuels produced from these

technologies are referred to as biofuels. Biofuels offer many benefits over

conventional petroleum fuels, including the wide regional distribution of biomass

feedstocks, high greenhouse gas reduction potential, biodegradability and their

contribution to sustainability (Reijnders 2006).

Biofuels produced by conventional technologies (ethanol and biodiesel) typically

contain oxygen levels of 10%–45% by mass, while petroleum fuels (gasoline and

diesel) contain very low oxygen levels. This makes the chemical properties of

biofuels more favourable for complete combustion, although it reduces energy

density. In addition, biofuels typically have very low sulphur contents


and often low nitrogen contents reducing the production of potentially harmful

emissions. Biomass resources can be used to produce a variety of biofuels. This

includes liquid fuels such as biodiesel ethanol, methanol, and Fisher-Tropsch diesel;

and gaseous fuels such as hydrogen, syngas and methane. Liquid biofuels are

primarily used in vehicles, but can also be used in stationery engines or fuel cells for

electricity generation. Biodiesel is widely used as an alternative fuel for diesel

engines, whereas ethanol is used as a substitute for automotive gasoline (Demirbas

2007).

Biodiesel is currently produced in commercial quantities from edible oil feedstocks

such as soybean oil, palm oil, and canola oil. Biodiesels produced from these

feedstocks are generally referred to as first-generation biodiesels. Although

biodiesels from these feedstocks offer reductions in greenhouse gas emissions

(GHG) and improves domestic energy security, first-generation biodiesels are

unlikely to be sustainable in the longer term due to land use impacts and the price

and social impacts associated with using food-based feedstocks. Second-generation

biodiesels produced from non-edible feedstocks have the potential to overcome the

disadvantages associated with first-generation feedstocks while addressing many of

the challenges of climate change and energy availability.

However, the majority of current vehicle engines are not optimised for the use of

biodiesel. When using biodiesel as a fuel, optimised engines may show increased

problems with carbon deposition, corrosion, lubricating oil contamination, poor low

temperature performance, and heavy gum and wax formation compared to petroleum

diesel(Jayed et al. 2009). The differences in performance between petroleum diesel

and biodiesel may be attributed to the variation between their physical properties and

their chemical compositions. Petroleum diesel is composed of hundreds of

compounds boiling at different temperatures (determined by the petroleum refining

process and crude oil raw material), whereas biodiesel contains primarily 6–24

carbon chain length alkyl esters (determined entirely by the feedstock) (Graboski and

McCormick 1998). In addition to these alkyl esters, biodiesel may also contain minor

amounts of mono-, di- and triglycerides resulting from incomplete trans-


esterification, methanol, free fatty acids, chlorophyll (in the case of algae) and sterols

(Knothe 2008).

As engines are currently manufactured to be optimised for petroleum fuels, Original

Equipment Manufacturers (OEMs) and industry associations have shown a cautious

response in their acceptance of biodiesel, especially from new feedstocks or

processes (Haseeb et al. 2011b). Engine manufacturers and users may be reluctant to

realise the potential of using second biodiesel in engines (especially from new

feedstocks) because its suitability as an automobile engine fuel may not be readily

verified. As a consequence, the potential of second-generation biodiesel is still

largely unexplored and is yet develop as a mainstream transportation fuel. This is in

spite of the fact that there are many such potential biodiesel feedstocks that have

already been identified including oilseed plants (Ahmad, Yasin, et al. 2011;

Banković-Ilić, Stamenković and Veljković 2012; Ashwath 2010b) and marine algae

(Mata, Martins and Caetano 2010; Chisti 2007). The slow uptake of biofuels can be

associated with the challenges of ensuring a consistent supply of feedstock, feedstock

cost, and the lack of experimental data to prove the quality of the fuel (resulting in

limited acceptance by consumers who are concerned about engine warranties and

performance) ((APAC) 2009). Undertaking experiments with automotive engines

and measuring multiple fuel quality parameters requires considerable quantities of

fuel, which can be a challenge from new sources. Fuel testing requires sophisticated

equipment and expert personnel which can be costly. These concerns potentially

restrict the progress of scientific research to establish widely acceptable second-

generation biodiesels.

In recent years, ANN modelling techniques have gained in popularity due to their

ability to accurately predict from small data sets (examples) rather than from larger

data sets requiring costly and time-consuming studies and experiments. ANN has

been successfully applied in various disciplines, including neuroscience (Alkım,

Gürbüz and Kılıç 2012), mathematical and computational analysis (Costa, Braga and

De Menezes 2012), learning systems (Carrillo et al. 2012), engineering design and

application (Samura and Hayashi 2012; Gao et al. 2012; Minnett et al. 2011b) and


chemical and environmental engineering (Zendehboudi et al. 2012; Roosta, Setoodeh

and Jahanmiri 2011; Kumar 2009b; Ali Ahmadi et al. 2013).

In this paper, the potential of ANN modelling techniques in identifying sustainable

future generation biodiesel feedstock are identified based on the most recent

literature. Current biodiesel technology (feedstocks, properties, production processes,

chemical compositions and factors contributing to fuel quality and its applicability as

an alternative fuel) and the application of ANN in fuel technology and the potential

second-generation biodiesel feedstock is also discussed. Findings from this literature

review contain valuable information to assist biodiesel manufacturers and researchers

to make important decisions to accelerate the technological development of biofuels.

2.1 BIODIESEL

Fatty acid methyl or ethyl esters, commonly referred to as “biodiesel”, are a liquid

fuel alternative to diesel. They are made from agricultural products, forest organic

matter and animal fat feedstocks. Biodiesel is the only currently available alternative

transport fuel made from oilseed crops and animal fat which can be used directly in

conventional unmodified diesel engines. Biodiesel is safer to handle, store and

transport compared to petroleum diesel because it is biodegradable, non-toxic and

has a higher flash point than diesel. One of the major advantages of biodiesel is that

it has potential to reduce dependency on imported petroleum through the use of

domestic feedstocks for production (Demirbas 2008b; Fernando et al. 2007).

In fuel property terms, biodiesel has a higher cetane rating than petroleum diesel

which improves engine performance. In addition, it has better lubricant properties

than petroleum diesel which can extend engine life (Haseeb et al. 2011b). The use of

biodiesel reduces particulate emissions by up to 75% when compared with

conventional diesel fuel. Biodiesel also substantially reduces unburned

hydrocarbons, carbon monoxides and particulate matters, including elimination of

sulphur dioxide in exhaust emissions. The exhaust emissions of particulate matter


from biodiesel have been found to be 30% lower than overall particulate matter

emissions from fossil diesel. The exhaust emissions of total hydrocarbons are up to

93% lower for biodiesel than diesel fuel (Hoekman et al. 2012).

As a fuel, there are currently several disadvantages to using biodiesel in diesel engine

applications. These differences mainly result from the difference in chemical

compositions between petroleum diesel and biodiesel. These major disadvantages

are: lower energy density, higher viscosity, higher copper strip corrosion and issues

with the degradation of fuel in storage for prolonged periods. Biodiesel also has a

higher cold-filter plugging point temperature than fossil diesel which means it will

crystallise into a gel at lower temperatures when used in its pure form. Biodiesel can

also cause dilution of engine lubricant oil, requiring more frequent oil changes than

when using petroleum diesel fuels in conventional diesel engines. This increase in

dilution and polymerisation of engine sump oil is due to the higher viscosity at lower

temperatures of biodiesel compared to petroleum diesel (Jayed et al. 2009).

2.1.1 Biodiesel feedstock

Feedstocks for biodiesel production can be classified into four groups. These are: (1)

virgin vegetable oil feedstocks such as rapeseed, soybean, sunflower, palm oil; (2)

waste vegetable oils; (3) animal fats including beef tallow, lard and yellow grease;

and (4) non-edible oils such as jatropha, neem oil, castor oil and the prevalence of

these feedstocks varies around the World (Figure 1). The regional availability of

feedstocks for biodiesel production depends greatly on climate, soil conditions and

options for alternate land use. Consequently, different regions are focusing their

efforts on different feedstocks (Lin et al. 2011). As an example, the widespread use

of soybeans in the USA as a food product has led to the emergence of soybean

biodiesel in that country. In Europe, rapeseed is the most common source of

biodiesel production. In India and Southeast Asia, the jatropha tree is used in

biodiesel production, and in Malaysia and Indonesia, palm oil is used as a significant

biodiesel source.


Figure 2-1. Biodiesel feedstocks around the world

2.1.2 First and second-generation biodiesel

Many potential feedstocks for biodiesel production have been investigated including

soybean oil (Goodrum and Geller 2005), sunflower oil, corn oil, used fried oil, olive

oil (Anastopoulos et al. 2005), rapeseed oil (Terry, McCormick and Natarajan 2006),

lesquerella oil, milkweed (Asclepias) seed oil (Holser and Harry-O’Kuru 2006a),

Jatropha curcas, Pongamia glabra (karanja), Madhuca indica (mahua) and

Salvadora oleoides (Pilu) (Kaul et al. 2007b), palm oil (Raadnui and Meenak 2003)

and linseed oil (Agarwal 1999). Most of those are produced from edible oil feedstock

and known as first-generation biodiesels (Rashid and Anwar 2008b). Although the

range of biodiesels available reveals the flexibility and potential of the biodiesel

industry, this potential is challenged by social and economic concerns. The most

contentious issue affecting the production of first-generation biodiesel is the use of

agricultural land for biodiesel production. This issue is commonly referred to as the

“Food verses Fuel” debate, whereby the main two issues are the use of edible crops

for biodiesel production, and the land space devoted to the growing of inedible crops.

First-generation biodiesels are primarily made from edible vegetable oils, therefore


crop space used for biodiesel production limits the crop space available for food

production. Farmers of these crops now have the choice to sell to the biodiesel

production market or the food market. Farmers must find ways to retain their

viability, and if a higher price is offered by the biodiesel production market, this will

more often than not be the option they will choose. This is of particular concern in

disadvantaged countries where crops used for biodiesel production displace the

production of food crops, causing shortages. Supply and demand dictates that a

shortage will cause a price rise, where countries such as Malaysia are currently

experiencing (Bradsher 2008). This issue became a global debate due to the 2007–

2008 world food price crises. Differing arguments exist for the cause of this crisis;

however, there has been widespread speculation that the increasing consumption of

biodiesel contributed to the food shortage and subsequent price increases (Kingsbury

2012). Although there is a global demand for biodiesel due to its proven benefits and

its potential to decrease dependence on fossil fuels, this should not lead to of people

suffering from hunger. As a result, first-generation biodiesels are unlikely to be

sustainable in the longer term, having limitations in their ability to contribute to

socio-economic growth (Bioenergy 2008). Therefore, an alternative must be

considered which eliminates the downfalls of first-generation biodiesels. Research is

currently underway in second and third-generation biodiesels which are targeted at

addressing the “Food verses Fuel” debate (Posten and Schaub 2009). A comparison

of first and second-generation biodiesel in terms of feedstock, advantages and

associated problems are shown in Table 2-1.

Table 2-1. Advances in biodiesel technology

Technology First-generation biodiesel Second-generation biodiesel

Feedstock Edible vegetable oil and animal fat Non-edible feedstock Cheap and abundant biomass

Advantage Commercially available Renewable Environmentally friendly Economic

Renewable Not competing with food Environmentally friendly More sustainable Social security

Problems Limited feedstock Competing with food

In development stage Unreliable sources of feedstock


Engine durability Not likely to be sustainable

High production cost

2.1.3 Potential second-generation biodiesel feedstock

Non-edible oils, which are considered as second-generation biodiesel feedstock,

currently contribute less than 5% of total global biodiesel production (Banković-Ilić,

Stamenković and Veljković 2012). Therefore, second-generation biodiesels are yet to

make a significant impact on the mainstream alternative energy system. One of the

main reasons for this is the lack of reliable and commercially viable feedstock

sources. The feedstocks most often used in second-generation biodiesel production

are jatropha, cottonseed, mahua, and waste cooking oils. A considerable amount of

research has been carried out in an effort to determine alternative feedstocks for

biodiesel production over the last few years. A large number of non-edible oilseed

plants and algae species have been identified around the world which could be a

valuable source for future generation biodiesel. For example, Ashwath (Ashwath

2010b) investigated the biodiesel potential of more than 200 Australian native plants

species, based on their ability to produce abundant quantities of seeds in their natural

environment in Queensland. Among those, 20 species have been identified as

containing more than 20% of non-edible oil in their seed. This study has concluded

that Australia has potential to be a major source of next-generation biodiesel

feedstock, having vast areas of grazing (cleared) and degraded (mined) land on

which biodiesel crops could successfully be established.

In Thailand, 27 types of plants have been found to contain more than 25% (w/w) of

non-edible oil in the seed (Winayanuwattikun et al. 2008). Mohibbe Azam et al.

(Mohibbe Azam, Waris and Nahar 2005a) identified 75 different non-edible plant

seed oils produced from plant species growing naturally in India, and containing

more than 30% of oil in their seeds or kernels. Kumar and Sharma (Kumar and

Sharma 2011) presented a brief description of the biology, distribution and chemistry

of fifteen potential non-edible oilseed plants from India. Moreover, in a critical

review, Balat and Balat (Balat and Balat 2008) reported that there are more than 350

potential oilseed crops for biodiesel production which have been identified, with


most of them being non-edible, yet useful as alternative fuel sources for diesel

engines.

Besides non-edible oilseed plants, researchers worldwide are giving more attention to

algae species as a promising source of second-generation biodiesel feedstocks. Algae

are usually found in seas, rivers, lakes, soils, walls, in plants and animals—almost

anywhere that there is light for photosynthesis. While growing, they convert sun

energy into chemical energy through photosynthesis and complete an entire growth

cycle every few days (Khan et al. 2009). Their growth and bio-oil production rates

can be accelerated by the addition of specific nutrients and sufficient aeration (Khan

et al. 2009). Therefore, it is possible to find species best suited to local environments

or which have specific growth characteristics, which is not possible with current

first-generation biodiesel feedstocks (e.g., soybean, rapeseed, sunflower and palm

oil) (Mata, Martins and Caetano 2010). Moreover, the oil productivity of many algae

greatly exceeds the oil productivity of even the best producing oil crops (Karmakar,

Karmakar and Mukherjee 2010). For example, some algae species are able to

produce bio-oil at a rate approximately 200 times higher than that of soybean plants

over an acre of land area (Hossain et al. 2008).

Algae also offers some significant advantages in the production of second-generation

biodiesel including: (1) providing a reliable and continuous supply for naturally

growing oil all year round; (2) can be harvested batch-wise nearly all year round; (3)

higher photon conversion efficiency (as evidenced by increased biomass yields per

hectare); (4) utilising salt and waste water streams, thereby greatly reducing

freshwater use; (5) possibility of combining CO2-neutral fuel production with CO2

sequestration; and (6) producing non-toxic and highly biodegradable biofuels

(Schenk et al. 2008). Furthermore, algae species are very diverse in nature with

diversity much greater than that of land plants. Hu et al. (Hu et al. 2008) reported

that over 40,000 species have already been identified, with potentially many more.

These species are commonly classified into multiple major groups which include:

cyanobacteria (Cyanophyceae), green algae (Chlorophyceae), diatoms

(Bacillariophyceae), yellow-green algae (Xanthophyceae), golden algae

(Chrysophyceae), red algae (Rhodophyceae), brown algae (Phaeophyceae),


dinoflagellates (Dinophyceae) and “pico-plankton” (Prasinophyceae and

Eustigmatophyceae). Several additional divisions and classes of unicellular algae

have been described, and details of their structure and biology are available

(Demirbas and Fatih Demirbas 2011; Scott et al. 2010).

The concept of using diverse non-edible oils produced from oilseed plants and algae

species has been explored in depth over the past few decades. At the same time, a

large number of new feedstocks have been identified, based on oil content. Table 2-2

summarises the potential oilseed plants and algae species feedstock for future

generation biodiesel production, focusing on feedstocks containing at least 40% non-

edible oil by dry weight as reported in some recent studies. The chemical

compositions of most of the oil produced from the feedstock listed in the Table 2-2

have also been analysed (Gouveia and Oliveira 2009). However, the production of

those oils is primarily confined to high-value specialty oils with nutritional value,

rather than commodity oils for biofuel and scalable, commercially viable systems

have yet to emerge. In considering any new oil as a candidate for large-scale

production in order to use as a biodiesel fuel source, several other issues should be

considered which include the economic necessities revolving around the production

of biodiesel and its suitability for use as fuel in combustion engines.

However, major technical and economic hurdles are still to be overcome before they

can be widely deployed on a fully commercial scale, and unfortunately, the progress

of research to address these issues is not progressing strongly. The quality and

suitability of biodiesel produced from a large number of non-edible oil-bearing

feedstocks as the alternative to petroleum fuel for automobile engine application is

still uncertain. Therefore, many potential non-edible feedstocks are yet emerging to

mainstream biodiesel production, and it appears that its contribution to the global

energy system is a long way off (Sims et al. 2010). In order to accelerate the progress

of second-generation biodiesel as an alternative automobile fuel, more detailed

research is still needed to ensure that second-generation biofuels will provide

economic benefits whilst fulfilling quality standards and combustion performance in

automobile engines. Such research requires pilot scale biodiesel production plants,

specialised equipment and a skilled workforce - all of which involve high-level


research investment and considerable economic risk. Therefore, it has become

challenging for many countries (especially developing or underdeveloped countries)

to take the initiative to establish their own biodiesel manufacture from locally grown

non-edible feedstock.

Table 2-2: Second-generation biodiesel feedstock containing oil by dry weight

(Banković-Ilić, Stamenković and Veljković 2012; Ahmad, Yasin, et al. 2011;

Ashwath 2010b; Mata, Martins and Caetano 2010; Gouveia and Oliveira 2009; No

2011; Amaro, Guedes and Malcata 2011) Non-edible oilseed plants Microalgae

Feedstock Oil content (% dry wet)

Feedstock Oil content (% dry wet)

Aleurites moluccana 42 Botryococcus braunii 25–75 Argemone mexicana 38–54 Chaetoceros calcitrans 40 Atalaya hemiglauca 18–43 Chlorella emersonii 25–63 Azadirachta indica 45–61 Chlorella minutissima 57 Brachychiton acerifolius 45–50 Chlorella protothecoides 14.6–57.8 Cerbera odollam 54 Chlorella vulgaris 5–58 Cocos nucifera 49–52 Crypthecodinium cohnii 20.0–51.1 Jatropha curcas 20–60 Cylindrotheca sp. 16–40 Euphorbia lathyris 48 Dunaliella tertiolecta 16.7–71.0 Garcinia indica 45.5 Isochrysis galbana 7–40 Hevea brasiliensis 40–60 Nannochloris sp. 20–56 Linum usitatissimum 35–45 Nannochloropsis sp. 31–68 Madhuca indica 35–50 Neochloris oleoabundans 29–65 Melia azedarach 19–45 Nitzschia sp. 16–47 Michelia chaampaca 45 Phaeodactylum tricornutum 18–57 Nicotiana tabacum 36–41 Porphyridium cruentum 9.0–60.7 Pongamia pinnata 21–50 Scenedesmus dimorphus 16–40 Putranjiva roxburghii 42 Scenedesmus obliquus 11–55 Raphanus sativus 40–54 Schizochytrium sp. 50–77 Ricinus communis 20–50 Skeletonema costatum 13.5–51.3 Salvadora oleoides 45 Simmondsia chinensis 45–55 Syagrus romanzoffiana 38–44

2.1.4 Production of biodiesel

More than 100 years ago, Rudolph Diesel demonstrated the operation of a diesel

engine using vegetable oil as a fuel, hence the potential of using these feedstocks has

been long recognised. However, vegetable oils are extremely viscous, with viscosity

ranging from 10 to 17 times higher than that of petroleum diesel. This makes


vegetable oil unsuitable to use as a direct fuel in the modern diesel engine. As a

consequence, researchers and scientists have developed various methods to reduce

the viscosity of bio-oils to make them suitable for diesel engine use. Some of these

methods include dilution with other fuels, esterification, micro-emulsification,

pyrolysis and catalytic cracking. Of these techniques, esterification is the most

promising and widely used solution due to its high conversion efficiency, simplicity,

low conversion cost and the fuel qualities of the product. Transesterification of bio-

oils with alcohols to produce esters is a widely used technique for commercial

biodiesel production (Lin et al. 2011).

Transesterification is a chemical reaction in which oils (triglycerides) are converted

into esters as shown in Figure 2-2. Triglycerides react with alcohols (e.g., methanol,

ethanol) under acid or base catalysed conditions, producing fatty acid alkyl esters and

glycerol. A catalyst is used to improve the reaction rate and yield. Because the

transesterification reaction is reversible, excess alcohol is used to shift the

equilibrium to favour production of the ester. After the reaction is complete, glycerol

is removed as a by-product. The biodiesel produced may be denominated by the

feedstock used and the ester formed including Fatty Acid Methyl Ester (FAME),

Fatty Acid Ethyl Ester (FAEE), Soybean Methyl Ester (SME) and Rapeseed Methyl

Ester (RME). The total ester content in biodiesel is the measure of the completeness

of the transesterification reaction (Rajendra, Jena and Raheman 2009).

The yield of biodiesel in transesterification is affected by several process parameters.

These parameters include the reaction temperature, the molar ratio of alcohol to oil,

the type and concentration of catalyst and the reaction time (Balat and Balat 2010;

Demirbas 2008a). While conducting experiments to optimise the transesterification

reaction conditions of two feedstock oils (including waste cooking oil and canola

oil), Leung and Guo (Leung and Guo 2006b; Zhang and Jiang 2008a) determined

that the optimal values of these parameters for achieving maximum conversion of

triglycerides to esters depended on the chemical and physical properties of the

feedstock oils. Other research has also determined varying optimum process

conditions for different oil feedstocks as shown in Table 2-3.


Figure 2-2. Transeterification reaction

Table 2-3: Reported optimum conditions for transesterification of oils for biodiesel production.

Feedstock

Reaction parameters Ester

(wt.%) References Temp

(°C)

Alcohol: Oil

(mol:mol)

Catalyst

(wt.%)

Time

(min)

Palm

38.44 6.44:1 1.25 26 98.02

Worapun et al. (Worapun,

Pianthong and Thaiyasuit

2012)

65 6:1 1.0 60 82

Darnoko and Cheryan

(Darnoko and Cheryan

2000)

Cottonseed

60 6:1 0.3 60 96 Hoda (Hoda 2010)

60 12:1 2 480 90 He et al. (Chen et al.

2007)

Rapeseed 65 6:1 1.0 120 96

Rashid and Anwar

(Rashid and Anwar

2008b)

Sunflower

60 6:1 1.0 120 97.1 Rashid et al. (Rashid et al.

2008)

70 3:1 0.28 60 96 Antolin et al. (Antolın et

al. 2002)

Jatropha 60 6:1 1.0 40 98.6

Nakpong and

Wootthikanokkhan

(Nakpong and

Wootthikanokkhan 2013)

Canola 45 6:1 1.0 60 98 Leung and Guo (Leung

and Guo 2006b)

Waste cooking 60 7:1 1.1 20 94.6 Leung and Guo (Leung

and Guo 2006b)

Soybean 70 9:1 0.5 180 99 De et al. (de Oliveira et al.

2005)


Figure 2-3: Soap formation (Rajendra, Jena and Raheman 2009).

Figure 2-4: Acid pre-esterification (Rajendra, Jena and Raheman 2009)

Alkali-catalysed transesterification cannot be directly used to produce high quality

biodiesel from feedstocks containing high levels of free fatty acids (FFA). This is

because FFAs react with the catalyst to form soap (Figure 2-3), resulting in

emulsification and separation problems. To overcome this problem, a pre-

esterification process may be used to reduce the content of FFAs in the feedstock.

A typical pre-esterification processes uses homogeneous acid catalysts, such as

sulphuric acid, phosphorous acid combined with sulphonic acid, or heterogeneous

“solid-acid” catalysts, to esterify the free fatty acids (Zhang and Jiang 2008a) as

shown in Figure 2-4.

2.1.5 Chemical composition of biodiesel

Petroleum diesel fuels are saturated straight chain hydrocarbons with carbon chain

lengths of 12–18, whereas vegetable oils and animal fats consist of 90%–98%

triglycerides, small amounts of mono-glycerides and free fatty acids. The fatty acid

compositions of triglycerides differ in relation to the chain length, degree of


unsaturation and the presence of other functional groups. The fatty acid compositions

are feedstock dependent and are affected by factors such as climatic conditions, soil

type, plant health, and plant maturity upon harvest. Using the carboxyl reference

system, fatty acids are designated by two numbers: the first number denotes the total

number of carbon atoms in the fatty acid and the second is the number of double

bonds indicating the degree of unsaturation. For example, 18:1 designates oleic acid

which has 18 carbon atoms and one C=C double bond. The most common fatty acids

found in biodiesels and their structures are listed in Table 2-4. However, biodiesels

from differing feedstock and origins have variations in the fatty acid in their

molecules, as shown in Figure 2-5.

Table 2-4: Chemical structure of common fatty acid in biodiesels

(Hoekman et al. 2012; Kapdan and Kargi 2006; Singh and Singh 2010; Ramos et al. 2009) Fatty acid Chemical structure

Caprilic (8:0) CH3(CH2)6COOH Capric (10:0) CH3(CH2)8COOH Lauric (12:0) CH3(CH2)10COOH Myristic (14:0) CH3(CH2)12COOH Palmitic (16:0) CH3(CH2)14COOH Palmitoilic (16:1) CH3(CH2)6 CH=CH (CH2)6 COOH Stearic (18:0) CH3(CH2)16COOH Oleic (18:1) CH3(CH2)7 CH=CH (CH2)7 COOH Linoleic (18:2) CH3(CH2)4 CH=CHCH2CH=CH (CH2)7 COOH Linolenic (18:3) CH3(CH2)2CH=CHCH2CH=CHCH2CH=CH(CH2)7 COOH Arachidic (20:0) CH3(CH2)18COOH Behenic (22:0) CH3(CH2)20COOH Erucic (22:1) CH3(CH2)9 CH=CH (CH2)9 COOH


Figure 2-5: Fatty acid profile of various biodiesel fuels

(Hoekman et al. 2012; Kapdan and Kargi 2006; Singh and Singh 2010; Taravus,

Temur and Yartasi 2009; Chuck et al. 2009; Kinoshita et al. 2007; Koçak, Ileri and

Utlu 2007)

2.1.6 Biodiesel standards

Quality standards are crucial for the commercial use of any fuel product. They serve

as guidelines for production, assure customers that they are buying high-quality

fuels, and to provide authorities with approved tools for a common approach to

transport, storage and handling. Modern diesel engines using common rail fuel

injection systems are more sensitive to fuel quality. Therefore, engine and

automotive manufacturers rely on fuel standards in determining consumer

warranties. However, the chemical compositions of biodiesel and petroleum diesel

are very different, and these differences result in varying physico-chemical

properties. In order to improve the viability of biodiesel for as a commercial fuel for

direct replacement of petroleum diesel, the properties of biodiesel need to reflect

functional equivalence with diesel.

Biodiesel can be used as a pure fuel (B100) or blended with petroleum diesel in

varying concentrations. For B100, the most internationally recognised standards are

EN14214 (Europe) and ASTM D-6751 (USA). Both standards are similar in content,


with only minor differences in some parameters (Hoekman et al. 2012). Many other

countries have defined their own standards, which are frequently derived from either

EN14214 or ASTM D-6751 (Hoekman et al. 2012). As a part of the Fuel Quality

Standards Act 2000, the Australian government released a biodiesel fuel standard,

“Fuel Standard (Biodiesel) Determination 2003”. A summary of the major fuel

quality parameters in these standards is detailed in Table 2-5.

Table 2-5: International biodiesel standards

(Singh and Singh 2010; Canakci and Sanli 2008)

Properties Units USA ASTM D-

6751 Europe EN

14214 Australia

Viscosity, 40 °C mm2/sec 1.9–6.0 3.5–5.0 3.5–5.0 Density gm/m3 n/a 0.860–0.900 0.860–0.900

Cetane number - 47 min 51 min 51 min Flash point °C 130 min 120 min 120 min Cloud point °C report report report

Acid number mg KOH/g 0.80 max 0.5 max 0.8 max Free glycerine wt.% 0.02 max 0.02 max 0.02 max Total glycerine wt.% 0.24 max 0.25 max 0.25 max Iodine number - - 120 max n/a

Oxidation stability h - 6 min n/a Monoglyceride Mass (%) - 0.8 max n/a

Diglyceride Mass (%) - 0.2max n/a Triglyceride Mass (%) - 0.2 max n/a

CFPP °C - - −4

2.1.7 Fuel properties

Biodiesel fuel properties vary significantly between feedstocks due to their differing

chemical compositions. Figure 2-6 summarises the key fuel properties of various

biodiesels reported in the more recent literature. The factors that influence biodiesel

fuel properties are discussed below.


Figure 2-6: Variation in fuel properties of various biodiesel

(Ramos et al. 2009; Taravus, Temur and Yartasi 2009; Koçak, Ileri and Utlu 2007;

Benjumea, Agudelo and Agudelo 2010; Sanford et al. 2009; Canakci and Sanli 2008;

Canakci 2005b; Barnwal and Sharma 2005; Alptekin and Canakci 2008; Kinast 2003a)

2.1.7.1 Kinematic viscosity

Viscosity is defined as the resistance to shear or flow; it is highly dependent on

temperature and it describes the behaviour of a liquid in motion near a solid

boundary such as the walls of a pipe. The presence of strong or weak interactions at

the molecular level can greatly affect the way the molecules of an oil or fat interact,

therefore affecting their resistance to flow. Viscosity is one of the most critical

features of a fuel. It plays a dominant role in fuel spray, fuel-air mixture formation

and the combustion process. In a diesel engine, the liquid fuel is sprayed into

compressed air and atomised into small droplets near the nozzle exit. In the

combustion chamber, a fuel form a cone-shaped spray at the nozzle exit which

affects the viscosity affects the atomisation quality, penetration and size of the fuel

droplet (Alptekin and Canakci 2008). Higher viscosities result in higher drag in the

fuel line and injection pump, higher engine deposits, higher fuel pump duties and

increased wear in the fuel pump elements and injectors. Moreover, the mean

diameter of the fuel droplets from the injector and their penetration increases with an


increase in fuel viscosity (Choi and Reitz 1999). Higher pressure in the fuel line can

cause early injection, moving the combustion of the fuel closer to top dead centre,

increasing the maximum pressure and temperature in the combustion chamber (Choi

and Reitz 1999; Lee et al. 2002; Tat and Van Gerpen 2003a). Therefore, fuel

viscosity significantly influences engine combustion, performance and emissions,

especially carbon monoxide (CO) and unburnt hydrocarbon (UHC) (Knothe and

Steidley 2005).

To estimate the influence of biodiesel viscosity on diesel engine exhaust emission,

Ng et al. (Ng, Ng and Gan 2012) conducted experiments on a light-duty diesel

engine using coconut methyl ester (CME), palm methyl ester (PME), soybean methyl

ester (SME) and blends with petroleum diesel. This study found that an increase in

kinematic viscosity by 1 mm2/s has the potential to raise the emitted CO

concentration by 0.02 vol%. UHC was increased by 1 ppm vol. for every 1 mm2/s

rise in kinematic viscosity. Problems with high viscosity in the fuel became more

severe in cold weather as viscosity of biodiesel increases with decreasing

temperature (Joshi and Pegg 2007a). However, very low fuel viscosity is not

desirable because the fuel then doesn’t provide sufficient lubrication for the precision

fit of fuel injection pumps, resulting in leakage or increased wear. Therefore, all

biodiesel standards define the upper and lower limits of biodiesel shown in Table 2-

4.

The viscosity of biodiesel is dependent on its fatty acid composition. A recent study

showed that viscosity increases with increasing length of both the fatty acid chain

and alcohol group (Geller and Goodrum 2004). As the lengths of the acid and alcohol

segments in the ester molecules increased, so did the degree of random

intermolecular interactions and consequently viscosity. The effect becomes more

evident at lower temperatures, where molecular movements are more restricted

(Knothe and Steidley 2007; Rodrigues Jr et al. 2006). However, Refaat (Refaat

2009b) reported that shorter fatty acid chains with longer alcohol moieties display

lower viscosity than ester with longer fatty acid chains and shorter alcohol moieties.

Other factors that influence biodiesel viscosity include: number and position of

double bonds (Cheenkachorn 2004), degree of saturation (Knothe 2005), molecular


weight (Gunstone 2011), branching (Lee, Johnson and Hammond 1995) hydroxyl

groups and the amount of impurities, such as unreacted glycerides or glycerol

(Knothe 2005; Gunstone 2011).

2.1.7.2 Density

Density is an important fuel property that influences the amount of fuel injected into

the engine cylinder. This is because in a diesel engine fuel injection system, pumps

and injectors must deliver a precise amount of fuel to provide proper combustion

(Boudy and Seers 2009; Baroutian 2008). However, fuel injection pumps meter fuel

by volume and not by mass, leading to denser fuel which contains a greater mass for

the same volume. Thus, changes in the fuel density will influence engine output

power due to the different mass of the fuel injected, and this directly affects engine

performance characteristics (Alptekin and Canakci 2008). Moreover, density

increases the diameter of the fuel droplets in the combustion chamber. Since the

inertia of the bigger droplets is relatively large, their penetration in the combustion

chamber will be higher as well (Choi and Reitz 1999). When a fuel with lower

density and viscosity is injected, improved atomisation and mixture formation can be

attained which consequently affects exhaust emissions. Szybist et al. (Szybist et al.

2007) found that fuel density correlated with particulate matter (PM) and NOx

emissions, with higher densities generally causing an increase in PM and NOx

emission in diesel engines. However, while investigating the biodiesel fuel properties

on exhaust emission in a light-duty diesel engine, Ng et al. (Ng, Ng and Gan 2012)

found that fuel properties moderately affect CO emissions but have no significant

impact on NOx, UHC and smoke opacity levels. Density is also a key factor in the

design of reactors, separation processes and storage tanks in biodiesel production

(Veny et al. 2009).

Density of biodiesel is closely related to the fatty acid composition and the purity of

a biodiesel. Studies have shown that density increases with decreasing chain length

and an increasing degree of unsaturation (Blangino, Riveros and Romano 2008; Lang

et al. 2001).


2.1.7.3 Cetane number (CN)

Cetane number (CN) is a widely used diesel fuel quality parameter, and is a

measurement of the combustion quality of diesel fuels during compression ignition.

It is related to the ignition delay (ID) time, that is, the time that passes between

injection of the fuel into the cylinder and the onset of ignition. A shorter ID time

results in a higher CN which provides better ignition properties (Meher, Vidya Sagar

and Naik 2006b). A high CN will help to ensure good cold start properties and will

minimise the formation of white smoke. On the other hand, lower CN may result in

diesel knocking and an increase in exhaust emissions.

Standards have been established worldwide for CN determination. It is measured by

comparing blends of two reference fuels. A long straight-chain hydrocarbon,

hexadecane (C16H34; trivial name “cetane”, giving the cetane scale its name) is the

high quality standard on the cetane scale with an assigned CN of 100. A highly

branched compound, 2, 2, 4, 4, 6, 8, 8,-heptamethylnonane (HMN, also C16H34), a

compound with poor ignition quality, is the low-quality standard and has an assigned

CN of 15. Both the Australian biodiesel standard and the European petroleum diesel

standard EN 590 limit the cetane number to a minimum value of 51, as shown in

Table 2-4.

CN is dependent on the fatty acid composition of a biodiesel. The longer the fatty

acid carbon chains and the more saturated the molecules, the higher the cetane

number (Bajpai and Tyagi 2006; Demirbas 2005). The most significant factor in

lowering CN is the degree of unsaturation. Geller and Goodrum (Geller and

Goodrum 2004) observed that a low CN was associated with highly unsaturated

compounds such esters of linoleic (C18:2) and linolenic (C18:3) acids, whereas high

CNs were observed for esters of saturated fatty acids such as palmitic (C16:0) and

stearic (C18:0) acids. Similar results have been reported by Knothe et al. (Knothe,

Matheaus and Ryan III 2003). Bangboye and Hansen (Bamgboye and Hansen 2008)

observed that a feedstock that is high in saturated fatty esters has a high CN, while a

feedstock with predominantly unsaturated fatty acids has lower CN values.


2.1.7.4 Heating (calorific) Value

Heating value is a significant fuel property which influences the suitability of

biodiesel as an engine fuel, as it indicates the energy content in the fuel. Due to the

high oxygen content of biodiesel, it is generally accepted that biodiesels are about

10% less energy dense as compared with petroleum diesel. The heating value of

biodiesel is related to its fatty acid profile. Heating value increases with increasing

carbon number in fuel molecules due to mass fraction decreases (Demirbas 2003).

Studies have also found that unsaturated esters have lower mass energy content

(MJ/kg). Demirbas (Demirbas 2008b) studied the correlation between viscosity and

higher heating value (HHV) by performing a linear least square regression analysis.

This study found that there is a high correlation between the heating value and the

viscosity of vegetable oils and their methyl esters and that the heating value of

vegetables oils and biodiesels increases with viscosity.

2.1.7.5 Flash point

Flash point is often used as a descriptive characteristic of liquid fuel and is defined as

the lowest temperature at which the fuel will start to vaporise to form an ignitable

mixture when it comes to contact with air (Ali, Hanna and Cuppett 1995a). Hence,

flash point is an important parameter for assessing fire hazards during fuel transport

and storage. This is reflected by the respective limits within Australian standards

(≥120 °C) and the European fossil diesel standard, EN 590 (>55 °C). However, the

flash point of biodiesel is approximately double that of petroleum diesel, which

makes biodiesel a more acceptable engine fuel in relation to concerns about safety. It

is also an important parameter in engine combustion performance. Canakci and Sanli

[93] found that with a high flash point, NOx emission decreased due to low

combustion pressure and temperature. Moreover, high flash point is also important in

niche applications such as underground mining. On the other hand, studies show that

a high flash point can cause cold engine startup problems, misfiring and ignition

delay, which increases carbon deposition in the combustion chamber (Ali, Hanna and

Cuppett 1995a).


Biodiesels from animal fat generally have higher flash points than those from

vegetable oils. This is the result of the highly saturated fatty acid compounds in

biodiesels from animal fats increasing the flash point temperature. Alcohol residue in

biodiesel significantly decreases flash point (Canakci and Sanli 2008).

2.1.7.6 Oxidation stability

Oxidation stability is an important fuel property which reflects the resistance to

oxidation during long-term storage. Usually biodiesels are more sensitive to

oxidative degradation than petroleum diesel due to their chemical composition. Fuel

quality declines due to gum formation during the oxidation process. This gum does

not combust completely resulting in poor combustion, carbon deposits in the

combustion chamber and lubrication oil thickening (Ma and Hanna 1999). Monyem

et al. (Monyem and H Van Gerpen 2001) observed that oxidised biodiesel starts to

burn earlier than unoxidised, increasing NOx emissions due to the associated increase

in viscosity and cetane number.

The chemical structure of biodiesel is an important factor in the oxidation reaction.

Oxidation is influenced by the presence of double bonds in the chains, that is,

feedstocks rich in polyunsaturated fatty acids are much more susceptible to oxidation

than the feedstocks rich in saturated or monounsaturated fatty acids (Graboski and

McCormick 1998). However, our understanding of oxidation is complicated by the

fact that fatty acids usually occur in complex mixtures, with minor components in

these mixtures catalysing or inhibiting oxidation. In addition, the rates of the

oxidation of different unsaturated fatty acids or esters can vary considerably. The

other factors that affect the oxidation stability of biodiesel include double bond

configuration, temperature, air, light and storage tank materials (Balat and Balat

2008).

2.1.7.7 Cold temperature properties

One of the major problems associated with the use of biodiesel in countries with a

cold climate include the poor cold flow properties when compared with petroleum


diesel fuels. The parameters generally used to determine cold flow properties are

cloud point (CP), pour point (PP) and cold-filter plugging point (CFPP). CP is the

temperature at which a material becomes cloudy due to the formation of crystals and

solidification of saturates, with PP being the lowest temperature at which the fuel can

be pumped (Lee, Johnson and Hammond 1995). In general, the CP occurs at a higher

temperature than the PP, with the CFPP defined as the lowest temperature at which a

fuel portion will pass through a standardised filtering device in a specified time.

Studies have found that biodiesel from all feedstock has a relatively high CP, PP and

CFPP when compared with petroleum diesel (Durrett, Benning and Ohlrogge 2008).

Moreover, biodiesel contains relatively few components compared to petroleum

diesel, and each component has its own solidification temperature. Therefore, the

solidification of biodiesel is more rapid and difficult to control as one or two

components can tend to dominate. At low temperatures, solids and crystals rapidly

grow and agglomerate, clogging fuel lines and filters and causing major operability

problems.

The fatty acid composition of biodiesel greatly influences its cold-flow properties.

Biodiesels made from feedstocks containing higher concentrations of high-melting

point saturated long-chain fatty acids tends to have relatively poor cold flow

properties (Dunn and Moser 2010). Studies found that the freezing point of a

biodiesel fuel increases with increasing numbers of carbon atoms in the carbon chain,

and decreases with increasing double bonds (Graboski and McCormick 1998;

Knothe 2005; Demirbas 2003). Therefore, saturated components have poor cold flow

properties over unsaturated components. Moreover, the double bond position also

affects the cold flow properties. Rodrigues (Rodrigues Jr et al. 2006) reports that a

double bond position near the end of the carbon chain results in poor cold flow

properties as compared with a double bond found in the middle of the molecules.

While conducting studies on the effects of chemical structure on the crystallisation

temperature using a series of branched alcohol esters, Nascimento et al. (Nascimento

et al. 2005) observed in the studies that branching in the carbon chain reduces the

crystallisation of esters.


2.1.7.8 Lubricity

Lubricity is defined as the ability of fuel to provide hydrodynamic and/or boundary

lubrication to prevent wear between engine moving parts. It is an important

parameter for diesel engine operation, because poor lubrication leads to the failure of

engine parts, such as fuel injectors and pumps as they are directly lubricated by the

fuel itself (Lacey and Lestz 1992). Increasingly strict regulations on the sulphur

content of commercial petroleum fuel have resulted in a decrease in fuel lubricity

over time. Therefore, the issue of lubricity in fuel is becoming increasingly important

with respect to diesel engine operation. Biodiesels typically have superior lubrication

properties when compared with petroleum diesel (Hu et al. 2005). Studies show that

biodiesel derived from vegetable oils can significantly increase diesel fuel lubricity at

blend concentrations of less than 1%. Therefore, biodiesel can be used as an additive

to improve the lubricity of petroleum fuel (Goodrum and Geller 2005; Anastopoulos

et al. 2005; Anastopoulos et al. 2001; Van Gerpen, Soylu and Tat 1999).

Knothe and Steidley (Knothe and Steidley 2005) examined the lubricity of biodiesel

and petroleum diesel components. They found better lubricity in fatty acid

compounds than hydrocarbons in petroleum diesel. This study also reported that pure

free fatty acids, mono-acylglycerols, and glycerol possess better lubricity than pure

esters. The main components responsible for biodiesel lubricity are FAMEs,

hydroxyl groups and mono-acylglycerols followed by free fatty acids (FFAs) and di-

acylglycerols, whereas triglycerols do not have any significant effect on the lubricity

of biodiesel (Geller and Goodrum 2004; Hu et al. 2005) On the other hand, the

structures of fatty acids have an impact on biodiesel lubricity. Increasing saturation

leads to a stronger lubrication layer as molecules can align themselves more easily in

straight chains and when they are packed closely on the surface (Wadumesthrige et

al. 2009). However different results have been reported by Knothe (Knothe 2005),

Anastopoulos et al. (Anastopoulos et al. 2001) and Bhuyan et al. (Bhuyan et al.

2006) who found that lubricity increases with the increase in length of fatty acid

chains, while Geller et al. (Geller and Goodrum 2004) showed that there is no

consistent trend relating chain length to lubricity enhancement.


2.1.7.9 Iodine value

Iodine value (IV) is a common method used to determine the degree of unsaturation

in a mixture of fatty materials regardless of their relative share of mono-, di- and

polyunsaturated compounds (Knothe and Dunn 2003). When iodine is added to the

fat or oil, the amount of iodine in grams absorbed per 100 mL of oil is reported as the

IV. As it is a measure of total unsaturation, several studies found a linear correlation

between the degree of unsaturation (DU) and the IV: the more unsaturation in the oil,

the higher the iodine value (Lin, Lin and Hung 2006; Kyriakidis and Katsiloulis

2000). Methyl esters show an almost identical IV to that of corresponding vegetable

oils or animal fats (Knothe 2005).

Iodine value is an important parameter in regard to fuel quality because higher IV

biodiesel leads to a higher rate of polymerisation of glyceride, which increases

rapidly with temperature. This results in increasing fuel viscosity, adversely affecting

the fuel’s ease of flow, and causing the formation of engine deposits, thus adversely

affecting fuel injector spray patterns. This will ultimately lead to poor combustion,

high emissions and, consequently, engine failure (Knothe 2005).

2.2 BIODIESEL AS A DIESEL ENGINE FUEL

Diesel engines produce mechanical power from conversion of the chemical energy

contained in the fuel. Energy is released by the combustion and oxidisation of the

fuel inside the engine. The fuel-air mixture prior to combustion and the products

from combustion are the working fluids. The boundary work which provides the

desired power output occurs directly between these working fluids and the

mechanical components of the engine (Heywood 1988).

Since the advent of the diesel-powered engine, compression ignition engine

technology has been under continuous development. However, the basic components

of the engine (Figure 2-7) have been unchanged, with the main difference between a


modern day engine and its predecessor being its combustion performance (Ferguson

and Kirkpatrick).

Biodiesel can be used in modern diesel engines in its pure form (B100), or blended

with petroleum diesel in any ratio (Lebedevas and Vaicekauskas 2006). There is an

increasing body of literature reporting on research into diesel engine performance

and engine emissions when fuelled with biodiesel. Some of these studies are

summarised in Table 2-6.

Figure 2-7: Schematic diagram of a typical diesel engine fuel system (Haseeb et al. 2011b)

2.2.1 Engine performance

Diesel engine performance parameters evaluated with biodiesel fuels in literature

typically includes engine power, torque, brake specific fuel consumption (BSFC),

thermal efficiency, and exhaust gas temperature (Table 2-5). While illustrating the

effect of biodiesel on engine power and/or torque, it is commonly argued that

biodiesel drops engine power and torque. This is mainly due to the lower heating

value of biodiesel compared with petroleum diesel. Utlu and Kocak (Utlu and Koçak

2008) ran a four-cylinder diesel engine with waste frying oil methyl ester (WFOME),

varying the engine speed from 1750 to 4400 rpm. They found on average a 4.5% and


4.3% reduction in power and torque respectively. Similar results have been reported

in many other studies, with some fluctuations in the reduction percentage. Studies

found that the loss of power was 7.14% for biodiesel when compared to diesel on a

three-cylinder, naturally aspirated (NA) submarine diesel engine at full load, yet the

loss of heating value of biodiesel was about 13.5% when compared to diesel

(Karabektas 2009; Hansen, Gratton and Yuan 2006; Murillo et al. 2007). Hansen et

al. (Hansen, Gratton and Yuan 2006) observed that the brake torque loss was 9.1% in

biodiesel at 1900 rpm as the results of variation in heating value (13.3%), density and

viscosity. Findings from these studies confirm that the lower heating value of

biodiesel is not the only factor which influences engine power and torque. Other

biodiesel fuel properties including viscosity, density and lubricity have significant

effects on engine output power and torque.

Table 2-6: Performance and emission of diesel engines with biodiesel

Fuel type Engine Test condition Increase/decrease vs. diesel

References Power Torque BSFC BTE Texh CO CO2 NOx PM HC

Soybean 1C 1400–2000 rpm ↓ ↓ ↑ ↓ ↓ ↓ (Qi et al. 2010)

Cottonseed 1C 850 rpm ↑ ↓ ↓ ↑ ↓ (Nabi, Rahman and Akhter

2009)

Sunflower 1C 1000–3000 rpm ↓ ↓ ↑ ↓ ↓ ↓ ↓ (Ilkılıç et al. 2011)

Waste cooking 4C 1750–4400 rpm ↓ ↓ ↑ ↓ ↓ ↓ ↓ (Utlu and Koçak 2008)

Cottonseed 4C 1500 rpm ↓ ↓ ↑ ↓ ↓ ↑ ↓ (Kumar 2009a)

Sunflower oil 4C 1100–2800 rpm ↓ ↓ ↑ ↓ ↑ ↓ (Haşimoğlu et al. 2008)

Soybean 4C 1400 rpm ↓ ↓ ↑ ↓ ↓ ↓ ↓ ↑ ↓ ↓ (Canakci 2007)

Waste cooking 4C 800–1400 rpm ↓ ↓ ↑ ↑ ↓ ↓ ↑ (Lin and Li 2009a)

Waste cooking 4C 1400–200 rpm ↓ ↓ ↑ ↓ ↓ ↓ ↑ (Lin and Li 2009a)

Mahua 1C 1600 rpm ↓ ↓ ↑ ↓ ↑ ↓ ↑ (Raheman and Ghadge 2007)

Tobacco seed 4C 1500–300 rpm ↑ ↑ ↓ ↑ ↓ ↓ ↑ (Usta 2005)

Rapeseed oil 4C 1200–2400 rpm ↑ ↑ ↓ ↓ ↑ (Karabektas 2009)

Cottonseed 1C 1200–2500 rpm ↑ ↑ ↓ ↑ ↓ ↓ ↑ (Aydin and Bayindir 2010)

For instance, higher viscosity of biodiesel improves air-fuel mixing by enhancing

spray penetration, and thus recovery in power and torque when compared to diesel

(Öner and Altun 2009; Monyem, Van Gerpen and Canakcl 2001). Higher viscosity

can also reduce engine power by decreasing combustion efficiency due to poor fuel

injection atomisation (Aydin and Bayindir 2010). On the other hand, the higher

density of biodiesel improves engine power and torque. Moreover the high lubricity


in biodiesel may result in reduced friction power loss, and this will subsequently

recover engine output power and torque (Ramadhas, Muraleedharan and Jayaraj

2005). Therefore, it is not surprising that some studies have reported increased power

and torque from engines when running on biodiesel.

For an example, Song and Zhang (Song and Zhang 2008) showed power and torque

increased with an increase in biodiesel percentage in blends while running an engine

with soybean oil methyl ester. Usta (Usta 2005) also found similar results when

using tobacco seed oil in a four-cylinder turbo-charged diesel engine. Furthermore,

negligible variation in engine power and torque between biodiesel and petroleum

diesel has also been found (Luque et al. 2008; Pal et al. 2010). More interesting

results have been reported by Haşimoğin et al. (Haşimoğlu et al. 2008) while using

waste cooking oil biodiesel in a four-cylinder turbo-charged diesel engine operating

between 1100 and 2800 rpm. This study found lower engine torque and power at

lower engine speeds (1100 to 1600 rpm) while power and torque increased at

medium and high engine speeds. However, Carraretto et al. (Carraretto et al. 2004)

has overcome the power loss of biodiesel engine by optimising biodiesel combustion

through reducing the injection advance. It is therefore evident that power and torque

developed in biodiesel engines is not only dependent on feedstock and fuel

properties, but also on the engine type and operating conditions, such as engine

speed, load, injection timing and injection pressure. Similar correlations have been

found in the literature for other performance parameters such as brake specific fuel

consumption, thermal efficiency, exhaust gas temperature and combustion

characteristics (Lin et al. 2011; Raheman and Ghadge 2007; Aydin and Bayindir

2010; Murillo et al. 2007).

2.2.2 Exhaust emissions

Combustion chemistry in internal combustion engines (ICE) is very complex and

depends on fuel types and operating conditions. In the combustion chamber,

hydrocarbon reactions are generally grouped into three distinct steps. The first step is

the breakdown of hydrocarbons; the second step is the oxidation of hydrocarbons and

hydrogen; the third step is the oxidation of combustion reaction products. The


exhaust gas from diesel engines contains many components including carbon dioxide

(CO2), carbon monoxide (CO), hydrogen (H2), oxygen (O2), sulphur oxides (SOx),

unburned hydrocarbons (HC), particular matter (PM), and nitrogen oxides (NOx).

These pollutants have various potential adverse health and environmental effects.

Numerous studies have been conducted to investigate the effect of biodiesel on

exhaust emissions in diesel engine applications. The emission parameters

investigated include carbon dioxide (CO2), carbon monoxide (CO), hydrocarbon

(HC), nitrogen oxides (NOx), sulphur oxides (SOx) smoke, particulate matter (PM).

Table 5 shows that most of the studies found a sharp reduction in all exhaust

emissions when biodiesel was used was compared with petroleum diesel fuel (except

NOx). However, a reduction in NOx in biodiesel use has also reported in some other

literature.

In general, biodiesel contains about 10% oxygen by mass, while diesel has little to no

oxygen. Biodiesel fuels result in more complete combustion and thereby reduces

exhaust emission, and various researchers have postulated reasons for this outcome.

The percentage change in emissions varies amongst these studies. The variety of

results reported can be attributed to variations in the fuel properties and chemical

structure of the biodiesels used, varying feedstocks and due to the variety of engines

used in tests. For example, Lin et al. (Lin and Li 2009a) conducted an experiment

with biodiesel from eight different feedstocks and found a significant reduction in

PM emissions (50%–73%). While conducting experiments with coconut, jatropha

and rapeseed oil biodiesel, Lance et al. (Lance et al. 2009) showed that rapeseed oil

biodiesel tended to give amongst the highest NOx emissions. Similarly, variations in

emissions from biodiesels using different feedstocks have been reported in many

other recent studies (Wu et al. 2009; Sahoo et al. 2009; Ozsezen et al. 2009;

Banapurmath, Tewari and Hosmath 2008). Monyem and Gerpen (Monyem and H

Van Gerpen 2001) found that oxidised biodiesel can significantly reduce emissions

while investigating the effect of biodiesel oxidation on diesel engine emissions. This

study found that oxidised biodiesel resulted in approximately 15% less CO emissions

and 21% less HC emission when compared with unoxidised biodiesel.


The vast majority of the literature reports that NOx emissions are the only parameter

that increases while operating diesel engines on biodiesel as compared to petroleum

diesel. Therefore, NOx emission may be the single most critical parameter for

biodiesel application. Many studies have suggested that properties of biodiesel such

as cetane number, oxygen content, biodiesel feedstock, advance in fuel injection,

engine type and operating conditions have an important effect on the formation of

NOx (Xue, Grift and Hansen 2011; Fazal, Haseeb and Masjuki 2011). It is commonly

argued that high cetane numbers improve combustion, therefore the temperature in

the combustion chamber is expected to be higher which leads to the formation of

more NOx emissions in higher oxygen content fuel. Another common argument is

that a high cetane number reduces ignition delay which can cause higher NOx

emission. However, some authors oppose this argument. NOx usually forms in the

combustion phase and a high cetane number not only reduces ignition delay, but also

leads to lowering of the premixed combustion phase, which eventually reduces the

formation of NOx. Therefore, it is not surprising that some literature reports show a

reduction of NOx emission with biodiesel (Qi et al. 2010; Utlu and Koçak 2008;

Karabektas 2009). Moreover, the different chemical structure of biodiesels influences

the formation of NOx emissions and most of the authors agreed that shorter chain

lengths and more saturated biodiesels were preferable to reduce NOx emission.

Knothe et al. (Knothe and Steidley 2005) tested NOx in a six-cylinder diesel engine

with conventional diesel fuel and three different fatty acid methyl esters including

oleic (C18:1), palmitic (C16:0) and lauric (C12:0). They found a 4% and 5%

reduction in NOx emissions for the saturated palmitic and lauric esters, respectively,

and a 6% increase for the oleic ester. While blending short chain methyl esters such

as caprilic (C8:0) and capric (C10:0) with soybean oil biodiesel, Chapman and

Boehman, (Chapman 2006) also found a significant reduction in NOx emission.

Particulate matter is another important factor which needs to be considered while

using biodiesel as an engine fuel. Particulate matter emitted by petroleum diesel

engines consists of black carbon (soot), hydrocarbons, sulphates and metallic ashes

(Majewski 2002). Most studies found in the literature indicate that biodiesel reduces

diesel engine particulate emissions on a mass basis. However, adverse health effects


from exposure to particulates may increase with a decreasing in particle size, even

though the particles are composed of toxicologically inert materials. For example,

fine particles with aerodynamic diameters lower than 2.5 µm (PM2.5) appear to have

considerably enhanced toxicity per unit mass compared to coarse particles with

aerodynamic diameters lower than 10 µm (PM10). Particles deposit in different parts

of the lung according to their aerodynamic diameter, and smaller particles are able to

penetrate deeper into the human lung. Furthermore, smaller particles tend to stay in

the atmosphere for longer periods of time, which means that there is a higher

probability that they will be inhaled and lead to respiratory diseases, inflammation

and damage to the lungs (Garshick et al. 2004). By considering these adverse effects

of ultrafine particles, an emission standard based on a solid particle number has

already been implemented by the European Union (EU) for its member states.

Particle number and size distribution is therefore a more appropriate parameter of

DPM when placing regulatory controls on the use of fuels, rather than relying on PM

mass alone (Surawski et al. 2009).

2.3 ARTIFICIAL NEURAL NETWORKS

The foundation of artificial neural networks (ANN) in a scientific sense begins with a

biological neuron as shown in Figure 2-8. In the brain, there is a flow of coded

information (using electrochemical media, the so-called neurotransmitters) from the

synapses towards the axon. The axon of each neuron transmits information to a

number of other neurons. Groups of neurons are organised into sub-systems and the

integration of these sub-systems forms the brain. On the other hand, an ANN is

composed of a large number of simple processing units called neurons which are

fully connected to each other through adoptable synaptic weight (Figure 2-9). This

resembles a brain in two aspects. Knowledge can be acquired through training and

knowledge can be stored. In the training process, weights are adjusted to minimise

the error between actual output and desired output.

The most important feature of artificial neural networks is their ability to solve

problems through learning by example, rather than by fully understanding the


detailed characteristics of the systems. This feature makes it very useful because it

works like a “black box” model, and does not require detail or complete information

about the problem, and can be utilised when all that is available are sets of data

inputs and outputs of the system. It has a natural propensity to store experiential

knowledge and to make it available for use (Figure 2-10). Therefore, this nonlinear

computer algorithm can model large and complex systems with many interrelated

parameters.

Figure 2-8: Biological neuron (Kalogirou 2003)


Figure 2-9: Multi-layer ANN model

Figure 2-10: Working principle of ANN

Since the development of high speed digital computers, the application of the ANN

approach has progressed at a very rapid rate. In recent years, this method has been

applied to various disciplines including automotive engineering, and in the

forecasting of fuel properties and engine thermal characteristics for various working


conditions (Ramadhas et al. 2006b). Prediction accuracy of the ANN approach was

found to be superior when compared with other linear and non-linear statistical

techniques.

Balabin et al. (Balabin, Lomakina and Safieva 2011) compared the prediction

performance of artificial neural networks (ANN), multiple linear regression (MLR),

principal component regression (PCR), polynomial and Spline-PLS versions, and

partial least squares regression (PLS) for prediction of biodiesel properties from near

infrared (NIR) spectra. The model was created for four biodiesel properties density

(at 15 °C), kinematic viscosity (at 40 °C), water (H2O) content and methanol content.

This study reported the lowest root mean square errors of prediction (RMSEP) for

ANN when compared to other techniques as shown in Figure 2-11. Agarwal et al.

(Agarwal, Singh and Chaurasia 2010) compared the linear regression and ANN

techniques in predicting biodiesel properties. Results of this study indicated that

ANNs were able to predict the properties of biodiesel better than a linear regression

model. Similarly Cheenkachorn (Cheenkachorn 2004) predicted biodiesel properties

such as viscosity, high-heating value, and cetane number using the fatty acid

compositions of various vegetable oils by statistical methods and ANN. It was

observed that ANN was able to predict more accurately than statistical methods.

Figure 2-11: Comparison of the performance of between ANN and various linear and

non-linear prediction techniques (Balabin, Lomakina and Safieva 2011).


On the other hand, ANN is not without having limitations. The main disadvantage of

an ANN model is due to the “black box” approach; it is difficult to gain insight in to

a problem without extra effort. Statistical techniques allow the user to determine the

most significant parameters among the important variables, hence, eliminating the

variables that do not fit the model which is not standby available with ANN.

However, this limitation can be overcome where necessary by combining the ANN

with multivariate data analysis, such as PCA or ANOVA. Moreover the requirements

of computational resources and standard software for ANN modeling may also be

considered as drawbacks over statistical techniques (Agarwal, Singh and Chaurasia

2010).

2.3.1 ANN in predicting engine emission and performance

One of the most important factors that affect the viability of an automobile fuel is its

suitability in terms of engine performance and exhaust emissions as discussed in the

previous section. However, conducting actual experiments with automobile engines

not only requires considerable amounts of fuel, heavy equipment and skilled

personnel, but it is also very time consuming and costly. Therefore, the use of ANN

modeling techniques in predicting performance parameters of internal combustion

engines has gained in popularity over the last few decades. Combustion related

performance using various types of fuels including diesel, gasoline, natural gas,

ethanol and biodiesel have successfully been modeled using ANN. Cay et al. (Cay et

al. 2012) developed an ANN model to predict the brake specific fuel consumption,

effective power and average effective pressure and exhaust gas temperature of the

methanol engine. A four-cylinder, four-stroke test engine was operated at different

engine speeds and torques to obtain model training and testing data. After training,

this study found an ANN prediction accuracy with R2 values close to 1 for both

training and testing data. RMS values less than 0.015 and mean errors less than 3.8%

for the testing data were reported. This study concluded that the ANN model is a

powerful technique for predicting performance parameters of internal combustion

engines.


Similarly, while predicting engine performance emissions with ANN, Sayin et al.

(Sayin et al. 2007) found prediction performance with correlation coefficients in the

range of 0.983–0.996, mean relative errors in the range of 1.41%–6.66%, and very

small root mean square (RMS) values, and concluded that ANN was an alternative to

classical modelling techniques. Arcaklioğlu and Çelıkten (Arcaklioğlu and Çelıkten

2005) conducted experiments with petroleum diesel in a turbo-charged four-cylinder

diesel engine. Using experimental data, the ANN model was developed to predict

engine torque, power, brake mean effective pressure, specific fuel consumption, fuel

flow, and exhaust emissions such as SO2, CO2, NOx and smoke level based on

injection pressure, engine speed and throttle position. The study found the precise

prediction ability of the ANN in diesel engine performance and emission parameters

provided a good correlation between experiment and predicted values. The overall

mean squire errors in their study were less than 0.03% and R2 values where close to

1.

Yap and Karri (Yap and Karri 2012) conducted experiments on a single-cylinder,

spark ignition engine using various engine speeds and throttle positions. They have

developed an ANN model for the prediction of engine power, CO, CO2, HC and O2

emissions. Parlak et al. (Parlak et al. 2006) also successfully used ANN to predict

diesel engine brake specific fuel consumption and NOx emission for a Ricardo E6

type, single-cylinder diesel engine considering engine speed, break mean effective

pressure and fuel injection timing as input variables.

ANN modelling techniques have also been utilised for predicting engine

performance and emissions based on the physical properties of various fuels.

Canakci et al. (Canakci et al. 2009) used ANN model in predicting fuel flow rates,

maximum injection pressure, thermal efficiency, load, maximum cylinder pressure

and exhaust emission (CO, NOx, HC) of a diesel engine based on fuel density, kinetic

viscosity and lower heating value. Similarly, Karonis et al. (Karonis et al. 2003) were

successful in predicting exhaust emissions (CO, NOx, HC and PM) of a single-

cylinder diesel engine using fuel cetane number, density and kinetic viscosity.


ANN has also been used to predict diesel engine emissions from in-cylinder pressure

along with engine operating parameters (Kesgin 2004; Manjunatha, Narayana and

Reddy 2010) investigated the effectiveness of various biodiesel fuel properties and

engine operating conditions on diesel engine combustion towards the formation of

exhaust emissions using ANN. They conducted experiments on a single-cylinder

direct injection (DI) diesel engine using blends of biodiesel from pongamia, jatropha

and neem oils. This study found good predictability of ANN modelling techniques in

predicting brake power, brake thermal efficiency, brake specific fuel consumption,

volumetric efficiency, and exhaust gas temperature regulated exhaust emissions (CO,

HC, NOx) based on diesel-biodiesel blend percentage, fuel properties and various

engine operating conditions. Similarly, different types of engines have been

successfully modeled using ANN techniques in a wide range of operating conditions,

performance parameters and fuels which are summarised in Table 2-7. All of these

studies demonstrated the suitability of ANN modeling techniques in internal

combustion engine application.

2.3.2 ANN in predicting fuel properties

The properties of fuel need to be estimated before their application to particular

combustion systems as these will significantly influence the end use performance of

such systems, as shown in previous sections. Modern official standards list more than

20 parameters that must be determined to certify any fuel’s quality before its use as

automobile engine fuel. However, testing of these properties requires considerable

amounts of fuel sample, standardised testing equipment, expert technicians and also

carries a significant cost (Balabin, Lomakina and Safieva 2011). Therefore it is a

worthwhile option to consider a prediction model to estimate the properties of any

new fuel prior to beginning large-scale production. As a consequence, a number of

models have been developed to predict the important fuel properties of diesel and

biodiesel using conventional linear regression and ANN techniques. Most of these

are based on fuel types, diesel-biodiesel blend ratios, chemical composition,

production methods etc., Agarwal et al. (Agarwal, Singh and Chaurasia 2010)

developed linear regression and an ANN model to predict several fuel properties of

biodiesel including heating value, density, viscosity, pour point, flash point, iodine

value and saponification value based on fatty acid composition. Experiments were


conducted using biodiesel produced from various edible and non-edible vegetable

oils. This study found a good correlation between the properties of biodiesel and its

chemical composition, with the ANN demonstrating a higher prediction ability than

linear regression techniques in predicting all the fuel properties.

A similar study has been conducted by Cheenkachorn focused on determining

biodiesel fuel properties based on fatty acid profile only (Cheenkachorn 2004).

However, these studies did not consider other chemical compositions contained in

biodiesel through the model input, including the amount of free glycerol, free fatty

acid, methanol and impurities which may have an impact on the physical properties

of the biodiesel. Kumar and Bansal (Kumar and Bansal 2010) compared the

applicability of the traditional statistical technique of linear regression (principle of

least squares) and ANN techniques in estimating the flash point, fire point, density

and viscosity of diesel and biodiesel mixtures. They have optimised the network with

three training algorithms, along with ten different sets of weight and biases. Results

of this study show that neural network is the better choice over principle of least

squares to predict the fuel properties of various mixtures of diesel and biodiesel.

However, the performance of a neural network can further be improved by adjusting

the other training parameters like goal, epochs, learning rate, magnitude of the

gradient, etc.


Table 2-7: ANN used in automobile engine application

References Model Input Model Target Engine Prediction accuracy Fuel used

(Arcaklioğlu and Çelıkten 2005) IP, RPM, TH T, P, BMEP, BSFC, FFR, SO2, CO2,

NOx, S

4S, 4C, TC R2: 0.9999; MSE: 8.5% Diesel

(Yap and Karri 2012) RPM, AFR, TH P, CO, CO2, HC, O2 2S, 1C, SI MSE: 2.27%–4.74% Gasoline

(Parlak et al. 2006) RPM, BMEP, IT BSFC, Texh 4S; 1C, NS, CI MSE: 1.93%–2.36% Diesel

(Choi and Chen 2005) CR, FFR, AFR, IT, EGR Start-of-combustion 4S, 1C, HCCI - Methane/n-heptane

(Karonis et al. 2003) CN, ρ, ν, Distillation curve CO, HC, NOx, PM 4S, 1C, CI R2: 0.937–0.99 Diesel

(Çelik and Arcaklioğlu 2005) RPM, P, Cooling water temperature BSFC, Texh, AFR 4S, 8C, CI R2: 0.99, MRE: 5.5% Diesel

(Deh Kiani et al. 2010) RPM, L, FBR P, T, CO, CO2, NOx, HC 4S, 4C, SI R2: 0.71–0.91 Gasoline, Ethanol

(Renald and Somasundaram 2012) L, FDR, Cylinder head geometry Texh, CO, CO2, O2, HC, NOx, ET 1C, 4S, SI - Gasolin, LPG

(Yusaf et al. 2010) RPM, FBR P, T, BTE, BSFC, Texh, NOx, CO, CO2 4S, 1C, CI R2: 0.95707–0.9934 Diesel, CNG

(Yusaf, Yousif and Elawad 2011) RPM, FBR P, T, BTE, BSFC, Texh, NOx, CO, CO2 4S, 1C, CI MSE: 0.0004 Diesel, CPO

(Obodeh and Ajuwa 2009) L, RPM NOx 4S, 4C, CI MRE: 0.68%–3.34%. Diesel

(Srinivasa Pai and Shrinivasa Rao 2011) L, IT, CR, FBR BTE, BSEC, Texh, NOx, HC 4S, 1C, CI MRE: 1.778%–5.889% Diesel, WCO biodiesel

(Tasdemir et al. 2011) RPM, Intake valve opening advance P, T, BSFC, HC 4S, 1C, SI - Gasoline

(Sayin et al. 2007) T, RPM, HV, Air inlet temperature BSFC, BTE, Texh, CO, HC 4S, 4C, SI R2: 0.983–0.996 Gasoline

(Kesgin 2004) ECP, CT, FAR, RPM, ES, CB, VT P, BTE, NOx, Heat Transfer TC, SI - Natural gas

(Canakci et al. 2009) RPM, HV, ρ, ν, Environmental conditions FFR, BTE, CO, NOx, HC, L, IP, ECP 4S, 4C, NA, CI R2: 0.99 WCO, Diesel

(Shanmugam et al. 2011) L, FBR BTE, CO, HC, CO2, NOx, S 4S, 1C, NA, CI R2: 0.975–0.999 Diesel, Bioethanol,

Cottonseed biodiesel

(Sharon et al. 2012) P, FBR BSFC, BTE, NOx, HC, CO, S 4S, 1C, NS R 2: 0.9989–0.999 Diesel, WCO biodiesel

(Manjunatha, Narayana and Reddy 2010) L, HV, CN, ρ, FBR P, BTE, BSFC, Texh, CO, HC, NOx 4S, 1C, CI R2: 0.95–0.99 Diesel, pongamia, jatropha,

neem oils biodiesel


Table 2-8: ANN in predicting fuel properties

References Use of ANN model (Boudy and Seers 2009) Density prediction in different temperatures for palm oil biodiesel (Ramadhas et al. 2006b) Cetane number prediction based on the fatty acid profile of biodiesel (Agarwal, Singh and Chaurasia 2010)

Density, kinetic viscosity, water and methanol content prediction for various biodiesels

(Kumar, Bansal and Jha 2007)

Flash point, fire point, density and viscosity prediction based on diesel-biodiesel blend ratio

(Liu et al. 2007) Density, flash point, freezing point, aniline point and net heat of combustion prediction for various jet fuels based on their chemical composition

(Korres et al. 2002) Lubricity prediction from physical properties of diesel (Marinović et al. 2012) Prediction of diesel cold temperature properties based on density,

kinetic viscosity, conductivity, sulphur content and 90% distillation point

(Pasadakis, Gaganis and Foteinopoulos 2006)

Octane number prediction from chemical composition of gasoline

(Pasadakis, Sourligas and Foteinopoulos 2006)

Cold temperature properties and distillation curve prediction from the chemical composition of diesel

(Wu et al. 2006) Prediction of cold filter plugging point of diesel from physical properties

(Yang et al. 2002) Cetane number and density prediction using chemical composition of diesel

(Cheenkachorn 2004) Viscosity, cetane number and heat of combustion prediction from fatty acid composition of various biodiesel feedstocks

Satyanarayana and Muraleedharan (Satyanarayana and Muraleedharan 2010)

developed the ANN model to analyse the relation of esterification methods with fuel

properties for biodiesel produced from rubber seed. They used transesterification

reaction parameters such as the methanol-oil ratio, catalyst concentration, reaction

time, and reaction temperature in the input layer and acid value of biodiesel in output

layer while constricting the ANN model. Although a good prediction was obtained,

this study did not consider the initial acid value of the vegetable oil, which may have

an impact on the final acid value of the biodiesel. This issue has been addressed by

Rajendra et al. (Rajendra, Jena and Raheman 2009) while using ANN techniques to

predict the acid value of sunflower oil biodiesel, including its initial acid value as

well as transesterification reaction parameters. Liu et al. (Liu et al. 2007) compared

the reputability of standard fuel property testing methods with ANN while

developing a model to predict density, flash point, freezing point, aniline point and

net heat of combustion of 80 different jet fuels. This study found that the


repeatability of neural network models for measuring density and flash point were

lower than the ASTM test methods. However, for the freezing point, aniline point

and net heat of combustion, the repeatability of the ANN methods are equal to the

ASTM methods. Therefore it can be said that not only the prediction accuracy but

also the ANN approaches are comparable to the repeatability values of the standard

ASTM methods, which are used for the experimental determination of the properties.

Similar conclusions have been made by Pasadakis et al. (Pasadakis, Gaganis and

Foteinopoulos 2006; Pasadakis, Sourligas and Foteinopoulos 2006) while predicting

pour point (CP), cloud point (CP) of diesel and octane number of gasoline based on

the chemical composition of the respective fuels. Several other studies which were

also successful in utilising ANN to estimate the properties of various fuels, which are

summarised in Table 2-8. The success of those studies proved that ANN has the

ability to accurately estimate the fuel properties instead of having costly and time

consuming experimental measurements.

2.4 ANN MODELING OF SECOND-GENERATION BIODIESEL

Although numerous feedstocks including oilseed crops and algae species have been

identified as being suitable for producing second-generation biodiesel, these types of

biodiesel have not yet been established, due to the unavailability of feedstock supply,

high production costs and a lack of knowledge about the fuel’s quality. Moreover, by

producing fuels from new feedstock, optimising production procesess, ensuring fuel

quality through measuring a number of physical and chemical properties, and

evaluating the end-use performance in automobile engines is costly, time-consuming

and requires a wide variety of specialised equipment and skilled workers. These

concerns have restricted the progress of second-generation biodiesel technology,

making it still unacceptable to both automobile engine manufacturers and customers,

which is yet to begin industrial-scale production. In order to address this issue, the

ANN modelling technique could be a very useful tool in predicting fuel quality and

engine combustion-related parameters when considering the chemical composition of

new biodiesel feedstocks. It would require laboratory scale biodiesel production and

basic chemical testing equipment, which will significantly reduce the research cost,


and hence accelerate the investigation of future generation biodiesel. However,

researchers should move to develop a universal ANN prediction model for second-

generation biodiesel, and this will enable the instigation of a wide range of biodiesel

feedstock and automobile engine systems. While training the network, they need to

consider all possible parameters in feedstock that affect the production process, the

quality and the combustion performance of biodiesel in automobile engine

applications. A two-stage artificial neural network (ANN) prediction model can be

proposed for this purpose. At the 1st stage of the model, chemical composition of

biodiesel in terms of fatty acid profile can be used as input parameters and fuel

properties can be used as output or target variable at. In the 2nd stage of the model,

fuel properties alone with engine specification and operating condition can be used

as input layer, whereas, engine performance, emission and combustion parameters

can be used as the target vector of output layer. The structure of such a two-stage

ANN model as proposed is shown in Figure 12. It can be expected that such an

approach will generate new knowledge, based upon which, second-generation

biodiesel will be more sustainable, commercially available and a key contributor to

the mainstream global energy system.

Figure 2-12: Proposed structure of ANN model


2.5 CONCLUSIONS

Biodiesel, produced from renewable feedstocks represents a more sustainable source

of energy and will therefore play a significant role in providing the energy

requirements for transportation in the near future. However, first-generation

biodiesels used around the World today are unlikely to be sustainable in the long

term as a result of being produced from edible oil feedstock. Second-generation

biodiesels produced from non-edible feedstocks have the potential to overcome this

challenge, and to serve as a more sustainable energy source in the near future.

Chemically, all biodiesels are fatty acid methyl esters (FAME), produced from raw

vegetable oil. Numerous fatty acids, ranging in chain length from 6 to 24, have been

found in various biodiesels, which are identical to their respective feedstock.

However, clear differences in chemical structure are apparent from one feedstock to

the next in terms of chain length, degree of unsaturation, number of double bonds

and double bond configuration-which all determine the important fuel properties of

biodiesel. This includes kinetic viscosity, density, cetane number, calorific value,

flash point, oxidation stability, cold temperature properties and iodine value.

Therefore, different levels of combustion performance and emission levels have been

observed in the literature when using different types of biodiesel as diesel engine

fuel. While considering production optimisation and engine durability issues, similar

trends have been observed. The literature reviewed in this study has assured that the

suitability of any biodiesel as automobile engine fuel can be explained largely

through the chemical composition of its respective feedstock.

ANN is a powerful computational modelling tool which has the ability to identify

complex relationships from input-output data. It can result in a higher level of

accuracy in its prediction ability when compared with other statistical methods.

Therefore, ANN has emerged and has found extensive acceptance in many


disciplines for modelling complex real world situations. However, most of the

literatures compared the prediction accuracy of ANNs and statistical methods based

on MSE or RMS which may not much appropriate. It is recommended to consider

some other error measurement technique including residual plots, the maximum error

percentage, minimum error percentage etc.

Recent literature shows that the complex relationship between biodiesel chemical

composition, fuel properties and diesel engine combustion performance can be

established at different operating condition conation by using ANNs. Several ANN

models have been developed to estimate the combustion-related performance of

various fuels in automobile engine applications with a high prediction accuracy, as

shown in Table 6. However, applicability of these models is limited to a specific

engine and to fuel types that have been used to collect the experimental data upon

which the network has been trained. These models also have serious limitations,

considering the limited number of engine operating parameters used in the

experiments. The automobile engine is a complex system, with a large number of

parameters directly influencing its combustion performance. Moreover, no study has

considered the physical and chemical properties of fuel while developing the ANN

model for predicting combustion performance, in spite of there being a strong

correlation between these parameters. Therefore, it would be worthwhile for

researchers to develop a universal ANN model which will be able to predict the

combustion performance of versatile automobile engines and fuel types. To ensure

the most robust ANN model, data should be used which cover as much a range as

possible. This model will able to access the sustainability of the wide ranges of

biodiesel feedstock collecting from different origin.

Chapter 3: Artificial neural network (ANN) model development 61

Chapter 3: Artificial neural network (ANN) model development

Correlation between chemical composition and properties

of biodiesel – a principal component analysis (PCA) and

artificial neural network (ANN) approach

Abstract

Biodiesel, produced from renewable feedstocks, represents a more sustainable source

of energy and will, therefore, play a significant role in providing the energy

requirements for transportation in the near future. Chemically, all biodiesels are fatty

acid methyl esters (FAME), produced from raw vegetable oil and animal fat.

However, clear differences in chemical structure are apparent from one feedstock to

the next, in terms of chain length, degree of unsaturation, number of double bonds

and double bond configuration – all of which determine the fuel properties of

biodiesel. In the present study, the sensitivity of biodiesel fuel properties was

compared against its chemical composition using experimental data. The effective

fuel properties include kinematic viscosity, density, higher heating value, oxidation

stability, cold filter plugging point temperature, flash point temperature and iodine

value. Principal component analysis (PCA) was used to understand the relationship

between important properties of biodiesel and its chemical composition. Finally,

several artificial intelligence-based models were developed to predict specific

biodiesel properties based on its chemical composition. As the relationship between

biodiesel properties and its chemical composition is complex, and there is a lack of

available knowledge to develop traditional mathematical models, a data driven

modelling technique, namely an artificial neural network (ANN), was used in this

study. The experimental study was conducted in order to generate training data for

the ANN. Available (experimental) data from the literature was also employed for

this modelling strategy. The analytical part of this study found a complex multi-

dimensional correlation between chemical composition and biodiesel properties.

62 Chapter 3: Artificial neural network (ANN) model development

Average numbers of double bonds in the chemical structure (representing the

unsaturated component in biodiesel) and the poly-unsaturated component in biodiesel

had a great impact on biodiesel properties. The simulation study demonstrated that

ANN was able to predict the relationship between biodiesel chemical composition

and fuel properties. Therefore, the ANN model developed in this study could be a

useful tool in estimating biodiesel fuel properties, instead of undertaking costly and

time consuming experimental tests.

3.1 INTRODUCTION

Vegetable oil methyl or ethyl esters, commonly referred to as biodiesel, are a

renewable liquid fuel alternative to petroleum diesel. In technical terms, biodiesel is

diesel engine fuel comprised of mono-alkyl esters of long chain fatty acids derived

from vegetable oil or animal fats (Demirbas 2008b). These mono-alkyl esters are the

main chemical species that give biodiesel similar or better fuel properties compared

with petroleum diesel (Fernando et al. 2007). It is also safer to handle, store and

transport than petroleum diesel because it is biodegradable and non-toxic. It has a

higher flash point than diesel. One of the major advantages of biodiesel is that it has

the potential to reduce dependency on imported petroleum through the use of

domestic feedstocks for fuel production (Demirbas 2008b; Fernando et al. 2007).

Biodiesels are usually made from vegetable oils and animal fat feedstocks, through a

chemical reaction called trans-esterification. In this process, the pure oil and fat is

converted from natural oil (three long chain carbon molecules struck together by

glycerine) into three mono-alkyl esters (three separated long chain carbon

molecules). Triglycerides are allowed to react with alcohol (normally methanol)

under acidic or basic catalyst conditions, producing fatty acid esters of the respective

alcohol and free glycerol. After the complete reaction, glycerol is removed as a by-

product and esters remain, which are known as biodiesel (Jahirul 2013).


Quality standards are crucial for the commercial use of any fuel product, which serve

as guidelines for the production process, to assure customers are buying fuels at the

appropriate quality, and provide authorities with approved tools for the assessment of

safety risks and environmental pollution. The fuel quality, which eventually affects

fuel combustion performance, exhaust emissions and engine durability, is more

sensitive in modern diesel engines, as the use of high pressure (about 75,000 bar) in

common rail fuel injection systems has increased (Haseeb et al. 2011a). Cetane

number (CN), widely used as a diesel fuel quality parameter, is a measure of the

combustion quality of diesel fuels during compression ignition. It is related to the

ignition delay (ID) time (i.e. the time that passes between injection of the fuel into

the cylinder and onset of ignition). The higher the CN the lower the ignition delay.

An adequate CN is required for good engine performance. A high CN helps ensure

good cold start properties and minimises the formation of white smoke. On the other

hand, lower CNs may result in diesel knocking and increased exhaust emissions

(Meher, Vidya Sagar and Naik 2006a). Kinematic viscosity (KV) is one of the engine

fuel properties that play a dominant role in the fuel spray, fuel-air mixture formation

and combustion process in diesel engine applications. It effects engine combustion,

performance and emission, especially carbon monoxide (CO) and unburnt

hydrocarbon (UHC) (Knothe and Steidley 2007). In a light-duty diesel engine, the

CO and UHC could increase by 0.02% (by volume) and 1 ppm (by volume),

respectively, by increasing 1 cSt. fuel viscosity (Ng, Ng and Gan 2012). Moreover,

high viscosity is more of a problem in cold weather, as viscosity increases with

decreasing temperature (Joshi and Pegg 2007b). On the other hand, low fuel

viscosity is not desirable, because fuel with low viscosity does not provide sufficient

lubrication for the precision fit of the fuel injection pumps, resulting in leakage or

increased wear. Therefore, all biodiesel standards defined the upper and lower limit

of fuel viscosity. Heating value is another fuel property indicating the energy content

in the fuel. Depending on the amount of oxygen contained in the biodiesel, it is

generally accepted that biodiesel from all sources has about 10% less energy content

compared with petroleum diesel. Similarly, other important fuel properties like

density, oxidation stability (OS), cold filter plugging point temperature (CFPP), flash

point temperature (FP) and iodine value (IV) also effect the combustion performance

of diesel engines and have been discussed by Jahirul et al. (2013). However, those

properties are largely determined by the complex chemical structure of biodiesel. For


instance, the KV of biodiesel increases with increasing carbon chain length in FAME

(Knothe and Steidley 2007). As the lengths of the acid and alcohol segments in the

ester molecules increase, so does the degree of random intermolecular interactions

and consequently, the kinematic viscosity. As reported in the literature (Refaat

2009a), shorter fatty acid chains with longer alcohol content in biodiesel display

lower viscosity than esters with longer fatty acid chains and shorter alcohol. Other

factors that influence biodiesel properties include number and position of the double

bonds, degree of saturation, molecular weight, branching hydroxyl groups and the

level of impurities, such as free fatty acid and unreacted glycerides or glycerol etc.

However, understanding the relationship between chemical composition and the

properties of biodiesel is not a trivial task, since the chemical structure of biodiesel is

so complex. The available mathematical models are still limited in their ability to

describe biodiesel fluid properties, in terms of their corresponding chemical

structure.

In recent years, ANN modelling techniques have increased in popularity due to their

excellent capability to learn and model complex non-linear relationships. The most

important feature of artificial neural networks is their ability to solve problems

through learning by example, without having the process knowledge. More precisely,

ANNs work like a ‘black box’ model, and they can map any relationship based on

system input and output data without knowing the detailed or complete information

about the problem. Therefore, ANNs have been successfully applied in various

disciplines, including neuroscience (Alkım, Gürbüz and Kılıç 2012), mathematical

and computational analysis (Costa, Braga and De Menezes 2012), learning systems

(Carrillo et al. 2012) and engineering design and application (Samura and Hayashi

2012; Gao et al. 2012; Minnett et al. 2011a). The application of ANN has also been

used to predict the fuel properties of biodiesel. Ramadas et. al. (Ramadhas et al.

2006a) used ANN to predict the CN of biodiesel based on fatty acid profile. ANNs

have also been used to predict viscosity, flash point and fire point, based on diesel-

biodiesel blend ratio (Kumar et al. 2007). However, those prediction models were

limited to a specific biodiesel and/or experimental conditions. No investigation has

been reported in the literature to develop an ANN model to predict biodiesel

properties for a wide range of feedstocks. In addition, the available literature have


not considered the impurities generally contained in biodiesel. Therefore, this study

aimed to develop a robust ANN model to estimate the important fuel properties of

biodiesel from its chemical composition. During the model development process, this

study also aimed to make an in-depth investigation of chemical composition and

important fuel properties of biodiesel, and to analyse the relationship between them.

3.2 DATA COLLECTION

Two types of data were used in this study: BERF and literature data. BERF data was

obtained from the experimental study of nine biodiesel samples, using the biofuel

engine research facility (BERF) testing facility. Among the samples, biodiesel

derived from canola oil (COME), cotton seed oil (CSOME), tallow (TOME),

soybean oil (SOME) and waste cooking oil methyl ester (WCO) are commercially

available. The other biodiesel samples, named C810, C1214, C1618 and C1822,

were produced by the fractionating of palm oil biodiesel produced by Proctor &

Gambel. The chemical composition, fatty acid profile and glyceride content of nine

biodiesel samples were analysed using gas chromatography-mass spectrometry (GC-

MS). Biodiesel samples were diluted 1:100 with n-hexane and 1uL samples were

injected into a PerkinElmer Clarus 580 GC-MS fitted with an Elite - 5MS, 30m x

0.25mm x 0.25um column. The split ratio was 30:1, with a column flow of 1mL/min

He. The temperature program was as follows: 120 °C initial, holding 0.5min,

ramping 10 °C/min until 310 °C, and holding for 2 min. Masses were analysed over

the range 40-350m/z. The total amount of carbon (C), oxygen (O) and hydrogen (H)

content in biodiesel was obtained through elemental analysis. In addition, acid

number and six fuel properties of biodiesel, including cetane number (CN) kinematic

viscosity (KV), density, higher heating value (HHV), were obtained through

experimental study, following recognised international standards, as shown in Table

3-1.


Table 3-1 Biodiesel property test standard

Fuel properties Unit Test Method

Element analysis (C, O, H) wt.% DIN EN 15104 Cetane value (CN) - DIN 51773 Kinematic Viscosity (KV) cSt ASTM D445

Density Kg/l ASTM D4052

Higher heating value (HHV) Mj/kg ASTM D4868

Acid number (AN) - ASTM D664

Literature data were collected from papers published in recognised international

journals, conferences and reports of renowned research centres around the world.

Scientific and electronic databases, including Elsevier, Taylor and Francis,

DieselNet, Scopus, Springer, Wiley International, American Chemical Society,

IEEE, SAGA Publication, MDPI etc., were searched for relevant papers for this

study. More than 120 papers were collected, most of which were published in the last

decade, containing experimental results of the chemical composition of biodiesel

along with corresponding fuel properties. During secondary data collection, special

care was taken to ensure the quality of the data and eliminate duplication. Data was

only taken from the literature when the experiments were conducted by the authors

themselves, following recognised international standards. Some extreme data was

excluded from database, due to the unexpected nature of the results. Data was also

eliminated from the database if it was too dissimilar compared to the fuel properties

recorded in the primary data collection results. Furthermore, the experimental results

for density and kinematic viscosity of biodiesel are highly dependent on temperature

(Joshi and Pegg 2007b; Yuan, Hansen and Zhang 2009). Although 15 ºC and 40 °C

temperatures are recommended for density and kinematic viscosity respectively,

some researchers did not mention the test temperature. Therefore, those data were

excluded from the database. Since the properties of a particular biodiesel can be

varied depending on the type of alcohol (methyl, ethyl etc.) used in the production

process, this study only considered methyl esters for inclusion in the database. The

list of papers, including feedstock use and country, are tabulated in Table 3-2. A

large number of feedstocks were investigated worldwide for biodiesel production;

including edible and non-edible vegetable oils, waste cooking oils, beef tallow,

chicken fats, fish oils, algae etc. It is also interesting to note that many investigations


used pure methyl esters in order to represent actual biodiesel, which are mostly

produced by artificial chemical processes. The most popular edible feedstock for

biodiesel investigated worldwide was soybean, followed by palm, sunflower, canola

and rapeseed oil. Among non-edible oils, the most-investigated feedstock was

Jatropha, as shown in Table 3-2.


Table 3-2: Biodiesel datasets investigated in this study

Feedstock Authors’ affiliation References

Algae USA (Do et al. 2011) Almond Iran, Nigeria (Atapour and Kariminia 2011; Giwa and Ogunbona 2014) Apricot Turkey (Gumus and Kasifoglu 2010) Babassu USA, India, Brazil (Sanford et al. 2009; Rodrigues Jr et al. 2006; Barnwal and Sharma 2005; Nogueira Jr et al. 2010) Brassica Austria (Dorado et al. 2004) Camelina USA, China, Ireland (Sanford et al. 2009; Chung 2010; Wu and Leung 2011; Fröhlich and Rice 2005; Moser and

Vaughn 2010; Soriano Jr and Narani 2012) Canola USA, Turkey, China,

Canada (Sanford et al. 2009; Albuquerque et al. 2009; Koçak, Ileri and Utlu 2007; Davis et al. 2009; Hu et al. 2005; Haagenson et al. 2010; Chhetri and Watts 2012a; Moser 2008; Chhetri and Watts 2012b; Kinast 2003b; Do et al. 2011; Duncan et al. 2010; Cecrle et al. 2012)

Coconut Philippine, USA, India, Thailand, Brazil

(Sanford et al. 2009; Alleman and McCormick 2006; Rodrigues Jr et al. 2006; Tan, Culaba and Purvis 2004; Kumar et al. 2010; Nakpong and Wootthikanokkhan 2010; Duncan et al. 2010; Cecrle et al. 2012; Feitosa et al. 2010)

Coffee Greece, Japan (Deligiannis et al. 2011; Todaka et al. 2013) Corn Brazil, USA, Romania (Lin, Huang and Huang 2009; Rodrigues Jr et al. 2006; Dantas et al. 2011; Serdari et al. 1998;

Cursaru, Neagu and Bogatu 2013) Cottonseeds Brazil, Greek, USA (Albuquerque et al. 2009; Royon et al. 2007; Rashid, Anwar and Knothe 2009; Demirbaş 2002;

Tang, Salley and Simon Ng 2008; Nogueira Jr et al. 2010) Fish oil Taiwan, Chile, Turkey (Lin and Li 2009a; Reyes and Sepulveda 2006; Behçet 2011) Golden cress Egypt (Ali 2013) Grape Spain, Romania (Ramos et al. 2009; Cursaru, Neagu and Bogatu 2013) Hazelnut Turkey (Koçak, Ileri and Utlu 2007; Moser 2012; Demirbaş 2002) Hepar USA (Sanford et al. 2009) Jathropa USA, India, Canada,

South Africa, China, Japan

(Sanford et al. 2009; Sarin et al. 2007; Choudhury and Bose 2008; Jain and Sharma 2012; Chhetri et al. 2008; Singh and Padhi 2009; Aransiola et al. 2012; WANG et al. 2012; Chhetri and Watts 2012a, 2012b; Kumar Tiwari, Kumar and Raheman 2007; Todaka et al. 2013)


Lard Portugal, Korea (South), Spain, USA

(Lee, Foglia and Chang 2002; Dias, Alvim-Ferraz and Almeida 2009; Berrios et al. 2009; Kinast 2003b; Wyatt et al. 2005)

Linseed Brazil, Lithuania, India, Egypt

(Rodrigues Jr et al. 2006; Guzatto, De Martini and Samios 2011; Samios et al. 2009a; Lebedevas et al. 2006; Puhan et al. 2009; El Diwani and El Rafie 2008; Radha and Manikandan 2011)

Mahua India, Turkey (Ghadge and Raheman 2005; Godiganur, Suryanarayana Murthy and Reddy 2009; Kapilan and Reddy 2008; Demirbas 2009a)

Mustard Turkey, Brazil, USA (Bannikov 2011; Jham et al. 2009) Neem India, South Africa,

Pakistan (Ragit et al. 2011; Aransiola et al. 2012; Sivalakshmi and Balusamy 2012; Sardar et al. 2011; Radha and Manikandan 2011)

Olive Spain, Greece, USA, Romania

(Ramos et al. 2009; Dorado et al. 2003; Kalligeros et al. 2003; Cecrle et al. 2012; Cursaru, Neagu and Bogatu 2013)

Palm Malaysia, Indonesia, Greece, Colombia, Japan, Spain, India, USA, Canada, Romania

(Karavalakis, Stournas and Bakeas 2009; Sarin et al. 2007; Kousoulidou et al. 2010; Ramos et al. 2009; Benjumea, Agudelo and Agudelo 2008; Kalam and Masjuki 2002a; Ng and Gan 2010; Loh, Chew and Choo 2006; Crabbe et al. 2001; Kalam and Masjuki 2002b; Pérez et al. 2010; Barnwal and Sharma 2005; Moser 2008; Do et al. 2011; Vedaraman et al. 2011; Cecrle et al. 2012; Park et al. 2008; Cursaru, Neagu and Bogatu 2013)

Peanut USA, Spain, Turkey, India, China, Romania

(Lin, Huang and Huang 2009; Ramos et al. 2009; Moser 2012; Pérez et al. 2010; Davis et al. 2009; Kaya et al. 2009; Barnwal and Sharma 2005; SUN et al. 2008; Cursaru, Neagu and Bogatu 2013)

Popyseed Turkey (Demirbaş 2002) Rapeseed USA, Greece,

Lithuania, Turkey, Spain

(Karavalakis, Stournas and Bakeas 2009; Wu 2008; Lin, Huang and Huang 2009; Senatore et al. 2000; Rashid and Anwar 2008a; Sahoo et al. 2007; Ramos et al. 2009; Fröhlich and Rice 2005; Lebedevas et al. 2006; Demirbaş 2002; Pérez et al. 2010; Cecrle et al. 2012; Park et al. 2008; Todaka et al. 2013)

Rice barn India (Sinha, Agarwal and Garg 2008) Rubberseed India (Ramadhas, Jayaraj and Muraleedharan 2005; Ikwuagwu, Ononogbu and Njoku 2000) Safflower USA (Rashid and Anwar 2008a; Demirbaş 2002) Sesame Nigeria, Pakistan (Betiku and Adepoju 2013; Ahmad, Ullah, et al. 2011) Soybean USA, Brazil, Spain, (Ali, Hanna and Cuppett 1995b; Wu 2008; Armas, Yehliu and Boehman 2010; Albuquerque et al.


India, Turkey, China 2009; Sarin et al. 2007; Lin, Huang and Huang 2009; Ali, Hanna and Cuppett 1995a; Rodrigues Jr et al. 2006; Ramos et al. 2009; Guzatto, De Martini and Samios 2011; Canakci and Van Gerpen 2003; Alcantara et al. 2000; Schwab, Bagby and Freedman 1987; Pérez et al. 2010; Davis et al. 2009; Barnwal and Sharma 2005; Moser 2008; Kinast 2003b; Duncan et al. 2010; Candeia et al. 2009; Cecrle et al. 2012; Pereira et al. 2007; Tang, Salley and Simon Ng 2008; Wyatt et al. 2005; Canakci 2005a; Feitosa et al. 2010; Qi et al. 2009; Moraes et al. 2008; Nogueira Jr et al. 2010; Park et al. 2008; Shah et al. 2013)

Soapnut Canada (Chhetri and Watts 2012a, 2012b) Sunflower Greek, Turkey, Spain,

India, Greece, USA, Pakistan, Romania

(Sarin et al. 2007; Lin, Huang and Huang 2009; Royon et al. 2007; Serdari et al. 1998; Ramos et al. 2009; El Diwani and El Rafie 2008; Demirbaş 2002; Pérez et al. 2010; Barnwal and Sharma 2005; Kalligeros et al. 2003; Antolın et al. 2002; Moser 2008; Rashid et al. 2008; Cursaru, Neagu and Bogatu 2013)

Tall Turkey (Altıparmak et al. 2007) Tallow USA, Lithuania, Spain,

India, Turkey, Brazil, Japan

(Ali, Hanna and Cuppett 1995b; Sanford et al. 2009; Ali, Hanna and Cuppett 1995a; Lebedevas et al. 2006; Alcantara et al. 2000; Barnwal and Sharma 2005; Öner and Altun 2009; Ramalho et al. 2012; Kinast 2003b; Tang, Salley and Simon Ng 2008; Wyatt et al. 2005; Moraes et al. 2008)

Terebinth Turkey (Özcanlı, Keskin and Aydın 2011) Terminalia Brazil (Dos Santos et al. 2008) Turnip USA (Shah et al. 2013) Walnut USA (Moser 2012) Waste cooking Vietnam, Spain,

Taiwan, Turkey, Brazil, Spain, USA

(Lin, Huang and Huang 2009; Phan and Phan 2008; Encinar, Gonzalez and Rodríguez-Reinares 2005; Lin and Li 2009a; Koçak, Ileri and Utlu 2007; Guzatto, De Martini and Samios 2011; Lapuerta et al. 2008; Alcantara et al. 2000; Demirbas 2009b; Chhetri, Watts and Islam 2008; Cecrle et al. 2012)

Yellow grease USA (Canakci and Van Gerpen 2003; Kinast 2003b) Pure methyl ester

USA (Rodrigues Jr et al. 2006; Knothe 2005; Knothe and Steidley 2005; Knothe 2008; Moser 2011)


Table 3-3: Structural formulae for fatty acids methyl ester found in biodiesel samples

Acid chain C:N* Type* CL*** Chemical structure Caprilic C8:0 SFA 8 CH3(CH2)6COOH Capric C10:0 SFA 10 CH3(CH2)8COOH Lauric C12:0 SFA 12 CH3(CH2)10COOH Myristic C14:0 SFA 14 CH3(CH2)12COOH Palmitic C16:0 SFA 16 CH3(CH2)14COOH Palmitoilic C16:1 MUFA 16 CH3(CH2)6 CH=CH (CH2)6 COOH Stearic C18:0 SFA 18 CH3(CH2)16COOH Oleic C18:1 MUFA 18 CH3(CH2)7 CH=CH (CH2)7 COOH Linoleic C18:2 PUFA 18 CH3(CH2)4 CH= CHCH2CH =CH (CH2)7 COOH Linolenic C18:3 PUFA 18 CH3(CH2)2CH=CHCH2CH=CHCH2CH

=CH(CH2)7 COOH Gondonic C20:1 MUFA 20 CH3(CH2)7 CH=CH (CH2)9 COOH Erucic C22:1 MUFA 22 CH3(CH2)7 CH=CH (CH2)11 COOH * C: the number of carbon atoms and N: the number of double bonds of carbon atoms in the fatty acid chain. ** SAF: saturated fatty acids, MUFA: Mono unsaturated fatty acids and PUFA: Poly unsaturated fatty acid. *** CL: Chain length of hydrocarbon of respective methyl ester

3.3 RESULTS AND DISCUSSION

3.3.1 Chemical composition

The main chemical components of biodiesel are mono-alkyl esters of fatty acids,

which are feedstock dependent and vary significantly from feedstock to feedstock.

Therefore, a wide range of fatty acid profiles were found in the collected data.

Among those, most commonly found FAMEs are tabulated in Table 3-3, together

with their structure and name. These FAMEs are straight-chain compounds ranging

in size from 8−22 carbons, which are mainly one of three types: saturated, mono-

unsaturated and poly-unsaturated. In the saturated acid, no hydrogen can be added

chemically, and they contain only single bonds, whereas in mono-unsaturated fatty

acids, one hydrogen can be added and it contains one double bond. Similarly, in

poly-unsaturated fatty acids, more than one hydrogen can be added and it contains

multiple double bonds. In general, fatty acids are designated by two numbers: the

first number denotes the total number of carbon atoms in the fatty acid and the


second is the number of double bonds. For example, 14:1 designates Myristoleic acid

which has 14 carbon atoms and one double bond.

Table 3-4 shows the chemical composition of nine biodiesel tested in this study. The

C810, C1214 and C1416 samples were mostly saturated FAMEs of different chain

lengths. C1822 contained very high unsaturated fatty acid methyl, which was over

90% by weight. Although TOME contained about 50% unsaturated fatty acids, most

those were mono-unsaturated fatty acid methyl esters which accounted for 45.1% of

total fatty acids. The average chain length (ACL) of C810 was 8.93; which was the

lowest among the samples tested in this study. CSOME and C1875 had the highest

ACL among the samples tested, being 17.93. CSOME and SOME were rich in

PUFAs, containing 55.4% and 49.38 % by weight, respectively. The oxygen content

of the samples was found to be between 10-11% by weight, except for samples C810

and C1214, which contained 18.72% and 13.31% by weight, respectively. The

average number of double bonds (ANDB) in the samples ranged from 0 to 1.48. The

COME contained more than 1% mono-glycerides, whereas other samples were found

to have very low levels, ranging from 0% to 0.58% by weight. Acid number

(representing the free fatty acid content) was found to range from 0.22 to 2.69.

Overall, the results obtained in this study showed a wide range of chemical

compositions, which were useful for building a robust model.

Table 3-4: Chemical composition of tested biodiesel

Biodiesels COME CSOME TOME SOME WCO C810 C1214 C1618 C1822

FAME C8:0 0 0 0 0 0 52.16 0 0 0 C10:0 0 0 0.36 0 0 46.38 0.17 0 0 C12:0 0.31 0 0.36 0 0 1.38 47.8 0.1 0 C14:0 0.35 0.53 3.07 0 0 0 18.89 0.06 0.03 C16:0 11.93 2.51 26.62 11.08 11.23 0 10.19 21 4.45 C16:1 0.22 0.55 3.13 0.18 0 0 0 0 0.12 C18:0 2.43 4.13 19.89 5.23 3.5 0 2.55 9.47 2.53 C18:1 55.72 35.78 41.66 33.82 67.03 0 18.53 58.72 71.04 C18:2 25.87 55.04 2.57 48.38 18.3 0 1.76 9.98 18.69 C18:3 0.96 0.59 0 1.22 0 0 0 0 0 C20:0 0 0.31 0 0 0 0 0 0 0 C20:1 0.57 0.30 0.31 0 0 0 0 0.24 1.03 C22:1 0.21 0.01 0 0.24 0 0 0 0 0 Mono-glyceride 1.03 0.58 0.09 0 0 0.08 0 0 0 Acid number (AN) 0.91 0.22 2.31 0.34 1.4 0.91 2.69 1.04 1.48


Oxygen (O, wt.%) 10.81 10.80 10.97 10.95 10.93 18.72 13.31 10.97 10.63 Hydrogen (H,wt.%) 11.95 11.81 12.19 11.98 12.17 11.69 12.30 12.25 11.87 Carbon (C, wt.%)% 75.86 76.55 74.81 77.00 76.95 69.51 74.23 76.35 75.39 Ave. chain length (ACL)

17.74 17.93 17.22 17.78 17.78 8.98 14.15 17.57 17.93

Ave. number of double bond (ANDB)

1.12 1.48 0.51 1.33 1.04 0.00 0.22 0.79 1.12

Saturation (wt%) 16.41 8.32 52.33 16.38 14.67 100 79.71 31.06 9.12 MUFA* (wt.%) 56.72 36.64 45.10 34.24 67.03 0.00 18.53 58.96 72.19 PUFA* (wt.%) 26.87 55.04 2.57 49.38 18.30 0.00 1.76 9.98 18.69

Table 3-5: BREF experimental results of biodiesel properties

Fuel properties

Diesel COME CSOME TOME SOME WCO C810 C1214 C1618 C1822

Higher heating value (MJ/kg)

45.93 38.8 38.42 38.2 39.4 39.75 35.34 38.44 37.59 39.83

Kinematic Viscosity (cSt)

2.64 5.45 4.08 4.52 3.86 4.82 1.95 4.37 4.95 5.29

Density (kg/l) 0.838 0.898 0.893 0.888 0.893 0.890 0.867 0.875 0.879 0.889 Cetane value 50.6 - 87 - - 58.6 42 75.3 70 59.2

The most-common fatty acids found in biodiesel samples were: Oleic (C18:1)

followed by Stearic (18:0), Linoleic (C18:2), Palmitic (C16:0) and Linolenic (C18:3)

acid esters. Figure 3-1 shows that these fatty acid esters were found in almost every

biodiesel sample. It is interesting to note in Figure 4 that the Oleic (C18:1) and

Linoleic (C18:2) acid esters were not only represented in most of the biodiesel

samples, but they also showed highest average weight percentage in the biodiesel

samples, which were about 40% and 32%, respectively. On the contrary, an average

of 7.5% and 6.5% Linolenic (C18:3) and Stearic (18:0) acids methyl esters were

present in the samples. This also reflects the average values for chain length,

saturated fatty acid esters, mono-unsaturated esters and poly-unsaturated methyl

esters, as shown in Table 3-3. Apart from fatty acid methyl esters, other common

chemicals found in the biodiesel were unreacted mono-glyceride and free fatty acid,

represented as the acid value.


Figure 3-1: Number and average weight in percentages of fatty acid methyl esters

found in the samples

3.3.2 Fuel properties

Fuel properties of the tested biodiesels, in terms of higher heating value (HHV),

kinematic viscosity (KV), density and cetane number (CN), are summarised in the

Table 3-6. The properties of petroleum diesel are also shown in the Table 3-5 for

comparison purposes. Similar to chemical composition, clear differences in fuel

properties were found between biodiesels in the experimental results. Reflecting its

exceptional chemical composition, C810 showed the lowest values for all properties

compared with other biodiesels investigated. Other fractionated methyl esters

showed similar properties to commercial biodiesels. The HHV of biodiesel was

found to be just below 40 Mj/kg, which is about 10% lower than that of petroleum

diesel. This is mainly due to the oxygen content in the chemical structure of its

FAMEs (Demirbas 2008b; Refaat 2009a). The KV, density and CN of these

biodiesels were found to be much higher than petroleum diesel, except for C810. The

KV of commercial biodiesels was found to be double than that of petroleum diesel.

The highest KV was found for COME (5.45 cSt.) followed by C1822, C1618 and

CSOME. The density of biodiesels was found to be an insensitive parameter among


the biodiesels tested, however the commercial biodiesels showed a slightly higher

density compared with commercial biodiesel. The difference in biodiesel CN among

the biodiesel samples investigated in this study was very noticeable, ranging from 42

to 87. CSOME, C1214 and C1618 showed very high CN compared to petroleum

diesel. The CN of C810 was found to be 42, which was much lower than petroleum

diesels.

A good amount of quality data are required for the purpose of a correlation study and

robust model development, with the accuracy of the ANN model found to increase

with a greater number of data sets. Therefore, secondary data were collected from

published peer reviewed literature, as summarised in Table 4. Among the eight

biodiesel properties identified during secondary data collection, four of those

(including oxidation stability (OS), cold filter plugging point (CFPP), flash point

(FP) and iodine value) were not investigated experimentally, due to a lack of

laboratory facilities. A wide variety of biodiesel types were investigated in the

literature (as discussed in the previous section) and variation was also observed in

terms of the number of biodiesel properties examined, with no single work reporting

on all of the important fuel parameters. The most commonly studied biodiesel

properties were KV, Density and HHV. Overall, we were able to collect about 350

data sets for each of these properties, along with the corresponding chemical

composition. On the other hand, flash point and oxidation stability were the least

studied parameter, consisting of less than 200 data sets.

The average fuel properties reported in the collected secondary data were found to be

within the limits of European (EU), American (US) and Australian (AU) biodiesel

standards, except for OS. The average OS was found to be 4.73 hr, which is much

lower than the minimum OS requirement (6 hr minimum) of EU and AU biodiesel

standards. These results indicate that a vast number of the investigated biodiesels

were unlikely to fulfil EU biodiesel standards, and this is may be one of the major

issues that restricts the wide spread use of biodiesel in conventional diesel engines.

The biodiesels that showed poor OS and were rich in unsaturated FAME include

soybean, sunflower, safflower, corn, cottonseeds, linseeds, jatropha and camelina.

European and Australian biodiesel standards also impose tight restrictions on


biodiesels kinematic viscosity, limiting it to a minimum of 3.5 and a maximum of 5

cSt. However, the KV of biodiesels in the secondary data ranged from 0.99 to 7.21

cSt, which means that many of them would be unlikely to meet EU and AU biodiesel

standards in terms of KV. US biodiesel standards are more lax in terms of KV (1.9 –

6 cSt), however they place tighter restrictions on CN and FP, which means many

biodiesels identified in the secondary dataset would still be unlikely to meet US

standards. Overall, most of the maximum and minimum values for fuel properties

were outside the range of biodiesel standards, demonstrating the significant level of

variation the in secondary data collected. This was not unexpected, because the

secondary data was collected from a large number of different biodiesels with a wide

variety of chemical structures. Therefore, the collected secondary data were useful

for conducting an in-depth correlation study and developing the artificial neural

network model.

Table 3-6: Summary of the secondary data for biodiesel properties

Properties

Biodiesel standard

N* Max.* Min.* Ave.* ASTM D7651

EN 14214

Australian

Cetane number 47, min 51, min 51, min 227 86 37 54.59

Kinematic viscosity (cSt.) 1.9-6 3.5-5 3.5-5 368 6 1.97 4.42

Density (kg/l) n/a 0.86-0.90 0.86 -0.90 345 0.924 0.829 0.876

Higher heating value (Mj/kg) n/a n/a n/a 336 45.5 35.86 39.91

Oxidation stability (hrs) 3, min 6, min 6, min 198 11.4 0.2 4.73

Cold filter plugging point (°C) Report Report Report 254 17 -20 -1.11

Flash point (°C) 130, min 120, min 120, min 187 191 96 159.83

Iodine value (g iod/100 g) n/a 120, max n/a 214 184.5 0.3 96.1

*N =Number of data sets; Max = Maximum value; Min. = Minimum value; Ave. = Average value


(a)

(b)

Figure 3-2: Correlation of (a) C18:2 with oxidation stability; (b) H2 with CN


3.3.3 Correlation of chemical composition and fuel properties

Due to variations in chemical composition, the fuel properties of biodiesel

significantly differ from one another. Linoleic (C18:2) and linolenic (C18:3) fatty

acid methyl esters are not only present in most biodiesel, but also influence almost all

of the fuel properties. For instance, OS stability and CFPP decrease significantly

with an increase in both C18:2 and C18:3. Figure 3-2a shows that biodiesels with

C18:2 are more than 40% more likely to fail to meet the lower limit of Australian

and European biodiesel standards and over 60 also failed to meet the US biodiesel

standard. However, those fatty acid methyl esters do not have much effect on KV

and FP. The stearic acid methyl ester (C18:0), which is also found frequently in

biodiesel, has an influence on all fuel properties except HHV and OS. Other

individual fatty acid methyl esters investigated in this study may not have an effect

on all fuel properties, but they have some influence in certain numbers. For example,

short chain methyl esters such as caprilic (C8:0) and capric (C10:0) acid methyl

esters were found to only correlate with KV, HHV and FP, while the palmitoilic acid

methyl ester (C16:1) was unlikely to correlate with any of the biodiesel properties

investigated in this study.

The influence of the weight percentage of oxygen (O2), hydrogen (H2) and carbon

(C) content on fuel properties was also observed and it was found that CN was highly

influenced by biodiesel H2 content, as shown in Figure 3-2b, whereas O2 and C were

not correlated with biodiesel CN. On the hand, the correlation of O2 and C with KV

was much higher than that of H2. It is interesting to see that the carbon content of

biodiesel only affected the kinematic viscosity and density of the fuel.

Compared to individual chemical components, certain chemical characteristics

seemed to have a greater influence on biodiesel properties. For example, the average

number of double bonds (ANDB) in the biodiesel (which indicates the concentration

of unsaturated fatty acid methyl esters) was found to be very influential, affecting all

biodiesel properties investigated in this study. It had a strong negative correlation

with CN and a positive correlation with IV, as shown in Figure 3-3.


(a)

(b)

Figure 3-3: Correlation of (a) ANDB with CN; (b) ANBD with IV


This figure indicates that the biodiesel with an ANDB greater than two is likely to

fail to meet the lower limit of all biodiesel standards in terms of CN and IV. Further,

the OS of biodiesel decreased rapidly with the increase in ANDB, because a higher

number of double bonds in the fatty acid chain make it much more susceptible to

oxidation. ANDB also has a strong negative correlation with biodiesel CFPP and a

moderate positive correlation with density, HHV and FP.

As described earlier in this section, the fatty acid methyl esters found in biodiesel can

be divided into three categories. These include saturated, mono-unsaturated (MUFA)

and poly-unsaturated (PUFA) fatty acid methyl esters. This study found that PUFA’s

had the greatest influence on biodiesel properties, affecting almost all of the fuel

properties investigated in this study, including CN, density, OS, CFPP and IV. On

the other hand, MUFA’s only impacted greatly on biodiesel KV. Moreover, fuel

properties such as KV, OS and IV were found to have a high correlation with

saturated compounds.

The average chain length (ACL) was correlated with all of the fuel properties except

CFPP. It had a very strong correlation with KV, as shown in Figure 3-4a. This is

mainly due to the increase in carbon content, as well as random inter molecular

interaction in the FAME, which consequently increases the KV. For the same reason,

ACL were also found to have a strong correlation with calorific value (Figure 3-4b)

and flash point temperature. Biodiesels with an ACL less than 14 were unlikely to

meet the lower limit of both US and EU standards. On the other hand, biodiesel with

a very high ACL (over 19) are more likely to exceed the upper limit of biodiesel

standards in terms of KV.

Apart from fatty acid methyl ester compounds, mono-glyceride and acid number

(representing the amount of free fatty acid) also had some correlation with a number

of biodiesel properties. For example, biodiesel density had a strong inverse

correlation with acid number and a comparatively less strong correlation with mono-

glyceride. The acid number also had some influence the HHV and FP, whereas

mono-glyceride affected KV and IV. Overall the presence of mono-glyceride and

free fatty acid content in biodiesel was not found to correlate with any biodiesel


properties. This may be due to its relatively small concentration (less than 0.5% on

average) in the tested biodiesel.

(a)

(b)

Figure 3-4: Effect of ACL on biodiesel (a) kinematic viscosity (KV) and (b) higher

heating value (HHV)


3.3.4 Principle component analysis

The findings of the correlation study reported in the previous section indicate a

complex relationship between biodiesel quality and its chemical composition. A

particular fuel property does not depend on a single chemical parameter, rather it is

influenced by multiple parameters and factors. Therefore, multivariate data analysis

is required in order to gain a detailed understanding of this relationship. Principle

component analysis (PCA) is one of the popular multivariate data analysis techniques

used by almost all scientific disciplines. PCA is used to analyse data sets with highly

inter-correlated dependent variables. It reduces the complexity and dimensionality of

the problem, thereby extracting the most important information and analysing the

structure of the observations and variables. PCA changes the input variables into

principal components (PCs) that are an independent and linear combination of input

variables. PCA also represents patterns in the observations and variables by

displaying them as points on a diagram. In this study, PCA analysis was conducted in

order to observe the influence of chemical composition on individual fatty acid

compositions. The variables used for the principle components were individual fatty

acid methyl esters chain length ranging from 8 to 22, while the interaction terms

included average chain length (ACL), average number of double bonds (ANDB), and

weight percentages of oxygen (O), hydrogen (H), carbon (C), saturated fatty acids,

MUFA and PUFA. The variables also included the most commonly found impurities

in biodiesel, namely mono-glyceride and acid number (AN). In general, variables

which lie close to (±45°) an observation are correlated, those lying in opposite

directions (135–225°) are anti-correlated, and those lying in an orthogonal direction

have less or no influence. The direction and length of the variables is indicative of

their influence on the observation, with a short length indicative of little influence.

The results of eight fuel properties are graphically shows in Figure 3-5.


(a) (b)

(c) (d)

(e) (f)

(f)


(g) (h)

Figure 3-5: Principle component analysis and correlation of biodiesel properties with

chemical composition:

(a) Cetane number (CN); (b) Kinematic viscosity (KV); (c) Density; (d) Higher

heating value (HHV); (e) Oxidation stability; (f) Cold filter plugging pint temp (CFPP); (g) Flash point temperature; (h) Iodine value (IV)

3.3.5 ANN model development

In the present work, several ANN models were developed to predict the biodiesel

properties from corresponding chemical composition. ANN represents a

mathematical relationship between input and output parameters of the system, like a

black box model. Figure 3-6 illustrates how several stages are involved in the ANN

model development process. The first step is pre-processing the training data,

whereby the raw data are rearranged according to the input-output structure of the

ANN. As the input ranges for ANN vary from one input to another, the rearranged

data are then rescaled into the range -1 to 1, and the data is spilt into training,

validation and testing data sets.

The second step involves setting the structure of the ANN, including the number of

hidden layers and hidden neurons. In this work a set of ANNs were developed where

each ANN predicted only one biodiesel parameter, which means that the ANN

predicts a single output based on several inputs. According to the literature, an ANN

(h)


with a single hidden layer is sufficient to generate single output-based model

properties (Tamura and Tateishi 1997; Hosen et al. 2014). Therefore, a single hidden

layer was used in this study and different hidden neuron sizes were used in the

training process, in order to diversify the structure of the ANNs. Finally, the best

ANN (whatever the neuron size), in terms of their performance index, was selected

for further study.

Figure 3-6: Proposed flow chart of ANN prediction model development

In order to initiate the training process, the initial training parameters and weights for

the ANN are assigned randomly, while after training, the weights are determined

85based on input and output data. When a satisfactory level of performance is

reached, the training stops and the network uses the optimal weights to make

decisions. Finally, the performance of the ANN was evaluated (in terms of


performance index) using the testing data set and the best ANN structure (in terms of

hidden neurons) was selected as final the ANN model.

The selection of input parameters (chemical composition of biodiesel) for ANN is

crucial in order to predict the parameters which reflect biodiesel properties. It is also

desirable to minimise the number of input parameter for an ANN system, in order to

reduce the computational time. In general, the best input parameters are selected

based on an understanding of the physics of the problem. In this study, the effective

parameters for chemical composition were used as input variables for the ANN,

whereas the single parameter of fuel properties was used as the output. In order to

avoid the repetitions of similar figures, a combine diagram representing eight ANN

models is shown in Figure 3-7.

Figure 3-7: Structure of ANN


Based on the correlation studies detailed in the previous section, eight parameters

were selected as biodiesel properties. The results of the correlation analysis are

presented in Figure 3-8. This figure shows that individual properties of biodiesels are

correlated with a certain number of chemical composition parameters, and, hence,

the effective inputs (from all parameters of chemical composition) for ANN were

selected as per Figure 3-10, in order to predict the corresponding parameter of fuel

properties. For an example, 14 parameters for chemical composition had a significant

influence on biodiesel oxidation stability, as shown in Figure 3-10. Therefore, these

14 parameters were selected as inputs during training of the ANN model for OS.

Variables CN KV Density HHV OS CFPP FP IV

C8:0

C10:0 C12:0 C14:0 C16:0 C16:1

C18:0 C18:1

C18:2 C18:3 C20:1 C22:1

O%

H%

C%

ACL

ANDB Saturation MUFA

PUFA Monogly AN

Figure 3-8: Analysis of influence between chemical composition and fuel properties


After selecting the input-output data for ANN training, the data were randomly split

into training, validation and testing data, as follows:

70% of total data were used for training data

10% of total data were used for validation data, and

20% of total data were used for testing data.

A single hidden layer was used for all ANNs. Different hidden neurons (6:2:30) were

used to train the ANNs, since ANN performance is sensitive to the size of hidden

neurons. Table 3-9 depicts the optimum number of hidden neurons for generating the

best ANN for estimating the corresponding parameters for biodiesel properties. Table

3-7 also shows the total number of inputs for corresponding biodiesel properties. In

this study, a logistic sigmoid (logsig) transfer function was used for the hidden layer,

while a purelinear (purelin) transfer function was used for the output layer. The

codes for the ANN training models are shown in Appendix A.

Table 3-7: Number of input variables and optimised number of neuron in ANN model

Properties Input variable

Neuron number

CN 11 20 KV 16 24 Density 14 23 HHV 13 18 OS 14 25 CFPP 11 18 FP 11 15 IV 11 20

3.3.6 Evaluation of ANN model performance

After the successful training of the network, the network was tested using the test

data set. Based on the results produced by the network, statistical methods were used

to investigate the prediction performance of the ANN results. To judge the prediction


performance of the ANN, several performance measures were used, including

statistical analysis in terms of absolute fraction of variance (R2), root mean squared

(RMS) and maximum average error percentage (MAEP). Formulas to calculate the

error parameters are shown in Equation 3-1 to 3-3.

2

1

2

12

)(1

NI

IMa

pa

Ni

i

EE

EER

Equation 3-1

N

EERMS

Ni

ipa

1

2

Equation 3-2

Ni

i a

pa

E

EE

1

100N

1 MEP

Equation 3-3

Where, Ea-Actual result; Ep-Predicted result; Em-Mean value of terget; N-Number of

sample size

The results of ANN model testing for biodiesel properties are shown in Figure 3-9.

Following statistical analysis, it was found that the absolute fraction of variance (R2)

was close to unity, ranging from 0.8932 to 0.9622, with Root-Mean-Squared (RMS)

errors ranging from 0.011 to 4.171 and maximum average error percentage (MAEP)

ranging from 1.86% to 5.53%. This variation in model performance may be due to

the quantity and quality of data, as well as the complexity of the correlation of input

and output variables. The best estimation accuracy was found for CN and IV, which

might be because these two parameters were correlated with fewer parameters, which

reduced the complexity of the system. The least accurate ANN model was found for

OS, due to highly complex correlation with input variables, and the smaller number

of data sets used during the training process. Overall, it can be seen that the ANN

was able to generate relationship between the measured and predicted fuel properties

of biodiesel. Therefore, the developed ANN models were trained well and can be

used to simulate fuel properties for a wide range of biodiesel fuels. However, the

prediction accuracy of the model should be further improved by increasing the

number and range of the training data sets.


(a) (b)

(c) (d)

(e) (f)


(g) (h)

Figure 3-9: Biodiesel properties estimation accuracy of developed ANN models

(a) Cetane number (CN); (b) Kinematic viscosity (KV); (c) Density; (d) Higher

heating value (HHV); (e) Oxidation stability; (f) Cold filter plugging pint temp (CFPP); (g) Flash point temperature; (h) Iodine number (IN)

3.4 CONCLUSION

The aim of this paper was to investigate the correlation between important fuel

properties of biodiesel, as well as the ability of artificial neural networks (ANN) to

predict the important fuel properties of biodiesel based on its chemical composition.

Experiments were conducted on nine different biodiesel feedstocks, with a wide

range of chemical compositions. The data were further improved by collecting

experimental secondary data collected from over 120 published literatures. The fuel

properties investigated in this study were cetane number, kinematic viscosity,

density, higher heating value, oxidation stability, cold filter plugging pint temp, flash

point temperature and iodine value. Correlation of individual fuel properties and

chemical composition were investigated using principle component analysis and

based on the graphical representation of the results, a complex relationship was

found between chemical composition and biodiesel properties. The most influential

chemical composition parameters, which affected all biodiesel properties in this

study, were the poly-unsaturated fatty fraction and average number of double bonds

present in the biodiesels. Using the data obtained from experiments and the literature,

standard back propagation (BP) neural network models with LM algorithms were


developed for individual biodiesel properties. The parameters obtained from the

chemical compositions of biodiesel where were used as input variables and fuel

properties were used as output variables during training of the network. The input

variables were selected based on the correlation results obtained for individual

biodiesel properties. The performance of the developed ANN prediction models were

tested and it was found that the absolute fraction of variance (R2) was close to unity,

ranging from 0.8932 to 0.9622, with Root-Mean-Squared (RMS) errors ranging from

0.011 to 4.171 and maximum average error percentages (MAEP) ranging from 1.86%

to 5.53%. The results of this study also show that the ANN has the ability to learn

and generalise a wide range of experimental conditions. Therefore, the use of ANNs

may be recommended, in order to optimise the chemical composition of biodiesels,

as well as the fuel quality for internal combustion engine application. However, the

network should be further improved by including additional robust data sets during

the training process.

Chapter 4: Biodiesel from Australian native plants 93

Chapter 4: Biodiesel from Australian native plants

4.1 INTRODUCTION

Australia has vast areas of grazing (cleared) and degraded (mined) land on which

plants for biodiesel feedstock can be grown, including a large number of native

species containing non-edible oil in fruits, which have already been assessed for

growth on degraded land in Australia (Ashwath 2010b). However, for any plant to be

deemed a viable candidate for large scale production as a biodiesel fuel source,

several issues must be considered, including: cultivation requirements, such as the

extent of irrigation or drainage systems; having a tolerance to a range of

environments and soil types; and matching the ecology of available or desired areas;

as well as propagation, planting, weeding, fertilising, trimming/pruning and growth

rates. Aside from environmental and cultivation requirements, the potential oil yield

per year per hectare of land, along with difficulties in the extraction of oil from the

seeds or fruits also need to be considered, as does the quality of biodiesel produced

from these native species. This work investigated the suitability of 11 native

Australian plants for the production of second-generation biodiesel. The plants were

selected by consulting with plant scientist Assoc. Prof. Nanjappa Ashwath, Centre

for Plant and Water Sciences (CPWS), Central Queensland University, and a brief

description of the selected plant is presented below. The seeds of native plants were

collected from local seed suppliers in Queensland, Australia. These seeds were

ground-dried (i.e. had been on the ground for some time prior to collection) and

mostly collected from coastal locations in Northern Queensland.

94 Chapter 4: Biodiesel from Australian native plants

4.2 POTENTIAL NATIVE OIL SEED PLANTS

A large number non-edible vegetable oil crops grow naturally across the vast land

area of Australia, which can be a valuable source for future generation biodiesel

production. Important features of the species investigated in this study are described

in following sections.

4.2.1 Beauty leaf (Calophyllum inophyllum)

Calophyllum Inophyllum, more commonly known as Beauty leaf, is a moderate sized

tree that grows between 8–20 m tall and is most notable for its decorative leaves and

fragrant flowers, as can be seen in Figure 4-1. The tree grows in tropical and sub-

tropical climates (typical temperature range of 18–33oC) close to sea level. Beauty

leaf trees grow in free draining soils near shorelines, however, it has been observed

growing in various clay soils within Australia (Hyland et al. 2003). Beauty leaf can

be transplanted from nurseries very successfully, although for the purpose of

cultivation, it is more beneficial to grow the trees from direct seeding. It is a

moderately fast growing tree that can grow up to 1 m tall within a year. It has also

been seen to flourish in the presence of weeds and other species, so the plant can be

grown in mixed cultures and weeding is not necessary (Mohibbe Azam, Waris and

Nahar 2005a). The tree bears fruit twice a year, the timing of which depends on when

the tree was planted, as it is an evergreen. With two yields per year, a healthy tree

produces around 8,000 fruits which contain a kernel within a hard husk. At a tree

density of 400 trees per hectare, up to 16,000 kg of dry seeds can be produced per

hectare per year (Ashwath 2010b). The fruits can be harvested either directly from

the tree once they are fully matured or once they have fallen off. Given that a number

of animals eat the ripe fruit, it may also be preferable to harvest fruit from trees just

before they fully mature (Friday and Okano 2006; Mohibbe Azam, Waris and Nahar

2005a). The kernels themselves are contained in a hard shell, as seen in Figure 4-1.


Figure 4-1: Beauty leaf tree growing along a beach front, and in a park and its

distribution in Australia

4.2.2 Candle nut (Aleurites Moluccana)

Aleurites Moluccana, also known as Candle nut due to the traditional use of its waxy

seeds and kernels as natural candles, grows naturally in both subtropical and tropical

dry or wet forest climates, reaching altitudes of up to 700 m (Okonko et al. 2009).

The tree needs 640-4300 mm of annual rainfall, however mature trees require little to

no maintenance. It begins bearing fruit after about 3-4 years of age. The tree

produces spherical fruits, typically 5-8 cm in diameter with a thick, rough and hard

nut shell, as shown in Figure 4-2 (Atabani et al. 2013). The fruits are usually fully

mature and begin dropping off the tree between March and May. The fruits can be

collected from the ground around the trees after they have fully matured and dropped

off. The kernels are extracted by cracking the hard shells of the seeds once fully

dried. The kernels have a dry weight of about 6 g, which, on a per hectare basis,

yields around 16,800 kg of dried kernels per year (Quintao et al. 2011).


Figure 4-2: The tree and kernels of Candle nut

4.2.3 Blue berry lily (Dianella Caerula)

The Blue berry lily (Figure 4-3) is an herbaceous shrub growing about 0.5-1.3 m

height, commonly referred to as the Blue berry lily because of its characteristic

purple berries. The seeds range in size from about 2.9-3.7 mm, with a smooth, black,

shiny texture. The Blue berry lily can be found on the east coast of Australia from

Torres Straight Island to Tasmania and is most common throughout New South

Wales (Ashwath 2010b). The Blue berry lily is easily propagated via direct seeding

in a wide range of environments. This plant is known as one of the hardiest plants,

tolerating droughts, frost, high humidity and heat. For cultivation purposes, a spacing

of 1 x 0.5 m should be used, accommodating a total of 20,000 plants per hectare. In a

year, a typical plant will produce around 0.11 kg of seed. The seeds can be collected

by stripping the stalks on which they grow. The collected fruits should be either dried

or fermented before extracting the seeds. Considering a plant yields 0.11 kg of seed

per year, up to 400 kg of oil can be produced in a hectare (Genever, Grindrod and

Barker 2003).


Figure 4-3: Blue berry lily plant and seeds.

4.2.4 Queen palm (Syagrus Romanzoffiana)

Queen palm (Figure 4-4) is widely cultivated as a decorative plant in Queensland and

the Northern Territory. It has the ability to spread rapidly and can grow in a wide

range ecosystems, from coastal sands to heavy creaking clay soil. The tree is a tall

slender palm reaching heights of around 20 m and it is tolerant of subtropical,

tropical and wetlands conditions (Paroissien 2012). This tree is best suited for acidic,

well-drained soils, thus drainage systems are necessary for large scale cultivation.

This tree also has substantial propagation abilities, partly due to its adaptability to a

large range of soil types, tolerance to a range of temperatures, and moderately low

watering requirements of 400-2000 mm per year. With proper conditions and

maintenance, especially watering, sunlight and soil conditions, around 2,000 kg of

dry seeds can be produced per hectare per year, assuming two harvests a year. The

fruits can be collected by cutting off the panicles on which they grow or by collecting

them from the ground once they have fully matured. The fruits are about 2.5-3 cm

long, have a diameter of 1-2 cm and contain a stringy or hairy kernel (Ashwath

2010b).


Figure 4-4: Tree and kernels of Queen palm

4.2.5 Castor (Ricinus Communis)

Castor is a moderately sized shrub that grows to 1-4 m tall. Growing mainly in

riparian habitats, such as along water courses and flood plains, this shrub has been

declared a weed due to its rapid propagation and resilience to a number of

unfavourable conditions. This shrub is drought tolerant and can flourish in range of

temperatures, from 7 oC to 28 oC [37]. It has 15-45 long leaves, palmate and 5-12

deep lobes with tooth margins. The leaves come in various colours, such as dark

green, dark reddish purple or bronze, as shown in Figure 4-5 [38]. The shrub grows

best in well-drained, moisture retentive clays or sandy loam, under full sunlight. The

shrub also prefers high temperatures and humidity [37]. A typical healthy shrub can

produce a total of about 0.2 kg of seeds in a year. This results in approximately 1,000

kg of seed per hectare, per year [37]. The fruits are easily harvested by picking them

directly from the shrubs or simply collecting them from the surrounding area. The

seeds are then taken out of the fruits by cracking them open. This can be done almost

immediately after being picked, as they are already dry by the time they are able to

be picked [40].


Figure 4-5: Shrub and seeds of Castor

4.2.6 Bidwilli (Brachychiton Bidwilli)

Bidwilli is usually classed as a shrub, but it can grow into a small tree. It grows in dry

rainforests and along various coastal regions, such as the central and southern

Queensland coasts. It produces fruits in a boat-shaped woody follicle containing a

number of hairy seeds, as shown in Figure 4-6 [41]. Bidwilli grows best in sand or

clay soils, requires minimal maintenance and approximately 2,700 plants can be

grown on a hectare of land area [13]. Due to its tolerance to dry conditions and

capability of growing in mixed cultures, Bidwilli holds a wide propagation potential

[42]. A healthy Bidwilli shrub or tree can produce up to 0.6 kg of seeds in a year,

totalling to about 1,600 kg of seeds per hectare [13].


Figure 4-6: Bidwilli plant and seeds

4.2.7 Karanja (Pongamia Pinnata)

The Karanja tree prefers humid and sub-tropical environments, with an average

temperature of 27-38 oC and can be grown up to 14-25 m high. The fruits produced

by Karanja grow in clusters and have pod like features, with a though outer shell

which contains a number of small seeds inside, as shown in Figure 4-7 [43]. It can be

easily cultivated by sowing the seeds in most soil types. The seeds require no pre-

treatments and germinate anywhere between seven days and one month after sowing.

The fruits are best collected directly from the tree once it has matured. The bunches

of fruit are easily collected by cutting the main stems connecting then to the branches

of the tree. The fruits are then dried so that the pods can be cracked open easily,

revealing the seeds. Mature trees can yield 8-24 kg of seed per year [43]. Per hectare,

a total of 3,200-3,600 kg of seeds can be harvested per year.


Figure 4-7: Karanja fruit and seeds

4.2.8 Whitewood (Atalaya Hemiglauca)

Whitewood, as shown in Figure 4-8, is a small tree that can grow up to 6 m tall. It is a

very drought tolerant tree growing throughout the open plains and alluvial flats of the

central regions of Australia [44]. Whitewood trees flower from spring until early

summer and produce a large quantity of fruits containing two or three seeds in each

pod [44]. Due to the ease with which it can be cultivated, coupled with its tolerance

to drought and light frost, this fairly small tree can also be transplanted from

nurseries, occupying 3 x 2 m2, allowing approximately 1,660 trees per hectare [13].

In a year, a healthy tree can produce about 2 kg of seed, which results in 3,320 kg of

seed per hectare, per year [13].

Figure 4-8: Tree and fruit of Whitewood


4.2.9 Cordyline (Cordyline Manners – Suttoniae)

Cordyline (Figure 4-9) is a small tree that resembles the shape of a palm tree. It is an

evergreen tree that typically grows in rainforests, but has also been seen in various

other forest climates. The tree grows near swamps or other areas with poorly drained

soils. The tree usually grows to about 2-5 m in height [45], growing best in well

shaded areas with a plentiful supply of water. It is easy to identify when this tree

does not have sufficient shading, as the leaves quickly scorch under direct sunlight.

The tree can however, withstand dry conditions, so long as irrigation systems are

utilised appropriately. Cordylines continue to flourish in the presence of other

species and can be grown with mixed species as an understory plant [45]. The fruits

can be collected by cutting off the panicles on which they grow and the seeds can be

extracted with relative ease as the fruits are fairly soft. On a per hectare basis, around

3,000 kg of seeds can be produced by Cordyline trees [13].

Figure 4-9: The tree and fruit of Cordyline

4.2.10 Flame tree (Brachychiton Acerifolius)

Flame trees (Figure 4-10), a medium sized tree reaching a height of 30-35 m, are

widely spread through subtropical rainforests, from northern New South Wales to all

throughout Queensland. The tree produces spectacular floral displays after hot and

dry periods [46]. It is a hardy plant that can grow in a range of soils, it thrives in

temperate to tropical climates and it is able to tolerant both dry conditions and heavy


rainfall. It can cope with droughts, however, it does not deal well with colder

temperatures, or harsh or salty winds [47]. The Flame tree flowers in late spring to

early summer and produces fruits with a capsular shape and leathery texture. These

fruits are small and contain 5-8 seeds. These seeds are extracted from the fruit by

allowing the fruit to dry and cracking open the outer shell. Care should be taken

when collecting the seeds as they can cause irritation. The optimal picking period for

the fruits is during the period when the fruits turn a coppery brown colour.

Figure 4-10: Flame tree, fruit and seeds

4.2.11 Chinese rain (Koelreuteria Formosana)

The Chinese rain tree, shown in Figure 4-11, is a moderately sized tree, able to

tolerate a range of conditions, allowing it to be grown in a range of ecosystems. Such

conditions range from frost to high heat, and from well-drained to wet soils [48]. It

can be grown from direct seeding or transplanting from nurseries. This tree can be

grown up to 18 m tall with a little maintenance. Chinese rain trees are able to survive

under a fairly wide range of conditions, however it thrives in warm climates under

full sunlight. Also, this tree can thrive on a number of different soil varieties, but

ideally, the soil should be free-draining [48]. The fruits of the Chinese rain tree grow

from February to March and mature by mid-year, yielding about 2,000 kg per year

per hectare. The fruit grows in drooping clusters and grows to about 50 mm in length

[48]. The seeds are spherical in shape and have a diameter of around 5 mm.


Figure 4-11: Chinese rain tree and fruits

4.3 SEED COLLECTION AND PREPARATION

Seed preparation is critical in optimising the oil extraction process, because physical

conditions such as size, hardness and dryness of the seeds and kernels varies

significantly from one species to another. Several steps are involved including:

kernel extraction, kernel grinding and drying.

4.3.1 Kernel extraction

Dry seeds were cracked open to expose and obtain the oil bearing kernels. In order to

reduce kernel damage and oil loss, seed cracking was done with care using a mallet.

In this process, a handful of seeds were placed on a table surface and cracked

individually, as shown in Figure 4-12. During the seed cracking process, it was found

that rubber-headed mallets were the least preferred compared to wooden or steel-

headed mallets, as they tended to rebound excessively. In order to maximise yield of

oil through the extraction process, the individual kernels had to be separated from the

seeds and crushed. This was done to ensure that the solvent used during the chemical

extraction process would be as efficient as possible in extracting the oil. In this study,

dry seeds were cracked manually using a mallet. Depending on the species of seeds,

various levels of difficulty were found when opening the seeds. For example, Candle

nut, Blue berry lily, Beauty leaf, Karanja, Castor and Chinese rain were relatively

easy to process due to their fruit and seed structure. Other seeds, such as Queen

plam, Whitewood and Cordyline, contained a very hard shell around the kernel which


made those seeds relatively difficult to process. Moreover, special care was needed

when handling the Bidwilli and Flame tree seeds. This is because those seeds

required some effort to crack open and the kernels inside were covered with tiny

hairs that can cause severe irritation to the skin. Latex gloves, safety goggles and

covered overalls were worn to prevent skin contact with these kernels.

Figure 4-12: Kernel extraction

4.3.2 Kernel grinding

The dried seed kernel samples were ground using a blender and coffee grinder to

obtain a fine consistency powder, in order to maximise particle surface area of the

kernels for exposure to the chemical solvent during the extraction process. After that,

the seeds were removed from the oven and placed in zip locked bags in a refrigerated

store room.

4.3.3 Kernel drying

The extracted kernels seeds naturally contained high levels of moisture, which

needed to be removed for effective oil extraction. After being ground into fine

particles, the kernels needed to be dried in order to reduce their moisture content.

The kernels are placed in an aluminium container (Figure 4-13) and left to dry in the

laboratory oven for 9 days at 70ᵒC. Each day, the kernels were weighed in order to

determine the amount of moisture which had evaporated. Each of the samples were


then stirred before returned to the oven, in order to ensure that the kernels dried

evenly.

Figure 4-13: Ground kernels

Based on the weights recorded in the table below, there was a gradual decrease in the

weight of the kernels, with the greatest decreases observed during the first three days

of drying. Once the weight of the kernels displayed no significant changes, they were

considered to be dried. They were then stored in air tight zip lock bags, in order to

maintain their dryness.

4.4 OIL EXTRACTION

Oils from the processed and dried seed kernels were extracted chemically using n-

hexane as a solvent. For this purpose, an accelerated solvent extraction (Dionex™

ASE 350®) machine, shown in Figure 4-14, was used to extract oil at high pressure

and temperature using an accelerated solvent extraction method. The oil extractor

comes with an automated extraction control system that uses elevated temperatures


and pressures to achieve extractions in a short period of time. Measured samples

were inserted into metal sample cells and the desired operating conditions were set

using the control interface. In order to observe the effect of temperature in oil yield,

the extraction process was conducted at the three different temperatures: 50 °C,

100°C and 150 °C. Due to the limitation of the equipment, oil extraction at over

150°C was not possible. N-hexane was pumped into the cell with pressurised

nitrogen gas to achieve a pressure of 1,600 psi. After the extraction process was

completed, all of the extracted oil samples were collected into the vessels in the

collection tray (Figure 4-14). The solvent was separated from the extracted sample

using the Dionex™ SE® 400 solvent evaporator system as shown in Figure 4-15.

Figure 4-14: ASE 350 cell loading process


Figure 4-15: n-hexan removing using DionexTM SETM 400

The percentage of oil obtained from the seed kernels are shown graphically in the

Figure 4-16. Overall, a wide range of oil yields were found and of the eleven native

species investigated in this study, Beauty leaf had the highest oil yield, followed by

the Queen palm, Karanja and Candle nut. However, most of the tested oilseeds

contained more than 30% oil in their kernel. The Chinese rain oil seed showed

lowest oil yield, of about 5%, followed by Cordyline (about 18%). It is also

interesting to see from Figure 4-16 that extraction temperatures had a significant

influence on the oil yield of seed kernels. In general, the oil yield increased with

increasing temperature. This is because the thermal energy of the solvent increases

with increases in temperature, which helps to overcome cohesive and adhesive

interactions. Moreover, higher temperatures increase the molecular motion of

molecules and decrease hydrogen bond interactions. However, this effect varied from

one species to another and may be due to differences in the physical condition of the

kernels. There were also variations in the colour of oils extracted in this study, which

is evident in Figure 4-17.


Figure 4-16: Oil yield of native plant seed kernels

Figure 4-17: Extracted bio-oil sample from native plants

A: Beauty leaf; B: Candle nut; C: Karanja; D: Queen palm; E: Blue berry lily; F:

Castor; G: Whitewood; H: Bidwilli; I: Chinese rain; J: Flame tree; K: Cordyline

4.5 CHEMICAL COMPOSITION

A systematic analysis of chemical composition and comparative fuel properties is

very important for selecting appropriate feedstock for biodiesel production. As

discussed in Chapter 3, chemical composition is a key factor in determining the

quality of biodiesel and the chemical composition of the extracted oil samples was

determined in terms of fatty acid profile and the percentage free fatty acid content

BAC D E F G H I J K


(FFA). The fatty acid profiles were analysed by gas chromatography and flame

ionisation detection (GC-FID), in accordance with EN 14103 standards. The gas

chromatograph (GC) was a Hewlett-Packard 6890 System fitted with Varian Select TM 30 m × 0.32 mm × 0.25 µm column. The chemical composition of the tested bio-

oils are presented in Table 4-1, where it can be seen that the chemical compositions

of bio-oil obtained from native oil seeds were similar to those for conventional edible

oil and with the exception of Queen palm and Castor, they were mostly rich in

triglycerides of Oleic (C18:1), followed by Stearic (C18:0), Linoleic (C18:2),

Palmitic (C16:0) and Linolenic (C18:3) fatty acids. Queen palm bio-oil was mostly

comprised of shorts chain fatty acids, including 42.05 wt.% Luatic acid (C12:0) and

10.45 wt.% Myristic acid (C14:0), as well as small amounts of Caprilic (C8:0) and

Capric (C10:0) acids, which were not found in any other tested bio-oil. These fatty

acids not only consisted of a short carbon chain but they were also saturated fatty

acids, which explains why Queen palm showed the lowest average chain length

(ACL) account and highest percentages of saturated fatty acids among the tested bio-

oil samples. Likewise, Bidwilli bio-oil was rich in saturated fatty acids, containing

42.39% C16:0 and 14.37% C18:0 fatty acids. On the other hand, the highest ACL

and lowest saturated fatty acid content was found for Castor bio-oil, comprising 95

wt.% long chain length fatty acids and mono-unsaturated Gondonic (C20:1) acid,

with small amounts of C18:2 and C18:1 fatty acids. The only other oil to contain a

significant amount of Gondonic (C20:1) acid was Whitewood, which accounted for

25.04% by weight. Due to its high Linolenic (C18:3) fatty acid content, Candle nut

oil showed the highest level of poly-unsaturated fatty acids (PUFA), followed by

Cordilyne, Chinese rain and Blue berry lily oils. In contrast, very small amounts of

PUFA’s were found with Whitewood, Castor and Queen palm bio-oil. As reported in

Chapter 3, the higher the unsaturation of biodiesel, the greater the tendency for the

biodiesel to oxidise. On the other hand, unsaturated fatty acids had a positive

influence on other fuel properties, such as cold filter plugging point temperature.

Therefore, both saturated and unsaturated FAMEs have a role to play in finding the

optimal balance for high quality biodiesel.

FFA’s have a significant effect on biodiesel processing from vegetable oil, as

discussed in Chapter 2. In this study, FFA content of the native plant oil was

analysed using a D5555-95 (2011) standard test method and the results are shows in

Table 4-1. Although the literature (Dorado et al. 2002; Lam, Lee and Mohamed

2010; Kumar Tiwari, Kumar and Raheman 2007; Ramadhas, Jayaraj and

Muraleedharan 2005) suggested that the FFA content of bio-oil should be below 5%

for alkali-trans-esterification, most of the bio-oil tested in this study contained much


higher FFA’s. These results indicate that FFA can be one of the issues impeding the

success of biodiesel production from native species. In particular, Flame tree oil

contained 36.7% FFA’s, which was the highest among the native bio-oils, followed

by Beauty leaf (22%), Queen palm (15%) and Blue berry lily (13.1%). The lowest

FFA content was found in Chinese rain oil, which consisted of 1.8%. In addition,

Queen palm contained an exceptionally higher amount of oxygen, which accounted

for 14.19% on a per weight basis. The oxygen content of the other methyl esters

range d from 10.25 to 11.82%, as shown in Table 4-1.


Table 4-1: Fatty acid profile and chemical composition of bio-oil produced from native plants

Fatty acid profile

Beauty leaf

Candle nut

Karanja Queen palm

Blue berry lily

Castor oil

Whitewood Bidwilli Chinese rain

Flame Cordyline

C8:0 (wt. %) 0.00 0.00 0.00 2.50 0.00 0.00 0.00 0.00 0.00 0.00 0.00 C10:0 (wt. %) 0.00 0.00 0.00 3.47 0.00 0.00 0.00 0.00 0.00 0.00 0.00 C12:0 (wt. %) 0.00 0.00 0.00 42.05 0.00 0.00 0.00 0.00 0.00 0.00 0.00 C14:0 (wt. %) 0.00 0.00 0.00 10.45 0.00 0.00 0.00 0.00 0.00 0.00 0.00 C16:0 (wt. %) 13.66 5.50 10.14 8.23 12.90 0.00 3.99 42.39 18.15 17.48 7.07 C16:1 (wt. %) 0.24 00 00 0.12 00 00 00 00 00 00 00 C18:0 (wt. %) 16.55 6.70 8.88 1.72 4.83 0.83 1.49 14.37 5.84 3.50 3.09 C18:1 (wt. %) 42.48 10.50 66.18 29.01 29.12 2.05 65.73 24.68 20.08 52.40 21.22 C18:2 (wt. %) 25.56 48.50 12.48 2.57 53.16 2.11 1.13 18.56 55.07 23.95 68.61 C18:3 (wt. %) 0.00 28.50 0.00 0.00 0.00 0.00 0.00 0.00 0.00 2.68 0.00 C20:1 (wt. %) 0.00 0.00 0.39 0.00 0.00 95.00 25.04 0.00 0.00 0.00 0.00 C22:1 (wt. %) 0.00 0.00 0.00 0.00 0.00 0.00 1.28 0.00 0.00 0.00 0.00 FFA (wt. %) 22 12.3 6.3 15.2 13.1 8.4 4.5 8.2 1.8 36.7 4.6 O2 (wt. %) 11.32 11.44 11.22 14.19 11.51 10.35 10.94 11.82 11.48 11.55 11.46 H2 (wt. %) 11.95 11.49 11.92 12.14 11.89 12.32 12.01 12.29 11.80 12.05 11.75 C (wt. %) 75.22 76.77 74.93 73.79 76.61 77.32 75.71 75.89 75.87 76.41 76.78 Sat. (wt. %) 27.83 12.20 20.95 65.92 17.72 0.83 12.82 56.76 24.85 20.98 10.17 MUFA(wt. %) 47.49 10.50 66.57 29.01 29.12 97.05 86.05 24.68 20.08 52.40 21.22 PUFA(wt.%) 24.68 77.00 12.48 2.57 53.16 2.11 1.13 18.56 55.07 26.63 68.61 ACL 17.76 17.84 17.87 14.37 17.74 19.90 18.52 17.15 17.65 17.65 17.86 ANDB 0.97 1.64 0.91 0.34 1.35 1.01 0.88 0.61 1.30 1.05 1.58


4.6 FUEL PROPERTIES

Based on the chemical composition of the bio-oil, the corresponding biodiesel

properties were estimated using the ANN model developed in Chapter 3. The

estimated biodiesel properties were: cetane number (CN), kinematic viscosity (KV),

density, higher heating value (HHV), oxidation stability (OS), cold-filter plugging

point temperature (CFPP), flash point temperature (FP) and iodine value (IV). Table

4-2 shows the results of estimated biodiesel properties and corresponding values of

AU, US and EU biodiesel standards. Estimation results indicated that the biodiesel

from most of the native plants met the biodiesel standards for all property indicators,

except oxidation stability.

Cetane number (CN), which is a measurement of the combustion quality of diesel

fuels during compression ignition, is a significant parameter for indicating fuel

quality. CN is associated with the ignition delay time of a fuel, which is the time that

passes between injection of the fuel into the cylinder and the onset of ignition. A

shorter ID time results in a higher CN and vice versa (Gopinath, Puhan and

Nagarajan 2009). According to AU and EU standards for biodiesel, the minimum CN

should be 51, whereas the US standards set a minimum value of 47. Most of the

eleven native species investigated in this study met the minimum requirements for

CN, except for Candle nut and Cordyline seed oils, which is likely to be a result of

their high PUFA concentration. For the same reason, the IV of Candle nut and

Cordyline were found to be very high, exceeding the recommended maximum value

in the EU biodiesel standard. Moreover, biodiesel must have an appropriate KV,

which plays a dominant role in fuel spray, fuel-air mixture formation and the

combustion process (Ramírez-Verduzco, Rodríguez-Rodríguez and Jaramillo-Jacob

2012). The KV of all native plant species listed in Table 4-2 where within the range

recommended by US standards, which was 1.9 – 6.0 cSt. However, the KV values

for Queen palm biodiesel were slightly below the minimum limit of AU and EU

standards. Another important indicator of biodiesel properties is density, which

determines the amount of fuel injected in the cylinder. Changes in fuel density will

influence the stoicheometric ratio of air and fuel, and consequently the engine output


power and exhaust emissions (Knothe 2009). Therefore, AU and EU biodiesel

standards are set at 0.86 – 0.90 kg/l for biodiesel density, and all biodiesel from the

native species investigated in this study were within the standard range. The HHV,

which indicates the energy content of the fuel, was also within the standard range

(39.87 - 40.12 MJ/kg) for regular biodiesel, which is normally 10% to 12% less than

that obtained for petroleum-derived diesel (46MJ/kg) (Ramírez-Verduzco,

Rodríguez-Rodríguez and Jaramillo-Jacob 2012). Oxidation stability is another

important parameter, which indicates the resistance to oxidation during long-term

storage. Usually biodiesel quality declines due to gum formation during the oxidation

process. However, most of the biodiesel investigated in this study performed poorly

in terms of OS. Queen palm biodiesel was the only sample that fulfilled all biodiesel

standards in terms of OS, due to its very high saturated fatty acid content. On the

other hand, the oxygen stability of Candle nut, Blue berry lily, Chinese rain and

Cordyline oil biodiesel were below the specified limits for all biodiesel standards.

However, the US biodiesel standard imposed less restriction in OS, which is set at a

minimum of 3 hours, and therefore, the biodiesel from Beauty leaf, Karenja, Castor,

Whitewood, Bidwilli and Flame tree oils comfortably met this standard. CFPP is a

critical factor in determining biodiesel quality, especially for cold climate conditions.

High CFPP values were found for the biodiesel from Queen palm, Bidwilli, Beauty

leaf and Whitewood, which is acceptable because the chemical composition of that

feedstock contained a high portion of saturated fatty acids. Flash point is another

important parameter for assessing biodiesel quality, indicating the fire hazards during

fuel transport and storage. It is the lowest temperature at which the fuel will start to

vaporise to form an ignitable mixture when it comes into contact with air (Ali, Hanna

and Cuppett 1995a). This is reflected by the respective limits within the AU, EU and

US biodiesel standards shown in Table 4-2. All of the investigated biodiesel listed in

Table 4-2 show good quality in terms of FP. The lowest FP was found for Beauty

leaf biodiesel, which was well above the recommended minimum value in all

biodiesel standards.


Table 4-2: Estimated biodiesel properties

Properties Unit

Biodiesel standard

Bea

uty

leaf

Can

dle

nu

t

Kar

anja

Qu

een

pal

m

Blu

e be

rry

li

ly

Cas

tor

oil

Wh

ite-

woo

d

Bid

wil

li

Ch

ines

e ra

in

Fla

me

Cor

dyli

ne

AUS US EU

CN - 51, min 47, min 51, min 64.12 38.42 59.38 62.28 51.79 59.84 59.18 64.55 50.43 56.64 45.78

KV cSt. 3.5-5 1.9-6 3.5-5 4.39 3.79 4.66 3.38 4.24 5.81 5.15 4.61 4.26 4.48 4.15

Density Kg/l 0.86-0.90 n/a 0.86-0.90 0.878 0.883 0.874 0.871 0.879 0.873 0.873 0.871 0.879 0.876 0.882

HHV Mj/kg n/a n/a n/a 40.12 39.87 40.01 39.39 39.97 40.10 40.04 39.95 39.97 39.98 39.97 OS hours 6, min 3, min 6, min 4.34 1.45 4.26 6.46 2.57 3.88 4.38 5.46 2.68 3.67 1.61 CFPP °C Report Report Report 12.54 -4.22 8.52 7.19 -4.85 -15.17 12.06 14.91 1.16 -5.09 -9.46 FP °C 120, min 93, min 120, min 142.78 157.57 148.23 185.44 162.43 201.87 164.74 163.28 165.61 160.92 163.95

IV g iod/ 100g

n/a n/a 120, max 79.25 164.77 77.51 29.86 114.32 79.75 74.04 53.16 112.19 93.17 131.25


4.7 EVALUATION OF NATIVE PLANT METHYLE ESTER

In order to constitute an ideal source of sustainable biodiesel, the feedstock should

contain a sufficient amount of bio-oil, with a suitable chemical composition, in order

to elicit good fuel quality properties. However, selection of the most suitable

feedstock for industrial production is a multi-criteria problem, as it involves multiple

quality indicators. In this study, a multi-criteria decision method (MCDM) software

PORMETHEE-GAIA was used for the selection of biodiesel for large scale

production. The most suitable native plant species were selected from the eleven

native species investigated in this study, based on the parameters listed in Table 4-3.

The GIGA plan displays how the alternatives perform in terms of the different

criteria, as shown in Figure 4-18. The length of the criteria vectors and their

directions indicate the influence these criteria have on the decision vector (red line in

Figure 4-18) and preference of the species. The preference functions of criteria were

modelled as Min (i.e. lower values are preferred for good biodiesel) or Max (higher

values are preferred for good biodiesel), as per Table 4-3. When the criteria are

oriented in an opposite direction they are in conflict and when they are oriented in a

similar direction they express the same preference (Espinasse, Picolet and Chouraqui

1997). For example, maximum values for oil yield (OY), IV and OS were preferable

for good biodiesel and therefore, those criteria lines are in same direction of decision

vector. The preference functions were obtained by principle component analysis

(PCA) techniques (Brans 2002), however preference function selection also

influenced the orientation of criteria (which was also suggested in (Islam et al.

2013)). For example, IV and OS were inversely related but still showing in the same

direction within ±45○. This is because OS was preferred to maximum, but iodine

number was preferred to minimum, as shown in Table 4-4. Therefore, criteria which

are in the same preference (min/max) and lie close to ±45° are correlated. The

decision vector, which is marked as red line in Figure 4-18, is the direction in which

the decision maker is invited to decide. The direction and length of criteria are

indicative of their influence on the decision vector (Islam et al. 2013), such that the

very short length of some criteria (i.e. difficulty level (DL) and HHV) indicate the

little effect they had on the decision vector. However, the freedom of decision vector


is modelled by the preference weight of individual criteria and therefore, if the

weights are modified, the decision maker is invited to decide in another direction

(when the position of the criteria and alternative remain unchanged) (Brans 2002).

The Phi value is the net flow score, which could be negative or positive depending

upon the angular distance from the decision vector and the distance from the centre.

Figure 4-18 shows the raking results of eleven native species biodiesels with its

corresponding phi value for the equally weighted criteria and preferred function

listed in Table 4-3. Results showed that Beauty leaf was most aligned with the

decision vector and in the farthest relative position from the centre, giving it the

highest of ranking followed, by Queen plam, Castor and Karenja. On the other hand,

biodiesel from the Flame tree was at the bottom of the ranking.

Table 4-3: Variables and preference used in PROMETHEE-GAIA analysis

No Variables Preference for PROMETHEE-GAIA

1 Seeds production per year per hector Max 2 Difficulty level of seed processing Min 3 Oil yield Max 4 Free fatty acid content in oil Min 5 Kinematic viscosity Min 6 Density Min 7 Higher heating value Max 8 Oxidation stability Max 9 Iodine value Min 10 Cetane number Max 11 Flash point temperature Max 12 Cold filter plug point Max


Figure 4-18: (a) Graphical Analysis for Interactive Assistance (GAIA) plot for eight

biodiesel showing 11 criteria and decision vector. (b) Corresponding complete

ranking and Phi value of biodiesel from native plants

The quality ranking analyses of biodiesel shown in the previous section was

conducted with an equal weighting of all parameters. However, for industrial and

economical biodiesel production, the oil content of the feedstock may be a more

important criterion than others. Moreover, the importance of some fuel properties

depends on the country and place where it will be used and stored. For an example,

in tropical/sub-tropical regions, CFPP was not considered to be of importance,

however elevated temperatures in these regions are likely to affect the OS of

biodiesel. On the other hand, in colder climate conditions, CFPP are more important

than OS. In this study, ranking sensitivity analysis was conducted for the criteria OY,

CFPP and OS, by increasing the weighting from 1 (equal to other parameters) to 10,

and the results are shown in the Table 4-4 to 4-6. As shown in Table 4-4, Beauty leaf

was always ranked number 1 with the increasing OY weighting, whereas the rank of

Candle nut and Flame tree gradually improved from 7 to 2 and 11 to 6, respectively.

Rank Biodiesel Phi

1 Beauty leaf 0.1917

2 Queen palm 0.1500

3 Castor 0.1333

4 Karanja 0.0917

5 Whitewood 0.0583

6 Chinese rain

0.0083

7 Candle nut -0.0083

8 Cordyline -0.0500

9 Bidwilli -0.1500

10 Blue berry lily

-0.2000

11 Flame tree -0.2250

(a) (b)


With a weighting of 10 for OY, Queen palm biodiesel dropped down from a ranking

of 2 to 3, however it increased to first position when the weighting of OS was

increased to 2 and it remained there as the weighting increased (see Table 4-5). The

largest improvement in ranking was observed for Bidwilli biodiesel, which increased

in rank from 9 to 4 when the weighting of OS was increased to 6. Whitewood also

showed a significant improvement in rank (from 5 to 2) for a heavier weighting of

OS. The rank of Flame tree also improved from 11 to 8 following the same increase

in weighting.

Table 4-4: Comparative rank shift with different weighting for bio-oil yield

feedstock Weighting

2 4 6 8 10

Beauty leaf 1 ‐ 1 ‐ 1 ‐ 1 ‐ 1 ‐ Queen palm 2 ‐ 2 ‐ 2 ‐ 2 ‐ 3

Castor 3 ‐ 4 5 5 ‐ 5 ‐ Karanja 4 ‐ 5 4 4 ‐ 4 ‐ Whitewood 6 6 ‐ 6 ‐ 6 ‐ 7

Chinese rain 7 10 10 ‐ 10 ‐ 10 ‐ Candle nut 5 3 3 ‐ 3 ‐ 2

Cordyline 8 ‐ 7 9 9 ‐ 9 ‐

Bidwilli 9 ‐ 8 8 ‐ 8 ‐ 8 ‐

Blue berry lily 11 11 ‐ 11 ‐ 11 ‐ 11 ‐

Flame tree 10 ‐ 9 7 7 ‐ 6

Table 4-5: Comparative rank shift with different oxidation stability of biodiesel

Feedstock Weighting

2 4 6 8 10

Beauty leaf 2 2 ‐ 2 ‐ 2 ‐ 3

Queen palm 1 1 ‐ 1 ‐ 1 ‐ 1 ‐ Castor 3 ‐ 5 6 6 ‐ 6 ‐ Karanja 4 ‐ 4 ‐ 5 5 ‐ 5 ‐ Whitewood 5 ‐ 3 3 ‐ 3 ‐ 2

Chinese rain 6 ‐ 7 7 ‐ 7 ‐ 7 ‐ Candle nut 7 ‐ 8 9 9 ‐ 9 ‐ Cordyline 9 10 11 ‐ 11 ‐ 11 ‐

Bidwill 8 6 4 4 ‐ 4 ‐

Blue berry lily 11 11 ‐ 10 ‐ 10 ‐ 10 ‐

Flame tree 10 9 8 8 ‐ 8 ‐


As shown in Table 4-6, a massive change in raking was observed with the heavier

weighting of CFPP. Due to their high saturated and monounsaturated fatty acid

content, the ranking of Beauty leaf and Queen palm biodiesel dropped dramatically

from 1 to 10 and 2 to 7, respectively. In addition, the ranking of Karanja, Whitewood

and Bidwilli also decreased with the heavier weighting of CFPP. In contrast, the

ranking of Castor, Cordyline, Bidwilli, Flame tree, Candle nut and Blue berry lily

improved significantly with a weighting of 10 for CFPP, being 1-5, respectively (see

Table 4-6).

Table 4-6: Comparative Rank shift with different cold filter plugging point temperature

Feedstock Weighting

2 4 6 8 10

Beauty leaf 2 6 9 9 ‐ 10

Queen palm 3 3 4 5 7

Castor 1 1 ‐ 1 ‐ 1 ‐ 1 ‐ Karanja 4 ‐ 7 8 8 ‐ 8 ‐ Whitewood 5 ‐ 9 10 10 ‐ 9

Chinese rain 7 5 6 7 6

Candle nut 8 4 3 4 4

Cordyline 6 2 2 ‐ 2 ‐ 2 ‐

Bidwilli 11 11 ‐ 11 ‐ 11 ‐ 11 ‐

Blue berry lily 9 10 7 6 5

Flame tree 10 8 5 3 3 ‐

4.8 CONCLUSION

The need for a new source of renewable energy is growing ever more critical and

together with the issue of food vs. fuel, it is essential that second-generation biodiesel

feedstocks do not compete with food crops or lead to land-clearing, whilst still

providing the same benefits as first-generation biodiesel (i.e. greenhouse-gas

reductions). This chapter reported on the investigation of eleven species of native

Australian plants, in order to discover their viability for serving as an alternative

feedstock for biodiesel. Dry seeds of selected plants were collected from local


sources in Queensland and processed for bio-oil extraction. DionexTM ASETM 350

Accelerated Solvent Extractor was used for this purpose and the respective oil yields

were determined in terms of oil percentage. The free fatty acid content of the

collected bio-oils were determined, before they were converted into biodiesel using

methyl alcohol. The chemical composition of biodiesel from native plants were

determined and important fuel properties were estimated using the ANN model

described in the previous chapter. Based on their dry seed production capability,

level of seed processing difficulties, bio-oil content in the seed kernel, amount of free

fatty acids and estimated fuel properties of biodiesel, the native species feedstock

was the evaluated and compared using the multi-criteria decision method (MCDM)

software PORMETHEE-GAIA. In addition, sensitivity analysis of native plant

ranking was investigated by changing the weighting of three important criteria - OY,

OS and CFPP. Overall, this study found that Beauty leaf, Queen palm, Castor and

Karanja were the top ranked candidates for biodiesel production. Based on the

variation of weighting for certain criteria, Beauty leaf and Queen palm biodiesel were

found to be a good choice for second-generation biodiesel production in tropical/sub-

tropical regions, however the opposite was true for cold weather conditions. For cold

climate conditions, Castor, Cordyline or Flame tree might be a better choice than

Beauty leaf and Queen palm.

Chapter 5: Pilot scale Beauty leaf (Calophyllum Inophyllum) biodiesel production 123

Chapter 5: Pilot scale Beauty leaf (Calophyllum Inophyllum) biodiesel production

Physico-chemical Assessment of Beauty leaf (Calophyllum

Inophyllum) as Second-Generation Biodiesel Feedstock

Jahirul M. I*, Brown R. J, Senadeera W, Ashwath N, Rasul M. G, Rahman M. M,

Muhammad A. I and O’Hara I. M

Publication: Submitted to the journal of Energy Conversion and Management

Author Contribution


Jahirul M. I Conducted the experiments, performed the data analysis and drafted the manuscript Signature

Brown R. J Supervised the project, aided with the data analysis, development of the paper and extensively revised the manuscript

Senadeera W Supervised the project, aided with the development of the paper

Ashwath N Assisted with conducting the experiment

Rasul M. G Assisted with conducting the experiment and revised manuscript

Rahman M. M Assisted with conducting the experiment

Muhammad A. I Assisted data analysis





Name


Signature

Date

124 Chapter 5: Pilot scale Beauty leaf (Calophyllum Inophyllum) biodiesel production

Abstract

Recently, second-generation (non-vegetable oil) feedstocks for biodiesel production

are receiving significant attention due to the cost and social impacts associated with

using food products for the production of energy products. The Beauty leaf tree

(Calophyllum Inophyllum) is a potential source of non-edible oil for producing

biodiesel because of its suitability for production in a wide range of climate

conditions, easy cultivation, high fruit production rate, and the high oil content in the

seed. In this study, oil was extracted from Beauty leaf tree seeds through three

different oil extraction methods. The important physical and chemical properties of

these extracted Beauty leaf oils were experimentally analysed and compared with

other commercially available vegetable oils. Biodiesel was produced using a two-

stage esterification process consisting of an acid catalysed pre-esterification process

and an alkali catalysed transesterification process. Fatty acid methyl ester (FAME)

profiles and physico-chemical properties including kinematic viscosity, density,

higher heating value and acid value were measured. Other fuel properties including

oxidation stability, iodine value, cetane number, flash point and cold filter plugging

point temperature were estimated using ANN models based on the FAME analysis.

Physico-chemical properties of Beauty leaf oil biodiesel are described and compared

with biodiesel standards and commercially available biodiesels produced from other

feedstocks. The quality of Beauty leaf biodiesel has been assessed based on 10

important chemical and physical properties through a Preference Ranking

Organisation Method for Enrichment Evaluation (PROMETHEE) and Graphical

Analysis for Interactive Assistance (GAIA) analysis. The results show that

mechanical extraction using a screw press produces oil at a low cost, however,

results in low oil yields. The study found that seed preparation has a significant

impact on oil yields, especially in the mechanical oil extraction method. High

temperature and pressure in the extraction process increases the oil extraction

performance. On the contrary, this process increases the free fatty acid content in the

oil. A clear difference was found in the physical properties of Beauty leaf oils, which

eventually affected the oil to biodiesel conversion process. However, Beauty leaf oils

methyl esters (biodiesel) were very consistent physico-chemical properties and able


to meet almost all indicators of biodiesel standards. Results of this study indicated

that Beauty leaf is a suitable feedstock for commercial production of second-

generation biodiesel. Therefore, the findings of this study are expected to serve as the

basis for further development of Beauty leaf as a feedstock for industrial scale

biodiesel production.

Key words: Beauty leaf, second-generation biodiesel, oil extraction, physico-

chemical properties, PROMETHEE-GAIA

5.1 INTRODUCTION

Rapid growth in population, urbanisation and energy demand, together with the

depletion of conventional fossil fuel reserves and degradation in air quality are

continuously motivating researchers to find more sustainable and cleaner energy

sources. As a consequence, biodiesels produced from vegetable oil and animal fat

feedstocks are receiving significant attention as an alternative to fossil-based diesel.

The first recorded production of biodiesel occurred in 1937 when fatty acid methyl

esters from palm oil were produced via trans-esterification. Biodiesel research

continued from this time but its potential was not fully realised until the 1970s

energy crisis when interest in alternative fuels was renewed (Lim and Teong 2010).

Since this time, biodiesel has been produced on an industrial scale and a multitude of

feedstocks have been assessed. It is generally held that biofuels offer many benefits

over fossil-based fuels, including ability to produce from regionally available

biomass sources, lower greenhouse gas emissions, enhanced biodegradability and

enhanced sustainability characteristics (Reijnders 2006; Ellabban, Abu-Rub and

Blaabjerg 2014). Biodiesel typically contains oxygen levels of 10–45% by mass

while fossil-based diesel has virtually no oxygen. This higher oxygen content makes

the chemical properties of biodiesel more favourable for complete combustion. In

addition, biodiesels typically have very low sulphur contents and many have low

nitrogen levels, which improves air quality from fuel combustion (Hoekman et al.

2012). At the same time, the rise in production and consumption of biodiesels has

focused attention on biodiesel quality standards (Behçet 2011). The most


internationally recognised biodiesel standards are: EN14214 (in Europe) and ASTM

D-6751 (in USA). Other countries have defined their own standards which typically

derive from either EN14214 or ASTM D-6751(Hoekman et al. 2012).

A large number of potential biodiesel feedstocks have been investigated including

soybean oil, sunflower oil, corn oil, used cooking oil, olive oil, rapeseed oil, castor

oil, lesquerella oil, milkweed seed oil, Jatropha curcas, Pongamia glabra (karanja),

Madhuca indica (mahua) and Salvadora oleoides (Pilu), palm oil and linseed oil.

(Goodrum and Geller 2005; Holser and Harry-O’Kuru 2006b; Kaul et al. 2007a;

Raadnui and Meenak 2003; Lin and Li 2009b; Marchetti, Miguel and Errazu 2008;

Leung and Guo 2006a). However, only a few feedstocks, including rapeseed,

soybean, sunflower, waste cooking oil and tallow are being used to commercially

produced biodiesel 6on industrial scale (Jahirul, Brown, Senadeera, #039, et al.

2013). These commercial biodiesels are produced from edible oil feedstocks and are

typically referred to as first-generation biodiesels (Rashid and Anwar 2008b). The

most contentious issue affecting the production of first-generation biodiesels is the

use of high quality agricultural land for biodiesel production. Farmers of these crops

now have the choice to sell feedstock to the biodiesel production market or food

market. If the biodiesel production market is offering a higher price, farmers will

choose this option more often than not to make a living. This is of particular concern

in poorer countries where crops used for biodiesel production displace the production

of food crops, thus causing a shortage. Supply and demand dictates that a shortage

will cause a price rise, which countries such as Malaysia are already experiencing.

This issue caused global debate due to the 2007-2008 world food price crises.

Different arguments exist regarding the cause of this crisis, however there has been

speculation that the increased consumption of biodiesel caused a food shortage and

subsequent price increases (Kingsbury 2007). Therefore, an alternative must be

considered which eliminates the disadvantages of first-generation biodiesels.

Research is currently taking place on second-generation biodiesels which do not

compete with food production.

In a recent study (Ashwath 2010a), a large number of non-edible oil seed plants were

been identified which have the potential to be used as feedstocks for second-


generation biodiesel production and have the ability to grow on previously cleared or

degraded land. Of the potential feedstocks assessed, Beauty leaf was identified as the

most suitable feedstock for future biodiesel production as a result of the high oil

productivity of the seeds. Beauty leaf is a moderately sized (8-20 m high) quick

growing tree that can grow up to 1 meter tall within a year. It has been seen to

flourish in the presence of weeds and other species, so can be grown in mixed

cultures with minimal cultivation (Mohibbe Azam, Waris and Nahar 2005b). The

tree naturally grows in tropical and sub-tropical climates (typical temperature range

of 18–33 oC) and in free draining soils near shorelines. Beauty leaf has been seen to

grow in clay soils within Australia and throughout southern and central Asia

including Indonesia, Sri Lanka and India (CSIRO 2010). The Beauty leaf tree bears

fruit twice a year and a healthy plant is able to produce around 8,000 fruits per year.

The fruits contain an oil bearing kernel within a hard husk. The fruits can be

harvested either directly from the tree once they are fully matured or from the ground

once they have fallen off the tree. With around 4,000 fruits per harvest (or 8,000

fruits per year), seed productivity can be as high as 40 kg of seeds per tree per year.

At a tree density of 400 trees per hectare, up to 16,000 kg of dry seeds can be

produced per hectare per year (Mohibbe Azam, Waris and Nahar 2005b; Okano

2006). However, its potential as a source of second-generation biodiesel is yet to be

realised, due to a lack of knowledge of the process for oil extraction from the seed,

oil quality and biodiesel quality. Therefore, this study aims to access different oil

extraction methods for Beauty leaf oil seed and to evaluate the quality of the oil and

biodiesel produced.

5.2 SEED PREPARATION

Seed preparation is critical in optimising the oil extraction process from plant to oil

seed. This is because the physical conditions such as size, hardness and dryness of

seeds and kernels varies significantly from one species to another. Several steps are

involved including: seed collection, kernel extraction and drying. Figure 5-1 shows

Beauty leaf seed preparation steps and brief descriptions of these steps are given in

following sections.


Figure 5-5-1: Flow chart of Beauty leaf seeds preparation

5.2.1 Seeds collection

About 140 kg of dry Beauty leaf seeds were collected from local seed suppliers in

Queensland, Australia. These seeds were in a ground-dried state (i.e. had been on the

ground for some time prior to collection) and collected mostly from coastal locations

of northern Queensland. In this state the flesh was not present with the endocarp

being the outermost layer. Furthermore, the kernels had shrunk from their fresh state

and a rattle could be heard when seeds were shaken.

5.2.2 Kernel extraction

Dry Beauty leaf seeds were cracked open to expose and obtain the oil bearing

kernels. In order to reduce kernel damage and oil loss, seed cracking was done with

care using two tools which are stompers and mallets. With the stomper, a large

number of seeds were placed on the ground and worked until a number had been

cracked after which the kernels and the waste husk were removed. For the mallet,

operators placed a handful of seeds on a table surface and cracked them individually,

before removing the kernels and the waste husk. During the seed cracking process, it

was found that rubber-headed mallets were preferred less than wooden or steel-

headed mallets, as they tended to rebound excessively. Using mallets meant that

seeds were cracked either individually or several at a time, whereas the stomper was

capable of cracking numerous seeds at a time. However, due to the variability in size

(roughly 1.5–4.5 cm in diameter) of the seeds, the efficacy of the stomper was

reduced as it only struck the largest seeds with each blow. About 51 kg of usable wet

kernels were obtained from cracking 140 kg of Beauty leaf seeds resulting in a kernel

Green seeds and plant

Dry seeds

Oil extraction

Kernel drying

Ground karnel


yield of 36%. Assuming a seed productivity of 16,000 kg of dry seeds per year per

hectare, it is likely that the Beauty leaf plant is able to produce ~5800 kg of wet

kernel per year per hectare.

5.2.3 Kernel drying

The kernels of Beauty leaf seeds naturally contain high moisture which needs to be

removed for effective oil extraction. Drying was conducted using a Clayson Electric

oven with temperature controller. Kernels were placed in the foil trays; generally 2

kg per tray to ensure the product was spread adequately for uniform drying. The trays

were weighed before placed in the drying oven for 3 days at 40 °C. After that the

temperature of the oven was increased to 70 °C and the drying progress was

monitored by weighing the trays several times daily. Because a fan-forced oven was

used, the tray positions in the oven seemed to impact on its drying, especially those

trays nearest to the oven walls. In order to reduce this effect, the trays were rotated in

the oven to ensure uniform drying rates. The seed was dried until it was observed

that the weight was remaining constant for one day. The moisture content of the

kernels was approximately 32%. Therefore it is expected that about 3960 kg of dry

kernel can be produced from Beauty leaf plant per hectare per year. However, this

may vary depending on seasonal variation, location and maturity of the seeds.

5.2.4 Kernel grinding

The dried seed kernel samples were ground using a blender and coffee grinder to

obtain a fine consistency to maximise particle surface area of the kernels to exposure

in the chemical solvent during the extraction process. After that the seeds were

removed from oven and placed in zip locked bags and placed in a refrigerated store

room.

5.3 OIL EXTRACTION

Oil was extracted from the kernel by three different methods which are: mechanical

oil extraction using an electric powered screw press, chemical oil extraction using n-


hexane as the solvent at room temperature and pressure and chemical oil extraction

under high pressure and temperature condition. Each of the extraction methods has

its advantages and limitations. A brief description of the oil extraction methods

conducted in this study is given in the following sections.

5.3.1 Mechanical oil extraction using oil press (OP)

A Mini 40 electric motor powered screw press shown in Figure 5-2 was used for the

mechanical oil extraction. As the screw press used in this study was not designed for

Beauty leaf seeds a degree of experimentation was undertaken to optimise pressure

and speed. Beauty leaf kernel was found to be very difficult to process using the

screw press due its physical properties and several cycles were required to extract the

oil. It was also difficult to control the soft kernel paste after one pass and to keep the

process clean. Two operators were required to constantly attend to the machine and

the rate of oil production was very low, typically taking over an hour to process just

about 200 g of sample. Mixing of rice husks with kernels significantly accelerated

the rate of oil production. It was also observed that temperature (both ambient and

barrel/product) have a significant impact on the oil yield. This was evident when

attempting to expel oil at low ambient temperatures (e.g. cold mornings) which took

longer. However, improvements in Beauty leaf oil extraction using the screw press

may be possible by optimising key design parameters of the machine including

pressure, compression ratio, speed, and temperature.

Figure 5-5-2: Mechanical oil extraction through a screw press


5.3.2 Chemical oil extraction using n-Hexane (nHX)

In this process, oil was extracted using n-hexane as an oil solvent at ambient

conditions. The ground kernels were put into conical flasks in which hexane were

added at a ratio of 2:1 (mL hexane: g kernel). The mixture was given an initial stir to

ensure that all kernels were wetted with hexane. Conical flask openings were covered

with aluminium foil and placed on an orbital mixer under the fume hood and the

samples were left to mix for at least eight hours. After this, the hexane/oil mixtures

were collected, filtered and decanted into aluminium foil containers for solvent

evaporation, and placed under the fume hood (Figure 5-3) for 8 - 10 hours. Hexane

was again added to the conical flask of kernels, but at a ratio of 1:1 for the second

extraction, and a similar procedure was followed for recovery of the oil. When it was

determined that the hexane had been fully evaporated, the oil was transferred into

containers for analysis. It was observed that the n-hexane oil extraction method

resulted in a much greater oil yield than the mechanical oil extraction process.

Figure 5-5-3: Chemical oil extraction

5.3.3 Accelerated solvent extraction (ASE)

The accelerated solvent extraction (Dionex™ ASE 350®) machine shown in Figure

5-4a was used to extract Beauty leaf oil at high pressure and temperature using

accelerated solvent extraction method. The oil extractor comes with an automated

extraction control system that uses elevated temperatures and pressures to achieve

extractions in a short period of time. Measured samples were inserted into metal

sample cells and the desired operating conditions were set using the control interface.

Although the machine allows for the use of up to 3 different types of solvents only n-


hexane was used as the solvent for lipid extraction. The electric oven maintains the

cell contents at the selected operating temperature throughout the extraction process

and was set to 150 °C. n-hexane was pumped into the cell with pressurised nitrogen

gas to achieve a pressure of 1600 psi. After the extraction process was completed, all

the extracted oil samples were collected into the vessels in the collection tray (Figure

5-4a). The solvent was separated from the extracted sample using the Dionex™ SE®

400 solvent evaporator system as shown in Figure 5-4b.

(a) (b)

Figure 5-5-4: ASE oil extraction (a) Dionex™ ASE 350® (b) solvent removal with

flow of nitrogen

5.4 OIL YIELD

The Beauty leaf oil yield from the three extraction methods used in this study are

summarised in Figure 5-5. All the results are averages of three replicates for each

extraction method. Overall the highest oil yield was obtained using the ASE oil

extraction method which produced on average 51.5 g of oil per 100 g of dry kernels.

The static n-hexane extraction methods produced on average 48 g of oil from 100 g

of dry kernels. These results indicated a 3-4% oil yield increase for the higher

pressure and temperature conditions. This result is likely to be due to the


improvement in solvation power of n-hexane under higher temperatures. With

increases in temperature, the thermal energy of the solvent increases, which help to

overcome cohesive and adhesive interactions. Moreover, higher temperatures

increase the molecular motion of molecules and decrease hydrogen bond

interactions. Higher pressure facilitates more interactions between the solvent and oil

especially oil that is trapped in pores and would normally not be contacted by

solvents under ambient conditions. These results indicate that about 1.56 tons of oil

per hectare per year can be produced from Beauty leaf plant using chemical

extraction methods with high pressure and temperature. The results also indicate that

the chemical method is more repeatable, and, given the relative ease of preparation

and no requirement for extensive training, it is considered to be more reproducible.

Seed preparation has a significant impact on oil yields especially for the screw press

extraction method. Mechanical extraction using the screw press can produce oil from

appropriately prepared product, but overall this method is ineffective, with relatively

low yields for a great deal of effort.

Figure 5-5-5: Beauty leaf oil yield from three different extraction methods.

5.5 COMPARISON OF OIL EXTRACTION METHODS


During Beauty leaf oil extraction using the three different oil extraction methods, it

was observed that all the methods have techno-economic advantages and limitations

compared with each other. For example, although the ASE method had higher oil

yields over the other two techniques, it requires high investment, sophisticated

equipment and skilled operators. The advantages and disadvantages of the three oil

extraction methods are summarised in Table 5-1.

Table 5-1: Advantages and disadvantages of the three extraction methods

Methods Advantages Disadvantages

Oil press Virgin oil is more sought after No potential for solvent contamination

Relatively inexpensive after initial capital costs

Minor consumables cost Low preparation is required Whole seeds or kernels can be processed

Time and labour intensive Low oil yields Operators require experience to achieve best results

High dependence on kernel moisture content

Relatively dirty process Filtration or degumming process of oil is required

Low and inconsistent oil production High oil loss

n‐Haxane Repeatable and reproducible

results and process High oil yields Relatively simple process Suitable for bulk oil extraction Low capital investment No especial equipment required

Less sought after than virgin oil High potential for solvent contamination Safety issues and environmental concerns regarding the use of hexane

Relatively costly High hexane requirement Only kernel can be processed

ASE Automatic technique

Condition can be optimised

More efficient

Clean process

Relatively less solvent

consumption

Less time and labor incentives

High oil yield

Very high initial cost

High preparation required

Special equipment and skill required

Potential for solvent contamination

Only kernel can be processed


5.6 OIL ANALYSIS

Experiments were conducted to determine the quality of the oil extracted in terms of

acid value, density, kinematic viscosity, surface tension and higher heating value.

Viscosity was measured using a Brookfield DV-III Rheometer according to the

ASTM D445 standard test method. Oil density and surface tension were analysed in

accordance with ASTM D1298 and ASTM D971-12 standard test methods using a

KSV Sigma 702 Tensiometer. Higher heating value of biodiesel was measured in

accordance with ASTM D240-09 standard method using a Parr 6200 oxygenated

bomb calorimeter. Acid value of oils was measured using D5555-95 (2011) standard

test method. All experiments were undertaken in triplicate and average results were

used. The experimental results are shown in Table 5-2 along with similar parameters

of other vegetable oil results obtained from literature. Beauty leaf oil obtained from

the screw press showed higher density, kinematic viscosity and lower heating value

compared to oil obtained from ASE and ambient n-hexane methods. This might be

due to the presence of suspended small particles remaining in the oil from

mechanical extraction although large particles were removed via centrifugation. The

acid values of oil from the screw press (36.26 mgKOH/g) and ASE oil (39.22

mgKOH/g) were much higher than the acid value of oil obtained from ambient n-

hexane extraction (24 mgKOH/g). The high pressure and temperature involved in

ASE and press oil might be responsible for creating high free fatty acid in the oil.

When compared with conventional vegetable oils, all of the Beauty leaf oil samples

showed much higher acid values in Table 5-2. These results confirm that raw Beauty

leaf oil is not suitable directly as a fuel for diesel engine application because of

having high acid value and kinematic viscosity and conversion to fatty acid methyl

esters is required prior to use as a fuel.


Table 5-2: Physical properties of Beauty leaf oil

Vegetable Oil Acid value (mgKOH/g)

Density (kg/lit.)

Higher heating value (Mj/kg)

Kinematic viscosity

(40 °C, cSt)

Beauty leaf*

Oil press 36.26 0.964 38.10 56.74

n-Haxane 24.00 0.936 39.52 42.24

ASE 39.22 0.945 39.34 44.05

Rapeseed** 0.39 0.907 40.05 38.25 Canola** 0.16 0.912 39.74 33.34

Soybean** 0.82 0.914 39.62 32.85

Sunflower** 0.20 0.916 39.49 31.63

Cottonseed** 0.30 0.914 39.40 33.70

Palm** 0.90 0.916 40.14 39.65 * Experiment; **Literature (Singh and Singh 2010; Ramos et al. 2009;

Hoekman et al. 2012)

5.7 BIODIESEL PRODUCTION

Like other conventional vegetable oils shown in Table 5-2, the kinematic viscosity of

Beauty leaf oils (42.24 – 56.74 cSt) are much higher than that of petroleum diesel (2-

3 cSt). Higher viscosities result in higher drag in the fuel line and injection pump,

higher engine deposits, higher fuel pump duties and increased wear in the fuel pump

elements and injectors. This can adversely influence fuel spray, fuel-air mixture

formation and the combustion process which eventually affects engine performance

and emissions (Jahirul, Brown, Senadeera, et al. 2013). Therefore, raw Beauty leaf

oils, as well as other vegetable oils, are not suitable to use as a direct fuel in the

diesel engine. In order to reduce the viscosity of bio-oils to make them normally

suitable for diesel engine use, transesterification is widely used due to its high

conversion efficiency, simplicity, low conversion cost and the fuel qualities of the

product (Lin et al. 2011; Gerpen 2005; Issariyakul et al. 2007). Transesterification is

a chemical reaction in which oils (triglycerides) react with alcohols (eg. methanol,

ethanol) under acid or alkali catalysed conditions, producing fatty acid alkyl esters

and glycerol. The catalyst is used to improve the reaction rate and yield of esters.

After the reaction is complete, glycerol is removed as a by-product and the esters are

purified to produce biodiesel (Fernando et al. 2007). However, alkali catalysed

transesterification cannot be directly used to produce biodiesel from feedstocks


containing high levels of free fatty acids (FFA). This is because FFAs react with the

catalyst to form soap (Figure 5-6), resulting in emulsification, separation problems

and reduction in biodiesel yield. To overcome this problem, a pre-esterification

process may be used to reduce the content of FFAs in the feedstock. A typical pre-

esterification process uses homogeneous acid catalysts, such as sulphuric acid, or

heterogenous ‘solid-acid’ catalysts, to pre-esterify the free fatty acids (Zhang and

Jiang 2008b; Haas 2005; Samios et al. 2009b) as shown in Figure 5-7.

Figure 5-5-6: Soap formation in oils contains high FFA (Jahirul, et al. 2013)

Figure 5-5-7: Acid pre-esterification (Jahirul, et al. 2013)

A schematic of a two-step process of biodiesel production from high free fatty acid

Beauty leaf oil is shown in Figure 5-8. Both acid-catalysed pre-esterification and

base-catalysed transesterification were conducted in a 500 ml triple neck bottom

flask reactor shown in Figure 5-9a. An oil quantity of 40 g was used for the acid-

catalysed pre-esterification experiments and 30 g was used for each base-catalysed

transesterification trial. For each experiment, oil was carefully transferred into the

reaction flask and preheated in an oil bath to the reaction temperature. For acid-

catalysed esterification trials, sulphuric acid (H2SO4) was used as catalyst. The

sulphuric acid and methanol solution was freshly prepared and added to the


preheated oil and the mixture was agitated for 2 hours. At the completion of 2 hours,

the mixture was centrifuged in a self-standing tube for 7 minutes to separate the

methanol-water and esterified oil phases as shown in Figure 5-9b. The majority of

the excess methanol, sulphuric acid and impurities were separated into the top phase.

The bottom phase containing the oil was collected for base-catalysed trans-

esterification. The procedures were undertaken in triplicate and average values were

taken. It was found that after acid-catalysed pre-esterification, the acid value of

Beauty leaf biodiesel reduced to 5.14, 3.66, and 6.30 respectively for screw press,

ambient n-Hexane and ASE extracted oils.

In the base-catalysed transesterification trials, sodium methoxide (NaOCH3) was

used as catalyst with a reaction time of 1.5 hours. Similarly to the acid-catalysed pre-

esterification trials, the phases of the transesterification product were separated using

a centrifuge and the bottom layer drained using a separation funnel as shown in

Figure 5-9(c). The top layer containing crude Beauty leaf biodiesel was collected and

washed to remove the soap, unreacted methanol and other contaminant. All

experiments were undertaken in triplicate. The average methyl ester conversion for

the screw press, n-Hexane and ASE extracted Beauty leaf oils were 75.47%, 93.05%

and 83.76%, respectively. The results clearly indicated the dependency of the

biodiesel conversion process the on the presence of free fatty acid in the base oil.

Therefore after analysing these results, it is clear that methyl ester production

efficiency not only depends on feedstock, but also the oil extraction methods.


Figure 5-5-8: Two step bio-diesel production process from Beauty leaf oil

Figure 5-5-9: Beauty leaf oil esterification

(a) Esterification reactor (b) Layer of Methanol-Water (top) and oil (bottom) after

acid-catalysed pre-esterification; (c) Layer of Beauty leaf oil methyl ester (top) and glycerol (bottom) after base-catalysed Trans-esterification


5.8 BIODIESEL ANALYSIS

The chemical composition of biodiesels is very important for determining their

suitability for automobile engine application. Chemically, all biodiesels are mono

alkyl esters of fatty acids, commonly referred as fatty acid methyl or ethyl esters.

Depending on the feedstock and production process, the fatty acids are different in

relation to the chain length, degree of unsaturation or presence of other chemical

functions. Fatty acids are commonly designated by two numbers: the first number

denotes the total number of carbon atoms in the fatty acid and the second is the

number of double bonds. For example, 18:1 designates oleic acid which has 18

carbon atoms and one double bond. Table 5-3 shows the fatty acid methyl ester

composition of Beauty leaf oil (BLOME) produced through three different oil

extraction methods along with traditional biodiesel obtained from soybean (SOME),

canola (COME), palm (POME), rapeseed (ROME) and sunflower (SOME) oil

feedstocks. The FAME composition of Beauty leaf, soybean and canola biodiesels

were analysed by gas chromatography and flame ionisation detection (GC-FID) in

accordance with EN 14103 standards. The gas chromatograph (GC) was a Hewlett-

Packard 6890 System fitted with Varian Select TM 30 m × 0.32 mm × 0.25 µm

column. FAME compositions of other biodiesel were collected from literature [28].

The prominent fatty acids found in chemical composition of biodiesels were Palmitic

(Hexadecanoic, C16:0), Stearic (Octadecanoic, C18:0), Oleic (9-Octadecenoic,

C18:1) and Linolenic (9, 12-Octadecadienoic, C18:2) acids.

Three main types of fatty acids were found in the biodiesel samples: saturated

(Cn:0), monounsaturated (Cn:1) and polyunsaturated with two or three double bonds

(Cn:2,3). The percentage of these compounds for each vegetable oil is given in Table

6-3. Based on this composition, average chain length (ACL) and Average number of

double bond (ANDB) were estimated using equations 5-1 and 5-2.

∑ ∙ : 0, 1, 2, 3, .% ……………………………..Equation 5-1

1 ∙ : 1, . % 2 ∙ : 2, .% 3. : 3, %

100

……Equation 5-2


where n is the number of carbon atom in fatty acid chain.

Similarly to other biodiesels shown in Table 5-3, Beauty leaf oil biodiesels were also

high in Palmitic (C16:0), Stearic (C18:0), Oleic (C18:1) and Linolenic (C18:2) acids

esters. Mono-unsaturated stearic (C18:1) acid methyl ester is the most prominent

consisting of 38.6-40.29% by weight followed by poly-unsaturated Linolenic (22.81

- 27%), saturated Stearic (16.59 – 18.64%) and Palmitic (14.48-14.73%). Beauty leaf

oil biodiesels have higher saturated esters consisting of 32.7 – 34% which after palm

oil biodiesel which is 44.6%. However, Beauty leaf biodiesel showed higher long

chain saturation factor (10.72 – 11.81) over palm oil biodiesel (7.37). This is because

palm oil biodiesel is richer in short chain saturated Palmitic acid esters compared

with Beauty leaf biodiesel. Overall, the chemical compositions of Beauty leaf

biodiesels were closer to palm oil biodiesel than any other biodiesels shown in Table

5-3.


Table 5-3: The fatty acid distributions of Beauty leaf and commercial biodiesels

FAME Formula BLOME* SOME* COME* POME** ROME** SFOME**

OP ASE nHX Lauric C12:0 0 0 0 0 0.4 0.1 0 0 Myristic C14:0 0 0 0 0 0.53 0.7 0 0 Palmitic C16:0 14.73 14.48 14.68 13.04 13.19 36.7 4.9 6.2 Palmitoilic C16:1 0.19 0.22 0.24 0.28 5.6 0.01 0 0.1 Stearic C16:1 0 0.04 0 0 0 0 0 0 Oleic C18:0 16.59 18.64 18.25 6.32 3.04 6.6 1.6 3.7 Linoleic C18:1 39.3 40.29 40.18 26.59 47.1 46.1 33 25.2 Linolenic C18:2 27 22.81 23.23 45.34 27.2 8.6 20.4 63.1 Gondonic C18:3 0.28 0.17 0.19 6.9 5.23 0.3 7.9 0.2 Erucic C20:0 0.95 1.04 1.02 0.44 0.55 0.4 0 0.3 Lauric C20:1 0.29 0.24 0.23 0.3 0.95 0.2 9.3 0.2 Myristic C22:1 0 0 0 0 0 0 23.0 0.1 Saturated (wt%) 32.7 34.7 34.4 20.4 18.8 44.6 6.5 10.4 Mono-unsaturation (wt%) 39.86 40.88 40.51 27.28 53.97 46.31 65.30 25.30 Poli- unsaturation (wt%) 27.28 22.98 23.42 52.24 32.43 8.90 28.30 64.00 Average chain length (ACL) 17.74 17.74 17.74 17.76 17.67 17.28 19.01 17.94 Average number of double bond (ANBD)

0.95 0.87 0.87 1.32 1.19 0.64 1.22 1.53

* Experiment; **Literature (Singh and Singh 2010; Ramos et al. 2009; Hoekman et al. 2012)


5.9 FUEL PROPERTIES

Biodiesel properties from any types of feedstock need to meet the relevant quality

standard before being accepted as an acceptable automobile fuel. However, biodiesel

properties can vary substantially from one feedstock to the next due to differences in

the compositional profiles describe above. In some cases properties also vary in

similar feedstocks from different origins and production processes. However, the

quality standards are crucial for the commercial use of any fuel product, which

serves as guidelines for the production process, to assure customers are buying high-

quality fuels, and provide authorities with approved tools for the assessment of safety

risks and environmental pollution. The most internationally recognised biodiesel

standards are: EN14214 (in Europe) and ASTM D-6751 (in USA). Numerous other

countries have defined their own standard, which in many cases derive from either

EN14214 or ASTM D-6751. With the increasing production of biodiesel within

Australia and as a part of the Fuel Quality Standards Act 2000, the Australian

government has released a biodiesel fuel standard, “Fuel Standard (Biodiesel)

Determination 2003”. This standard is an adaptation of the above US and EU

standards and fuel standards differ only slightly to conform to Australian climate

related requirements. A summary of the important fuel quality parameters of Beauty

leaf oil biodiesels and conventional biodiesels across all three standards are shown in

Table 5-4. Among the fuel properties listed in the Table 5-4, kinematic viscosity,

density, higher heating value and acid value were obtained from experiment for

Beauty leaf biodiesels, soybean and canola biodiesel. A similar experimental

procedure was followed for these four parameters were described in the previous

section. For comparison purposes, experimental data for above mentioned parameters

were obtained from literature (Ramos et al. 2009) for palm, rapeseed and sunflower

oil biodiesel. The other fuel property parameters were estimated using ANN models

developed in Chapter 3.


5.9.1 Kinematic viscosity

Kinematic viscosity (KV) is an important property of biodiesel since it plays a

dominant role in the fuel atomisation, fuel-air mixture formation and combustion

process particularly at cold weather when an increase in viscosity affects the fluidity

of fuel. The higher the KV, the higher is the pressure loss in the fuel line and

injection pump, therefore resulting in increases in engine deposits and shoot

formation, requiring more energy to pump the fuel and increasing wear on fuel pump

elements and injectors. On the other hand, low fuel KV is not desirable because it

will not provide sufficient lubrication for the precision fit of fuel injection pumps,

resulting in leakage or increased wear (Jahirul, Brown, Senadeera, #039, et al. 2013).

Therefore, the upper and lower limit of biodiesel KV is defined in all biodiesel

standards shown in Table 5-4. The KV of produced Beauty leaf biodiesels was 4.38 -

4.46 mm2/sec which were at the acceptable limit according to all biodiesel standards.

Table 5-4 also shows that the KV of Beauty leaf, palm, rapeseed and sunflower oil

biodiesels were quite similar at around 4.4 mm2/sec whereas soybean oil biodiesel

has the lowest (3.86 mm2/sec) and cottonseed oil biodiesel has the highest kinematic

(5.45 mm2/sec). The Beauty leaf biodiesel made from oil through oil press showed

slightly higher KV compared with chemical oil extraction, which may be due to the

higher viscosity of the same feedstock. Overall there is only minor variation in KV

was found between three different Beauty leaf biodiesels.

5.9.2 Density

Density is defined as mass per unit volume of the liquid fuel commonly expressed in

units of kg/m3. Density is an important property for automobile fuel because it

influences the amount of fuel injected in the engine cylinder. Changes in fuel density

will influence engine output power due to a different mass of fuel injected which

directly affects engine performance. Comparing crude vegetable oil (Table 5-2) and

vegetable oil methyl ester (Table 5-4), it can be seen that esterification process

reduces the density by 7 to 8%. Beauty leaf biodiesel produced from oil obtained

through mechanical extraction showed slighter higher density compared with that of

other Beauty leaf biodiesels. However, the densities of all biodiesels shown in Table

5-4 were in the acceptable range specified by Australian and European biodiesel

standards.


5.9.3 Higher heating value

Higher heating value (HHV) is important fuel property for identifying the suitability

of biodiesel as an engine fuel. It indicates energy content in the fuel per unit mass.

Therefore, the conventional unit of higher HHV is KJ/gm or MJ/kg. The HHV of

Beauty leaf biodiesels were found to vary from 40.85 to 40.96 MJ/kg with very little

fluctuation among the beauty oil source. These results indicate that the HHV of

vegetable oil methyl ester is about 4% higher than the crude vegetable oil as show in

Table 5-2. Table 5-4 shows that the biodiesel produced from non-edible Beauty leaf

oil produced has a HHV close to that of commercial biodiesel produced from edible

vegetable oil feedstocks.

5.9.4 Acid number

Acid number (AN) indicates the amount of carboxylic acid present, such as in fatty

acids. It is expressed as the amount of KOH (mg) required for neutralising 1 g of

fatty acid methyl ester or biodiesel. Fuel with high acid number can cause higher

level of lubricant degradation and severe corrosion in engine fuel systems (Haseeb et

al. 2011a). AN is set to a maximum of 0.5 KOH/g in both European (EN14214) and

American (ASTM D6751) biodiesel standards, whereas the Australian standard

allow slightly higher AN, setting the maximum value at 0.8 KOH/g. Naturally, the

AN of crude Beauty leaf oils were very high compared with the traditional edible

vegetable oil shown in Table 5-2. Table 5-4 shows that a significant reduction of this

acid value occurred in the two stage biodiesel production process utilised in this

study. However the acid number of Beauty leaf oil biodiesels remained high when

compared with other commercial biodiesels. Table 5-4 indicates that oils obtained

through oil press, n-Hexane and ASE produced biodiesel were 0.88, 0.76 and 1.00

KOH/g respectively. Although only biodiesel from n-Hexane oil met the Australian

biodiesel standard (Table 5-4), the other biodiesels were only slightly higher than the

standard. It is likely that with further optimisation, biodiesel from Beauty leaf oil

should be able to meet EN standards.


5.9.5 Oxidation stability

Oxidation stability (OS) is a fuel property which reflects the resistance of the fuel to

oxidation during long-term storage. Usually biodiesels show less oxidative stability

compared with petroleum diesel due to their different chemical composition and this

is one of the major issues that limits the wide spread use biodiesel as a fuel in

automobile engines. All of the biodiesels listed in Table 5-4 failed to meet the ASTM

standard in terms of oxidation stability, which is 3 hours minimum. Only Beauty leaf

and palm biodiesel were in the range of European standard of oxidation stability.

Beauty leaf biodiesels showed oxidation stability from 4.12 to 4.42 hours which was

much higher than all conventional biodiesel except palm oil biodiesel. This is

because oxidation is influenced by the presence of double bonds in the chains, that is,

feedstocks rich in polyunsaturated fatty acids are much more susceptible to oxidation

than the feedstocks rich in saturated or monounsaturated fatty acids. For the same

reason, Beauty leaf biodiesel obtained from oil press showed less oxidation stability

then other types. An overall oxidation estimation result confirms that Beauty leaf

biodiesel is a better fuel in terms of OS then most of the commercial biodiesel.

5.9.6 Iodine value

Iodine value (IV) is an important parameter in regard to fuel quality because higher

IV biodiesel leads to a higher rate of polymerisation of glyceride which results in

increasing fuel viscosity, causing the formation of engine deposits, thus adversely

affecting fuel injector spray patterns. This property is set to a maximum value of 120

g I2 /100g according to EN14214 standard. The IV results tabulated in Table 5-4

indicate that all biodiesels meet the EN14214 standard except the sunflower oil

methyl ester. The IV of Beauty leaf oil biodiesels (74.81 – 81.44 I2 /100g) were well

below the allowable limit and also below most of the commercial biodiesel. Only

palm oil biodiesel showed better result than Beauty leaf biodiesel in IV which was

estimated 57 I2 /100g. Oil press Beauty leaf biodiesel showed slightly higher IV due

to having a higher degree of unsaturation compared to other types.


5.9.7 Cetane Number

Cetane number (CN) is a widely used diesel fuel quality parameter, and is a

measurement of the combustion quality of diesel fuels during compression ignition.

It is related to the ignition delay (ID) time, that is, the time that passes between

injection of the fuel into the cylinder and the onset of ignition (Knothe 2005). A high

CN will help to ensure good cold start properties and will minimise the formation of

white smoke. On the other hand, lower CN may result in diesel knocking and an

increase in exhaust emissions. Australian and European biodiesel standard limit the

CN to a minimum value of 51 whereas ASTM standard limit it minimum value of 47

as shown in Table 5-4. Results indicate an excellent ignition quality of biodiesel

produced from Beauty leaf oil biodiesels. The CN of Beauty leaf biodiesels were

58.53 to 60.39; much higher than the minimum recommended value of 51. Moreover

the CN of Beauty leaf biodiesels were far better than most of the commercial

biodiesel produced from edible oil. The CN of palm oil biodiesel was found to be

61.80, slightly higher than Beauty leaf oil biodiesel. This is because palm oil contains

a higher percentage of saturated methyl ester. Oil press biodiesel showed a slightly

lower CN then the other Beauty leaf oil biodiesels which may due to the higher

linoleic acid content, which increases the degree of unsaturation and hence reduces

the CN.

5.9.8 Flash point temperature

The flash point (FP) is defined as the lowest temperature at which the fuel will start

to vaporise to form an ignitable mixture when it comes in contact with air. Australian

and European biodiesel specification required flash point temperature at least 120 °C,

whereas in the US the minimum requirement level is 93 °C. Table 5-4 shows that the

FP temperature of Beauty leaf biodiesels were between 143.06 and 145.64 °C, which

is higher the minimum requirement specified in the biodiesel standards. While

comparing with commercial biodiesel, Beauty leaf biodiesel showed lower flash

point temperature. It is noted that very high flash point temperature of automobile

fuel is not desirable because it can cause cold engine start-up problems, misfiring and

ignition delay, which increases carbon deposition in the combustion chamber

(Szybist et al. 2007). No significant variation in FP temperature was noted among the

different Beauty leaf biodiesel results shown in Table 5-4.


5.9.9 Cold filter plug point (CFPP)

One of the major problems associated with the use of biodiesel in countries with a

cold climate is their poor cold flow properties when compared with petroleum diesel

fuels. The parameter generally used to determine the cold flow property is cold-filter

plugging point (CFPP). The CFPP is defined as the lowest temperature at which a

fuel portion will pass through a standardised filtering device in a specified time

(Jahirul, et al. 2013). The cold temperature properties of biodiesel should be reported

according to the Australian, European and US although the limits are not specified.

However it is commonly understood that biodiesels with low CFPP, CP and PP are

better options for diesel engine fuels operating in cold weather condition. Table 5-4

shows that all cold temperature properties of Beauty leaf biodiesels were much

higher than that from most commercial biodiesel. Among the biodiesel shown in

Table 5-4, palm oil biodiesel showed highest CFPP temperature followed by Beauty

leaf oil biodiesel. The rapeseed oil biodiesel followed by soybean oil biodiesel

showed the lowest cold temperature properties. The average CFPP of Beauty leaf

biodiesel was found 3.5, 12.6 and -2.9 °C, respectively. Beauty leaf oil produced

through oil press showed slightly better cold weather properties due to having a

higher linolenic acid methyl ester content.


Table 5-4: Fuel properties of Beauty leaf oil biodiesel and commercial biodiesels

Property Unit Biodiesel Standard BLOME* SOME* COME* POME** ROME** SFOME**

Australian ASTM D6751

EN 14214 OP ASE nHX

Kinematic viscosity @40°C

mm2/sec 3.5-5 1.9-6 3.5-5 4.46 4.34 4.38 3.86 5.45 4.5 4.4 4.2

Density kg/m3 .860-900 n/a 860-900 0.894 0.892 0.893 0.863 0.871 0.874 0.877 0.880

HHV Mj/kg n/a n/a n/a 40.85 40.52 40.46 40.78 41.59 41.24 41.55 41.26

Acid number mg KOH/g .8, max 0.5, max 0.5, max 0.88 1.00 0.76 0.34 0.91 0.12 0.16 0.15

Oxidation stability hours n/a 3, min 6, min 4.14 4.44 4.42 2.71 3.21 5.31 3.09 1.88

Iodine value g iod/ 100g n/a n/a 120, max 81.44 74.81 75.24 119.47 107.07 57 109 132

Cetane Number - 51 min 47, min 51 min 58.53 60.42 60.39 47.89 49.16 61.80 52.02 44.90

Linolenic acid content

%(m/m) n/a n/a 12, max 0.28 0.17 0.19 6.9 5.23 0.3 7.9 0.2

Flash point °C 120, min 93, min 120, min 145.64 143.06 143.65 160.87 162.00 176 170 177

CFPP °C Report Report Report 2.45 4.11 3.92 -5.76 -2.94 10 -10 -3 *Experimental; **Literature (Ramos et al. 2009; Singh and Singh 2010; Hoekman et al. 2012)


5.10 VALIDATION OF BEAUTY LEAF BIODIESEL

To be an ideal source of biodiesel, Beauty leaf biodiesel should have suitable

chemical composition to ensure compliance with standard biodiesel properties. The

fuel properties of Beauty leaf biodiesel from three different extraction methods were

analysed and compared with five other commercially available biodiesels. To

determine the suitability of Beauty leaf biodiesels compared to other biodiesels based

on 14 criteria (fuel properties): CN, IV,OX, AN, HHV, KV, Density, FP, CFPP,

Linolenic acid, ACL, MUFA and PUFA, a multi-criteria decision method (MCDM)

software was used. In this study, the Preference Ranking Organisation Method for

Enrichment Evaluation (PROMETHEE) and Graphical Analysis for Interactive

Assistance (GAIA) were used because of their rational decision vector which

stretches towards the preferred solution compared to other MCDM (Brans and

Mareschal 1994).

Table 5-5: Variables and preference used in PROMETHEE-GAIA analysis

Variables Preference For PROMETHEE‐GAIA

Kinematic viscosity (KV) Min Density Min Higher heating value (HHV) Max Acid number (AN) Min Oxidation stability (OX) Max Iodine value (IV) Min Cetane Number (CN) Max Linolenic acid (LA) Min Flash point (FP) Max Cold filter plug point (CFPP) Min

In GAIA plane, the criteria which lie close to (±45°) are correlated, while those lying

in opposite directions (135–225°) are anti-correlated, and those in a roughly

orthogonal direction have no or less influence (Espinasse, Picolet and Chouraqui

1997). The preference function criteria (fuel property) were modelled as minimum

(i.e. lower values are preferred for good biodiesel) or maximum (higher values are

preferred for good biodiesel). The selection of preference function also influences the


direction of criteria. For example, IV and CN were inversely related but still showed

the same direction within ±45○. This is because the Cetane number were preferred to

maximum, but iodine number was preferred to minimum, as shown in Table 5-5,

which was suggested by Islam et al. (2013). Therefore, criteria which are in the same

preference (min/max) and lie close to ±45° are correlated. The direction and length

of criteria are indicative to their influence on decision vector (marked as red line in

Figure 5-10) (Islam et al. 2013), such that the very short length of some criteria, in

particular ‘Density’ and ‘HHV’, indicate the little effect on the decision vector.

The decision vector indicates the most preferable samples (i.e. those that align with

the direction of this vector) and the outermost criteria in the direction of the decision

vector are the most preferable (Figueira, Greco and Ehrgott 2005). In this study,

equally weighted criteria showed (Figure 5-10a) that POME was most aligned with

the decision vector and its farthest position from the centre gave it the highest

ranking.

Rank Biodiesel Phi

1 POME 0.16

2 ROME 0.05

3 BLOME_OP 0.02

4 BLOME_nHX 0.01

5 BLOME_ASE -0.01

6 COME -0.06

7 SFOME -0.08

8 SOME -0.10

(a) (b)

Figure 5-5-10: (a) Graphical Analysis for Interactive Assistance (GAIA) plot for

eight biodiesel showing 10 criteria and decision vector. (b) Corresponding ranking of

biodiesel on their outranking flow.


Figure 5-10(b) shows the overall ranking of the different biodiesel and the three

biodiesel from Beauty leaf, BLOME_OP, BLOME_nHX and BLOME_ASE, were

placed third, fourth and fifth, respectively, in the middle of the overall rankings. The

Phi value is the net flow score which could be negative or positive depending upon

the angular distance from the decision vector and the distance from the canter.

Biodiesel from soybean oil was at the bottom of the ranking compared with other

biodiesel. In can be seen from Figure 10 that the quality of Beauty leaf biodiesel in

terms of fuel properties did not depends on oil extraction methods. The results of this

analysis indicate the ability of Beauty leaf biodiesel to compete with commercially

available first- generation biodiesels.

Table 5-6: Comparative rank shift with different OS and CFPP weighting

OS CFPP Weighting 1‐3 4‐6 6‐10 1‐2 3‐4 5‐6 7‐10

POME 1 1 ‐ 1 ‐ 1 5 7 8

ROME 2 5 6 2 1 1 ‐ 1 ‐ BLOME_OP 3 2 2 3 6 6 ‐ 6 ‐

BLOME_nHX 4 3 3 4 7 8 7

BLOME_ASE 5 4 4 5 8 5 5 ‐

COME 6 6 ‐ 5 6 3 3 ‐ 3 ‐

SFOME 7 7 ‐ 7 ‐ 7 4 4 ‐ 4 ‐

SOME 8 8 ‐ 8 ‐ 8 2 2 ‐ 2 ‐

Black arrows upward: rank increase; Black arrows downward reduce rank; Hyphen: no ranking change

The quality ranking analyses of biodiesel shown in the previous section was

conducted with an equal weighting of all parameters. However, the importance of

some fuel properties depends on the country and place where it will be used and

stored. In tropical/sub-tropical regions, CFPP was not considered to be of importance

here. Elevated temperatures of these regions are, however, likely to affect oxidative

stability of the biodiesel. On the other hand, in the winter climate condition CFPP are

more important than oxidation stability. Therefore, ranking sensitivity analysis was

conducted for the fuel properties CFPP and OS by increasing the weighting from 1

(equal to other parameters) to 10, and the results are shown in the Table 5-6. A

significant change in ranking was found for both OS and CFPP. POME always


ranked 1 with the increasing of OS weighting. At the same time, the rank of Beauty

leaf biodiesels improved and was ranked just after POME. In contrast, the rank of

ROME dropped dramatically with the weighting increase of OS. On the other hand,

an opposite trend was observed when the weighting was increased for CFPP. Both

POME and Beauty leaf biodiesels dropped in rank and were placed at the bottom in

the ranking table. Therefore, as for palm oil biodiesel, Beauty leaf oil biodiesels are

unlikely to be suitable for cold climate conditions, especially in winter. These results

indicate that Beauty leaf biodiesels are a better choice for tropical/sub-tropical

regions than colder climate conditions.

5.11 CONCLUSION

Second-generation biodiesel is gaining more interest in the market as a sustainable

alternative of diesel fuel. However, to produce biodiesel from new sources and

continue to develop these in the market, various aspects must be examined. In this

study, the potential of Beauty leaf plant was evaluated as a source of second-

generation biodiesel. Oil was extracted from dry seed kernels using 3 different oil

extraction methods and oil properties have been analysed. Oil has been esterified to

produce biodiesel using a two-step esterification technique and the physico-chemical

properties were assessed. From the results obtained in this study the following

conclusion can be made.

By flowering two seasons in a year, Beauty leaf plant is able to produce a large

amount of seeds that contain non-edible oil. Due to the variability in size of the

seeds, having relatively soft and high moisture containing (about 32% by weight) oil

bearing kernels, special care need to be taken during the seed cracking process. This

will prevent damage to the kernels and reduce oil loss. The conventional seed

cracking methods using mallets and a stomper were able to produce good quality

kernels but those processes were found to be slow, labour-intensive and might be not

suitable for large scale production, processing approximately 2–3 kg of seeds per

operator per hour. Therefore, an automated seeds cracking device needs to be

designed for industrial scale production using Beauty leaf oil seeds.


This study also found that all oil extraction methods had several advantages and

disadvantages in terms of oil production from Beauty leaf oil seeds, which are

summarised in Table 1. The performance of Beauty leaf oil extraction using an oil

press resulted in a low oil yield. This drawback was overcome using chemical oil

extraction using n-hexane as oil solvent. Furthermore, the oil yield further increased

by 3-4% with high pressure and temperature extraction. The highest oil yield was

found on average 51.5% of dry kernels in ASE extraction method, which suggested

that Beauty leaf plant is able to produce about 1.56 tons of oil per year per hectare.

When comparing quality with edible vegetable oils, conventionally used as biodiesel

feedstock, in terms of acid value, density, kinematic viscosity, surface tension and

higher heating value, Beauty leaf oil showed much higher acid values resulting from

high free fatty acid contents. Chemical oil extraction under atmospheric conditions

produced oil containing relatively low levels of free fatty acids. However, those

results have illustrated that raw Beauty leaf oil may not suitable for direct use in

diesel engines. Another drawback of Beauty leaf oil is that conventional base-

catalysed transesterification cannot be used directly for biodiesel production.

Therefore, a two-step esterification process, involving acid-catalysed pre-

esterification and base-catalysed trans-esterification, was used in this study. during

the first stage of this process, the acid value was significantly reduced to the

acceptable limit for base-catalysed trans-esterification. The highest biodiesel

conversion efficiency was found to be 93.05% for the oil produced by chemical oil

extraction in atmospheric condition, whereas oil obtained from screw press and ASE

methods showed 75.74% and 83.76%, respectively, under similar reaction

conditions, which is due to variations in the acid value of the respective oils.

Beauty leaf oil biodiesels mostly comprise esters of saturated Hexadecanoic (C16:0)

and Octadecanoic (C18:0) acid, mono-unsaturated 9-Octadecenoic acid (C18:1) and

poli-unsaturated 9, 12-Octadecadienoic (C18:2). This biodiesel is rich in saturated

methyl esters compared with commercial biodiesels, except biodiesel from palm oil

and is also rich in long chain saturation factors. Like palm oil, this makes Beauty leaf

oil biodiesel better in terms of most of fuel properties, including kinematic viscosity,

density, higher heating value, oxidation stability, iodine value, cetane number, flash


point, linoleic acid content. On the other hand, Beauty leaf biodiesels are perform

worse in terms of cold temperature properties and free fatty acid content. However,

Beauty leaf biodiesel is able to meet the American, European and Australian

biodiesel standards. The multivariate data analysis using PROMETHEE-GAIA

software indicated that biodiesel from Beauty leaf oil could be a better option for

automobile engine application compared with many other commercial biodiesel,

including biodiesel from cotton seed, sunflower and soybean oil, specially in

tropical/sub-tropical regions.

Chapter 6: Production process optimisation of biodiesel 157

Chapter 6: Production process optimisation of biodiesel

Biodiesel Production from Non-Edible Beauty Leaf

(Calophyllum inophyllum) Oil: Process Optimisation Using

Response Surface Methodology (RSM)

Md I. Jahirul , Wenyong Koh, Richard J. Brown , Wijitha Senadeera , Ian O’Hara

and Lalehvash Moghaddam

Publication: Journal of Energies. Vol. 7(8), pp. 5317-5331, 2014.

http://www.mdpi.com/1996-1073/7/8/5317

Author Contribution


Jahirul M. I Conducted the experiments, performed the data analysis and drafted the manuscript Signature

Wenyong Koh Assisted with conducting the experiment, performed the data analysis and drafted the manuscript

Brown R. J Supervised the project, aided with the data analysis, development of the paper and extensively revised the manuscript

Senadeera W Supervised the project, aided with the development of the paper

Moghaddam L Assisted with conducting the experiment





Name


Signature

Date

158 Chapter 6: Production process optimisation of biodiesel

Abstract

In recent years, the Beauty leaf plant (Calophyllum Inophyllum) is being considered

as a potential 2nd generation biodiesel source due to high seed oil content, high fruit

production rate, simple cultivation and ability to grow in a wide range of climate

conditions. However, however, due to the high free fatty acid (FFA) content in this

oil, the potential of this biodiesel feedstock is still unrealised, and little research has

been undertaken on it. In this study, transesterification of Beauty leaf oil to produce

biodiesel has been investigated. A two-step biodiesel conversion method consisting

of acid catalysed pre-esterification and alkali catalysed transesterification has been

utilised. The three main factors that drive the biodiesel (fatty acid methyl ester

(FAME)) conversion from vegetable oil (triglycerides) were studied using response

surface methodology (RSM) based on a Box-Behnken experimental design. The

factors considered in this study were catalyst concentration, methanol to oil molar

ratio and reaction temperature. Linear and full quadratic regression models were

developed to predict FFA and FAME concentration and to optimise the reaction

conditions. The significance of these factors and their interaction in both stages was

determined using analysis of variance (ANOVA). The reaction conditions for the

largest reduction in FFA concentration for acid catalysed pre-esterification was 30:1

methanol to oil molar ratio, 10% (w/w) sulphuric acid catalyst loading and 75 °C

reaction temperature. In the alkali catalysed transesterification process 7.5:1

methanol to oil molar ratio, 1% (w/w) sodium methoxide catalyst loading and 55 °C

reaction temperature were found to result in the highest FAME conversion. The good

agreement between model outputs and experimental results demonstrated that this

methodology may be useful for industrial process optimisation for biodiesel

production from Beauty leaf oil and possibly other industrial processes as well.

Keywords: biodiesel; Beauty leaf; trans-esterification; response surface

methodology (RSM)


6.1 INTRODUCTION

The current global energy supply is heavily dependent on finite reserves of fossil

fuels (oil, natural gas, coal) which represent 88% of total global energy consumption.

Based on current production scenarios, it is expected that the peak of global oil

production will occur between 2015 and 2030 (Jahirul, Brown, Senadeera, O'Hara, et

al. 2013). Therefore, fossil resources have practical limitations in their capacity to

supply future global energy requirements, and there are currently few large scale

alternatives available. Moreover, combustion of fossil fuels results in greenhouse gas

emissions and contributes to anthropogenic climate change. Despite global measures

such as the Kyoto Protocol and scientific innovation, atmospheric CO2 concentration

continues to increase and is exceeding benchmark levels much earlier than had

previously been predicted (Weitzman 2007).

With a growing world population, increasing energy consumption per capita, and the

impacts of global warming resulting from greenhouse gas emissions, the need for

long-term alternative energy source is acute (Jahirul et al. 2012; Jahirul et al. 2007;

Jahirul et al. 2010). Over the past few decades, biodiesel produced from oilseed

crops and animal fat is receiving much attention as a renewable and sustainable

alternative for automobile engine fuels, particularly for petroleum diesel (Reijnders

2006). It is currently produced in commercial quantities from edible

oil feedstocks such as soybean, palm, rapeseed and canola oil. Biodiesels produced

from these feedstocks are generally referred to as first-generation biodiesels (Rashid

and Anwar 2008b). Although biodiesels from these feedstocks offer reductions in

greenhouse gas emissions (GHG) and improve domestic energy security, first-

generation biodiesels are unlikely to be sustainable in the longer term due to land use

impacts and the price and social impacts associated with using a food-based

feedstock. Second-generation biodiesels produced from non-edible feedstocks have

the potential to overcome the disadvantages associated with first-generation

feedstocks, while addressing many of the climate change and energy availability

challenges (Posten and Schaub 2009).


Vegetable oils are extremely viscous, ranging from 10 to 17 times higher viscosity

than that of petroleum diesel (Sinha, Agarwal and Garg 2008; Haseeb et al. 2011b).

This makes the raw oils unsuitable for direct use as a fuel in a modern diesel engine.

As a consequence, researchers and scientists have developed various methods to

reduce the viscosity of bio-oils to make them suitable for diesel engine use. Some of

these methods include dilution with other fuels, trans-esterification, micro-

emulsification, pyrolysis and catalytic cracking (Lin et al. 2011). Among these

techniques, transesterification is the most widely used solution due to its high

conversion efficiency, simplicity, low conversion cost and the good fuel qualities of

the product (Fernando et al. 2007).

Transesterification is a chemical reaction in which oils (triglycerides) react with

alcohols (e.g., methanol, ethanol) under acid or alkali catalysed conditions,

producing fatty acid alkyl esters and glycerol. A catalyst is used to improve the

reaction rate and ester yield. Because the transesterification reaction is reversible,

excess alcohol is used to shift the equilibrium to favour production of esters. After

the reaction is completed, glycerol is removed as a by-product and the esters are

purified into biodiesel (Fernando et al. 2007).

One limitation with the alkali catalysed transesterification process is that this process

is not suitable for vegetable oils containing high levels of free fatty acids (FFA). This

is because FFAs react with the catalyst to form soaps, resulting in emulsification and

separation problems (Rajendra, Jena and Raheman 2009). In addition excessive soap

formation reduces biodiesel yield and obstructs subsequent purification processes

including glycerol separation and water washing (Lam, Lee and Mohamed 2010).

However, the maximum limit of FFA in vegetable oil for alkali catalysed

transesterification is still uncertain with different benchmarks being reported. For

example, Van Gerpan reported that vegetable oils containing up to 5% FFA can be

trans-esterified using an alkali catalyst while Dorodo et al. (Dorado et al. 2002) and

Ramadhas et al. (Ramadhas, Jayaraj and Muraleedharan 2005) reported that FFA

content should not be greater than 3% and 2%, respectively. Many researchers have

also reported that FFA should be kept less than 1% for alkali catalysed

transesterification (Kumar Tiwari, Kumar and Raheman 2007; Ma and Hanna 1999;


Zhang et al. 2003). In order to overcome the difficulties related to trans-esterifying

high FFA oils, a pre-esterification process can be used in which a homogeneous acid

catalysed process is used prior to transesterification (Lam, Lee and Mohamed 2010;

Zhang and Jiang 2008a).

The yield and quality of biodiesel are affected by several pre-esterification and

transesterification reaction parameters such as the quantity of alcohol, reaction

temperature, FFA content of the oil and the type and concentration of catalyst (Balat

and Balat 2008; Demirbas 2008a). For the stoichiometric transesterification reaction,

three moles of methanol are required per mole of triglyceride to yield three moles of

methyl esters and one mole of glycerol. The theoretical molar ratio of methanol to

triglyceride should, therefore, be 3:1 (Ma and Hanna 1999). However, the ratio of

alcohol to oil used in the reaction is much higher than this to promote complete

conversion of oils to FAME and varies with oil quality and the type of catalyst used.

For example, the molar ratio of alcohol to oil for alkali catalysed reactions is

typically 6:1, and for acid catalysed reactions it may be 15:1 or higher. An increase

in the concentration of catalyst generally increases the conversion of triglycerides

into fatty acid esters (Ma and Hanna 1999). Insufficient catalyst leads to an

incomplete conversion reaction and lower levels of fatty acid esters, whereas excess

catalyst has a negative impact on end product yield, because of the formation of

soaps.

On the other hand, a higher reaction temperature increases the reaction rate and

decreases the reaction time due to the reduction in viscosity of the oils. High reaction

temperatures above optimal levels, however, leads to a decrease in biodiesel yield, as

higher reaction temperatures accelerate the saponification of triglycerides (Leung and

Guo 2006b). Therefore, researches seek to optimise the important reaction

parameters for different biodiesel feedstock in order to achieve an efficient and

economical biodiesel production process.

The availability and price of feedstock are significant factors as feedstock cost

represents approximately 75%–88% of the total biodiesel production cost (Bozbas


2008; Haas et al. 2006). However, there are vast areas of grazing (e.g., cleared) and

degraded (e.g., mined) land on which biodiesel crops can be successfully established

for complementing fuel supplies. In a recent study, a number of species have been

found suitable for growth on degraded land which has the capacity for producing a

considerable amount of non-edible oil for biodiesel production (Ashwath 2010b).

Among these species, Beauty leaf has been identified as the most suitable feedstock

for future generation biodiesel (Ashwath 2010b; Jahirul, Brown, Senadeera,

Ashwath, et al. 2013). It is a moderately sized tree that grows between 8–20 m tall

and is most notable for its decorative leaves and fragrant flowers. The tree grows in

tropical and sub-tropical climates close to sea level. It is a moderately quick growing

tree reaching up to 1 m tall within a year. It has also been seen to flourish even with

the presence of weeds and other species, so the plant can be grown in mixed cultures.

The Beauty leaf tree has the ability to produce about 4800 kg of non-edible oil per

year per hectare (Jahirul, Brown, Senadeera, Ashwath, et al. 2013). However, the

potential of Beauty leaf as a source of future generation biodiesel is yet to be

established in part due to a lack of knowledge of its optimum production process.

Response surface methodology (RSM) is a collection of mathematical and statistical

techniques that are useful for modelling, analysis and optimisation problems in

which the response of interest is influenced by several factors (Jeong and Park 2009;

Vicente et al. 1998). In this technique, a well-designed experiment can substantially

reduce the number of tests, and yet provide the essential information required for

process optimisation. RSM uses statistical methods for experimental design to

identify important factors by characterising the response surface using a polynomial

model (Ferella et al. 2010). In the practical application of RMS it is necessary to

develop a statistically valid approximating model for the true response surface. The

relationship of the response variable Y and the dependent variables X1, X2, … Xk, in

the RSM application is generally expressed as in the following equation (Saidur et al.

2008):

, , …………… . . Equation 6-1

Where, E is the noise or error observed in the response Y; and f is the response

surface.


This study aims to investigate the effect of several reaction parameters on the

production of biodiesel from high FFA vegetable oil obtained from the Beauty leaf

seed. This study also implemented RSM in developing linear and full quadratic

polynomial equations for predicting FFA and FAME content and predicting the

optimum reaction condition for pre-esterification and transesterification processes.

6.2 MATERIALS AND METHOD

6.2.1 Beauty leaf oil extraction method

The Beauty leaf oil used in this study was obtained through a chemical oil extraction

methods using n-hexane as a solvent. In this process, dried seed kernels were ground

using a blender and coffee grinder to obtain a fine consistency to maximise particle

surface area. The ground kernels were then put into conical flasks into which n-

hexane was added at a ratio of 1.6:1 by weight (n-hexane:seed kernels). The mixture

was given an initial stir to ensure that all kernels were wetted with hexane. The

conical flask openings were covered with aluminium foil and placed on laboratory

scale orbital mixer in a fume hood, and the samples were extracted for at least 8 h

with 150 rpm shaking speed. Following extraction, the hexane/oil mixtures were

collected, filtered and decanted into aluminium foil containers for solvent

evaporation, and placed into the fume hood for 8 to 10 h. Hexane was again added to

the conical flask of kernels, but at a ratio of 8:1 (weight) for the second extraction,

and a similar procedure was followed for recovery of the oil. When it was

determined that the hexane had been fully evaporated, the raw Beauty leaf oil was

collected.

6.2.2 Analysis methods

FFA content of the Beauty leaf oil was analysed using D5555-95 (2011) standard test

method. Ester content of the FAME was analysed by gas chromatography and flame

ionisation detection (GC-FID) in accordance with EN 14103 standards. The gas

chromatograph (GC) was a Hewlett-Packard 6890 System fitted with Varian


Select™ 30 m × 0.32 mm × 0.25 µm columns. Oil density and surface tension were

analysed following ASTM D1298 and ASTM D971-12 standard test methods using a

KSV Sigma 702 Tensiometer Viscosity was measured using Brookfield DV-III

Rheometer and following the ASTM D445 standard test method. The fatty acid

compositions of the oils were analysed using a Hewlett Packard Plus 6890 series GC-

FID and a capillary column of acidified polyethylene glycol (HP-INNOWax

19091N-133, 30 m × 250 μm × 0.25 μm).

6.2.3 Pre-esterification and transesterification methods

Both acid-catalysed pre-esterification and base-catalysed transesterification were

conducted in a 500 mL triple neck bottom flask reactor (Figure 6-1a). An oil quantity

of 40 g was used for the acid-catalysed pre-esterification experiments, and 30 g was

used for each base-catalysed transesterification trial. For each experiment, oil was

carefully transferred into the reaction flask and preheated in an oil bath to the

reaction temperature. For acid-catalysed esterification trials, sulphuric acid (H2SO4)

was used as catalyst. The sulphuric acid and methanol solution were freshly prepared

and added to the preheated oil, and the mixture was agitated for two hours. At the

completion of the two hours, the mixture was centrifuge in a self-sanding tube for 7

min to separate the methanol-water and esterified oil phases as shown in Figure 6-1b.

The majority of the excess methanol, sulphuric acid and impurities were separated

into the top phase. The bottom phase containing the oil was collected for base-

catalysed trans-esterification.


Figure 6-6-1: (a) Esterification and transesterification reactor; (b) Layer of

Methanol-Water (top) and oil (bottom) after acid-catalysed pre-esterification; (c)

Layer of Beauty leaf oil methyl ester (top) and glycerol (bottom) after base-catalysed

Trans-esterification.

Table 6-1: Experimental range and levels of independent variables

Variables Unit Symbol coded Range & levels

−1 0 1

Acid-catalysed pre-esterification

MeOH: Oil mole M 10:1 20:1 30:1 H2SO4 wt% oil C 5 10 15

Temperature °C T 45 60 75

Base-catalysed transesterification

MeOH: Oil mole M' 4:1 6:1 8:1 CH3ONa wt% oil C' 0.6 0.8 1

Temperature °C T' 45 60 75

Table 6-2: Coded experimental design

Run

Acid-catalysed pre-esterification

Base-catalysed trans-esterification

M C T M' C' T'

1 0 −1 1 0 0 0 2 −1 −1 0 1 0 −1 3 −1 0 −1 −1 −1 0 4 1 −1 0 −1 1 0 5 0 0 0 −1 0 −1 6 1 1 0 −1 0 1 7 0 1 1 1 1 0 8 0 0 0 0 1 1 9 0 0 0 0 −1 −1

10 0 −1 −1 0 −1 1

(a) (b) (c)


11 0 1 −1 1 0 1 12 −1 0 1 1 −1 0 13 1 0 −1 0 0 0 14 −1 1 0 0 0 0

15 1 0 1 0 1 -1

In the based-catalysed transesterification trials, sodium methoxide (NaOCH3) was

used as a catalyst with a reaction time of 1.5 h. Similarly to the acid-catalysed pre-

esterification trials, the phases of the transesterification product were separated using

a centrifuge and the bottom layer drained using a separation funnel as shown in the

Figure 6-1c. The top layer containing Beauty leaf methyl ester was collected for

analysis.

Experiments were carried out according to a Box-Behnken response surface design

which involves 3 factors and requires 3 levels and a total of 15 runs. The factors and

the ranges and levels used in this study are shown in Table 6-1. The Minitab 16

statistical software package was used to randomly generate runs orders of the

experiments which are shown in Table 6-2.

Since the key focus of acid-catalysed esterification reaction is on reducing the free

fatty acid content to be <3–5 wt%, a full quadratic model was used for statistical

analysis in order to correlate the %FFA with the operating variables. The form of the

full quadratic model for the first step is as shown in Equation (6-2):

2 2 20 1 1 2 2 3 3 1,2 1 2 1,3 1 3 2,3 2 3 1,1 1 2,2 2 3,3 3Y X X X X X X X X X X X X

………………………..Equation 6-2

Where, Y is the %FFA; β0 is a constant; β1, β2, β3 are regression coefficients and X1,

X2, X3 are independent variables. For the base-catalysed transesterification reaction,

the same form of the full quadratic model is used; however, in this case Y is the ester

content of the biodiesel (%).



6.3.1 Beauty leaf oil characterisations

The quality of Beauty leaf oil has been characterised in terms of chemical

composition and physical properties in order to identify its suitability as a feedstock

for biodiesel for diesel engine combustion. The results of analyses of the chemical

composition and properties of the crude Beauty leaf oil used in this study are shown

in Tables 6-3 and 6-4. The compositional analysis shows that Beauty leaf oil contains

high levels of stearic (C18:0), oleic (C18:1) and linoleic (C18:2) acids (Table 6-3).

This indicates the potential for high combustion quality and hence suitability of

Beauty leaf oil as a fuel (Refaat 2009b). However, kinematic viscosity and surface

tension are significantly higher than other oils which may lead to poor atomisation

and volatility characteristics. Therefore, Beauty leaf oil may not be suitable as fuel

for direct use in conventional diesel engines (Balat and Balat 2008).

Table 6-3: Fatty acid composition of Beauty leaf oil

Fatty Acid Weight percentage

Palmitic, C16:0 13.66 Palmitoleic, C16:1 0.24 Heptadecanoic, C17:0 0.15 Heptadecanoic, C17:1 0.06 Stearic, C18:0 16.55 Oleic, C18:1 42.48 Linoleic, C18:2 25.56 Linolenic, C18:3 0.20 Arachidic, C20:0 0.87 Arachidonic, C20:1 0.23

Table 6-4: Properties of Beauty leaf oil.

Properties Values

Density, kg/m3 936 Surface tension (mN/s) 35.6 Kinematic Viscosity @40 °C, cSt 40.05 Free fatty acid (wt%) 12


On the other hand, the FFA content of Beauty leaf oil was 12% (w/w) of oil (Table 6-

4) which is much higher than the recommended FFA content of vegetable oil for the

base catalysed transesterification (Dorado et al. 2002; Ramadhas, Jayaraj and

Muraleedharan 2005). To overcome this high FFA level, biodiesel production was

conducted in two processing steps as described. For both pre-esterification and trans-

esterification, final acid value and production yield has been optimised, and a

statistical production model has been developed.

6.3.2 Acid-catalysed pre-esterification

Table 6-5 summarises the experimental conditions and results from each pre-

esterification experimental run. The results indicate a significant reduction in FFA

content of the Beauty leaf oil following acid-catalysed pre-esterification. FFA

content of the pre-esterified samples ranged from 3.25 to 1.83. The minimum FFA

content resulted from the esterification condition with 30:1 MeOH to oil molar ratio,

10% weight concentration of catalyst and 75 °C reaction temperature (Test 15). An

FFA content of less than 2% was also achieved for Test 6 (30:1 MeOH to oil molar

ratio, 15% weight concentration of catalyst and 60 °C reaction temperature). Based

on the experimental results, a linear and a quadratic equations have been developed

using Minitab 16 software in order to predict the FFA percentages as a function of

methanol to oil molar ratio, catalyst concentration and reaction temperature in acid

catalyst esterification. The developed quadratic models equations are shown in

Equations (6-3) and (6-4):

FFA(%) 3.97617 0.0528 0.0055 0.00542M C T ……………..Equation 6-3

2 2 2

FA(%) 1.493 0.00498 0.02487 0.06596 0.000085 0.0007083

0.0002233 0.0001554 0.001558 0.000458

M C T MC MT

CT M C T

……….Equation 6-4


Table 6-5: Experimental conditions and results for acid-catalysed pre-esterification

Test MeOH: Oil Molar

Ratio H2SO4 Conc.

(wt%) Temp (°C) FFA (wt%)

1 20 5 75 2.39 2 10 5 60 3.25 3 10 10 45 2.88 4 30 5 60 2.10 5 20 10 60 2.61 6 30 15 60 1.97 7 20 15 75 2.39 8 20 10 60 2.61 9 20 10 60 2.53

10 20 5 45 2.61 11 20 15 45 2.67 12 10 10 75 3.02 13 30 10 45 2.12 14 10 15 60 3.10 15 30 10 75 1.83

Table 6-6 summarises the regression coefficient generated using Minitab 16

software. The significance of each coefficient in this equation was evaluated by

ANOVA using Minitab software in terms of the p-value. Low p-value indicates that

the corresponding coefficient is significant. In the linear model shows that the

methanol to oil ratio (MeOH:Oil) was the most significant with p-value of 0 followed

by temperature and catalyst concentration (H2SO4) with the p-value of 0.07 and

0.511 respectively. For the full quadratic model, temperature was the most

significant with p-value of 0.069. Following this, the interaction effect between

methanol to oil molar ratio and temperature was most significant with p-value of

0.08. Finally, the quadratic effect of temperature was most significant with p-value of

0.097. Table 6-6 also shows that all p-values were fairly high, considerably above

0.05 which infer that the coefficients in full quadratic model are not statistically

insignificant in 95% confidence interval.

The accuracy of the prediction model obtained by the regression analysis was

verified by a scattered diagram (Figure 6-2), where experimental results for FFA

were compared to predicted values from the model. In linear model, the regression


coefficient (R2) and the adjusted regression coefficient (R2 (adj)) of 0.983 and 0.949

shows a good fit between actual and predicted results whereas in full quadratic

model, those parameters were 0.981 and 0.949 respectively. Therefore, it is apparent

that the linear model is statistically more appropriate than that of full quadratic

model. Moreover, the full quadratic model is over-specified as none of the

coefficients are significant in 95% confident interval.

Table 6-6: Regression coefficients for %FFA prediction

Predictor Linear Full quadratic

Coefficient p-value Coefficient p-value

Constant 3.97617 0 1.493 0.212 MeOH:Oil (M) −0.0528 0 −0.00498 0.874

H2SO4 (C) −0.0055 0.511 −0.02487 0.695 Temp (T) −0.00542 0.07 0.06596 0.069

MeOH:Oil × H2SO4 (MC) 0.000085 0.934 MeOH:Oil × Temp (MT) −0.0007083 0.080

H2SO4 × Temp (CT) −0.0002233 0.744 MeOH:Oil × MeOH:Oil (M2) −0.0001554 0.771

H2SO4 × H2SO4 (C2) 0.001558 0.476

Temp × Temp (T2) −0.000458 0.097

Figure 6-6-2: Scatter diagram of experimental FFA (%) and predicted FFA (%) of a

linear model.


(a)

(b)

Figure 6-6-3: Response surface of FFA content against

(a) methanol to oil molar ratio and reaction temperature at 10% acid catalyst

(H2SO4); (b) against catalyst concentration and reaction temperature at 30:1 methanol to oil molar ratio.


Figures 6-3a and 6-3b show the surface plots generated using the model equation on

the effect of each variable on FFA content. Methanol to oil molar ratio has a strong

effect on FFA reduction which is evident in Figure 6-3a. One the other hand, H2SO4

concentration has only a minimal impact on FFA reduction within the range of

catalyst concentrations used, suggesting low linear effect on FFA. In addition, FFA

content increases correspondingly after 10 wt% H2SO4, implying that further

increase in H2SO4 concentration will have adverse effects. This might be the result of

oil decomposition at high acid concentrations. Reaction temperature has a small

effect on FFA content at lower values and effect on FFA reduction is significant after

65 °C, which was more evident in Figure 6-3b.

Table 6-7: Experimental data for base-catalysed trans-esterification.

Run MeOH: oil molar ratio NaOCH3 (wt%) Temp (°C) FAME

(%)

1 6 0.8 60 89.21 2 8 0.8 45 88.51 3 4 0.6 60 74.15 4 4 1.0 60 85.51 5 4 0.8 45 81.92 6 4 0.8 75 78.02 7 8 1.0 60 89.41 8 6 1.0 75 87.70 9 6 0.6 45 64.18 10 6 0.6 75 75.76 11 8 0.8 75 84.78 12 8 0.8 60 63.02 13 6 0.8 60 87.72 14 6 0.8 60 87.20 15 6 1.0 45 90.76

6.3.3 Base-catalysed transesterification of pre-esterified Beauty leaf oil

All samples produced from the acid-catalysed esterification process were thoroughly

mixed to produce a homogenous feedstock for trans-esterification. The FFA content

of the mixture was found to be 2.46% (w/w). Similarly to the acid-catalysed pre-

esterification trials, 15 experimental runs were undertaken based on Box-Behnken

design as shown in Table 6-2. The results obtained from those experiments are

shown in Table 6-7. In the transesterification experiments, the ester content of the


FAME ranged from 63.02% to 90.76% with the highest content resulting from

reaction conditions with 6:1 methanol to oil molar ratio, 1 wt% NaOCH3 and 45 °C

temperature.

As from the experimental data processed using Minitab 16 software to generate the

linear and quadratic model for statistical prediction of ester content as a function of

methanol to oil molar ratio, catalyst concentration and reaction temperature. The

linear and quadratic model equation resulting from this is shown in Equations (6-5)

and (6-6) respectively and Table 6-8 summarises the resulting regression coefficients

and corresponding p-value:

%FAME 40.9817 0.3825 ' 47.6688 ' 0.00742M C T ………Equation 6-5

2 2 2

%FAME 129.28 2.256 ' 339 ' 1.817 9.392 ' ' 0.0014 ' '

1.2203 ' ' 0.7892 ' 171.57 ' 0..007011 '

M C T M C M T

C T M C T

…………………..Equation 6-6

Table 6-8: Regression coefficients for FAME (%) prediction

Predictor Linear Full quadratic

Coefficient p-value Coefficient p-value

Constant 40.9817 0.008 −129.28 0.141

MeOH:Oil (M') 0.3825 0.727 2.256 0.809

NaOCH3 (C') 47.6688 0.001 339 0.021

Temp (T') 0.00742 0.959 1.817 0.239

MeOH:Oil × NaOCH3 (M'C') 9.392 0.133

MeOH:Oil × Temp (M'T') 0.0014 0.985

NaOCH3 × Temp (C'T') −1.2203 0.141

MeOH:Oil × MeOH:Oil (M'2) −0.7892 0.0207

NaOCH3 × NaOCH3 (C'2) −171.57 0.025

Temp × Temp (T'2) −0.007011 0.502

The regression coefficient (R2) and the adjusted regression coefficient (R2 (adj)) of

linear model were 0.6465 and 0.55 demonstrated that the linear model may not be

suitable for estimate FAME in given reaction condition. Whereas full quadratic

model with 0.9224 of regression coefficient (R2) and 0.8413 of adjusted regression


coefficient (R2 (adj)) shows better model for FAME estimation. Figure 6-4 shows the

accuracy of the prediction model in a scattered plot between experimental and

predicted ester contents. All points are close to straight line demonstrate a good

agreement between experimental results and those ones calculated by the model.

Figure 6-6-4: Scatter diagram of experimental and calculated FAME (%) of full

quadratic model.

More detailed analysis of the effect of base-catalysed transesterification reaction

parameters on Beauty leaf FAME ester content are shown in Figures 6-5a and 6-5b.

These figures predict that an optimal methanol to oil molar ratio would be 7.5:1,

however, further increase in methanol would not have a positive effect on ester

content. On the other hand, NaOCH3 concentration has a strong effect on ester

content of the FAME with corresponding increment with agreement in terms of

linear and quadratic effects. Temperature had a less significant effect than methanol

to oil molar ratio and catalyst concentration. Figure 6-5 shows that the optimum

temperature of transesterification was 65 °C. These figures illustrated that although

all parameters are not statistically significant at 95% confident level but the

relationship still contains useful information for some biodiesel production purposes.


(a)

(b)

Figure 6-6-5: Response surface ester content against catalyst concentration vs. (a)

methanol to oil molar ratio at 55 °C; (b) reaction temperature at 7.5:1 methanol to oil

molar ratio.


6.4 CONCLUSIONS

A response surface method based a Box-Behnken design was employed to determine

a feasible experimental plan to optimise the Beauty leaf oil to biodiesel conversion

procedure. Due to the high FFA content of Beauty leaf oil (12 wt%), a two-step

process was employed utilising sulphuric acid catalysed pre-esterification followed

by sodium methoxide catalysed trans-esterification. Effects of reaction parameters

such as methanol to oil molar ratio, catalyst loading and reaction temperature were

statistically investigated on the reduction of FFA content in pre-esterification and

ester content in trans-esterification. The optimal conditions for pre-esterification

were 30:1 methanol to oil molar ratio, 10 wt% sulphuric acid catalyst and 75 °C

reaction temperature which reduced the FFA content to 1.8 wt%. With the aid of

statistical modelling, the predicted optimal conditions for transesterification

methanol to oil molar ratio, catalyst concentration and reaction temperature were

7.5:1, 1% and 55 °C respectively. Based on these conditions, the highest achievable

ester content of FAME predicted by the model was found to be approximately 93%.

However a higher result may be achievable by future lowering FFA content of

Beauty leaf oil. In terms of a linear effect on FFA reduction for the first step,

methanol to oil molar ratio was found to be highly significant and reaction

temperature moderately significant. For trans-esterification, catalyst concentration

was found be the most dominant variable in achieving high ester contents. The

limitation of the developed response surface model is that all the p-values are greater

than 0.05. Therefore, the developed models might be over-specified and some that

terms can be omitted. However, the information contained in the model and

experiment in this study is very significant in industrial biodiesel production.

Chapter 7: Diesel engine testing with biodiesel of controlled chemical composition 177

Chapter 7: Diesel engine testing with biodiesel of controlled chemical composition

Particle emissions from biodiesels with different

physicochemical properties

M. M. Rahman, A. M. Pourkhesalian , M. I. Jahirul , S. Stevanovic , P. X. Pham,

H. Wang , A.R. Masri , R. J. Brown and Z. D. Ristovski

Publication: Journal of Fuel, Vol. 134, pp. 201-208, 2014

http://dx.doi.org/10.1016/j.fuel.2014.05.053 0016-2361/2014 Elsevier Ltd. All rights reserved

Author Contribution


M. M. Rahman Conducted the experiments, performed the data analysis and drafted the manuscript

A. M. Pourkhesalian Assisted with conducting the experiment and data analysis

M. I. Jahirul Conducted the experiments, performed the data analysis and drafted the manuscript Signature

S. Stevanovic Assisted with conducting the experiment

P. X. Pham Assisted with conducting the experiment

H. Wang Assisted with conducting the experiment

A.R. Masri Supervised the project and revised the manuscript

R. J. Brown Supervised the project and revised the manuscript

Z. D. Ristovski Supervised the project and revised the manuscript




Name


Signature

Date

178 Chapter 7: Diesel engine testing with biodiesel of controlled chemical composition

Abstract

Biodiesels produced from different feedstocks usually have wide variations in their

fatty acid methyl ester (FAME) so that their physical properties and chemical

composition are also different. The aim of this study is to investigate the effect of the

physico-chemical properties of biodiesels on engine exhaust particle emissions.

Alongside with neat diesel, four biodiesels with variations in carbon chain and

degree of unsaturation have been used at three blending ratio (B100, B50, B20) in a

common rail engine. It is found that particle emission increased with the increase of

carbon chain length and degree of unsaturation in FAME. However, for similar

carbon chain length, particle emissions from totally unsaturated biodiesel is found to

be slightly less than that of partially (about 50%) unsaturated biodiesel. Particle size

is also found to be dependent on fuel type. The fuel or fuel mix responsible for

higher PM and PN emissions is also found responsible for lager particle median size.

Particle emissions reduced consistently with fuel oxygen content regardless of the

proportion of biodiesel in the blends, whereas it increased with fuel viscosity and

surface tension only for higher diesel-biodiesel blend percentages (B100, B50).

However, since fuel oxygen content increases with the decreasing carbon chain

length, it is not clear which of these factors drives the lower particle emission.

Rather, overall, it is evident from the results presented here that chemical

composition of biodiesel is more important than its physical properties in controlling

exhaust particle emissions.

Keywords: Biodiesel, particle emissions, fuel physical properties, fuel chemical

composition

Highlights

Four biodiesels were used to investigate their influence on particle emissions.

Particle emission increased with the increase of biodiesel carbon chain length.

Particle emissions reduced consistently with fuel oxygen content.

Particle median size found dependent on the type of fuel used.

Biodiesel chemical composition found more important than physical properties.


7.1 INTRODUCTION:

Compression Ignition (CI) engines are increasing in popularity due to their higher

thermal efficiency. They power a wide range of land and sea transport as well as

provide electrical power, used in farming, construction and industrial applications.

Tail pipe emissions of diesel engines, especially particulate matter (PM) are still a

matter of concern due to its harmful effects both on human health and the

environment(Brito et al. 2010; Jacobson 2001). Exposure to diesel particulate matter

(DPM) can cause pulmonary diseases such as asthma, bronchitis and lung

cancer(Brito et al. 2010) and because of these adverse effects, the International

Agency for Research on Cancer (IARC) included DPM as carcinogenic to human

health.

The harmful effects caused by DPM are related to both the physical properties and

chemical composition of the particles. The physical properties that influence

respiratory health include particle mass, surface area, mixing status of particles,

number and size distribution (Ristovski et al. 2012). The particles deposit in different

parts of the lung depending on their size. The smaller the particles the higher the

deposition efficiency (Broday and Rosenzweig 2011) and the greater the chance of

them penetrating deep into the lung. The smaller particles stay suspended in the

atmosphere for longer thus have a higher probability of being inhaled and

consequently deposited deep in the alveolar region of the lung. Particle number

governs the ability of particles to grow larger in size by coagulation while particle

surface area determines the ability of the particles to carry toxic substances. Recent

studies reveal that DPM surface area and organic compounds play a significant role

in initiating various cellular and chemical processes responsible for respiratory

disease (Giechaskiel, Alfoldy and Drossinos 2009; Ristovski et al. 2012). In addition

to this, a large fraction of DPM is black carbon, which is considered the second most

potential greenhouse warming agent after carbon dioxide (Jacobson 2001). After

treatment devices (ATD) like diesel particulate filters (DPF) and diesel oxidation

catalysts (DOC) aid in reducing DPM (Herner et al. 2011). Alternative fuels are

another potential emission reducing source (Bakeas, Karavalakis and Stournas 2011).

Of these fuels, biodiesel is considered one of the more promising for diesel engines


as it (Varuvel et al. 2012; Xue, Grift and Hansen 2011) produces less PM and other

gaseous emissions (Lapuerta, Armas and Rodríguez-Fernández 2008b; Xue, Grift

and Hansen 2011; Surawski, Miljevic, Ayoko, Roberts, et al. 2011). Biodiesel in

diesel engines has the potential to greatly reduce carbon emissions and is a renewable

source of energy.

Biodiesel is a mixture of fatty acid esters with physicochemical properties that

mostly depend on the structure of this molecule. Biodiesel can be produced from a

variety of feedstock sources such as vegetable oil, animal fat, municipal and

industrial waste and some from insects (Salvi and Panwar 2012; Sharma, Singh and

Upadhyay 2008; Morshed et al. 2011; Alptekin, Canakci and Sanli 2012). An

extensive range of fatty acid profiles exist among these feedstocks (Moser 2014),

with some being within the same feedstock; which can be controlled. Physical

properties and chemical composition of biodiesel varies among different feedstocks,

which can have a noticeable influence on engine performance and emissions

(Hoekman et al. 2012; Mccormick, Graboski, Alleman and Harrin 2001).

McCormick et al. (McCormick, Graboski, Alleman, Herring, et al. 2001) reported

constant PM emissions from different biodiesel feedstocks when the density was less

than 0.89 g/cm3 or cetane number was greater than about 45, but increase of NOx

emissions with the increase of biodiesel density and iodine number. In contradiction

to these findings, a difference in particle emissions from biodiesel from different

feedstocks has also been reported (Surawski, Miljevic, et al. 2011a; Allan, Williams

and Rogerson 2008). Lapuerta et al.(2008b) reported a 10% increase of NOx and

20% decrease of particle emissions by unsaturated biodiesel. Benjumea et al.(2011)

found that the degree of unsaturation in biodiesel doesn’t significantly affect the

engine performance but increases smoke opacity and THC emissions. Kravalkis et al.

(2011) reported noticeable influence of biodiesel origin on particle emissions,

especially particles associated with PAH and carbonyl emissions. Very recently

Salamanaca et al.(2012) reported increased PM and HC emissions from biodiesel

that contains more unsaturated compounds that favour soot precursor formation.

There is no distinction however, that exists in the literature, which indicates whether

chemical composition of biodiesel, physical properties or a combination of these is

responsible for this variation in engine performance and emissions. This study


therefore, aims to investigate the effect of biodiesel physical properties and chemical

composition on engine exhaust particle emissions. It is an extension of the previous

study (Pham et al. 2013) where results from the same experiments were presented for

the engine performance characteristics and emission of pollutants including some

preliminary results for the particle emission, particularly for pure biodiesel. It should

be noted that the results for B100 are reproduced here for comparison purposes.

Furthermore, the paper elaborates on these findings and presents new analysis in

terms of the physico-chemical properties of the fuels and their blends.

Table 7-1: Test engine specification

Model Cummins ISBe220 31

Cylinders 6 in-line

Capacity (L) 5.9

Bore x Stroke (mm) 102 x 120

Maximum power (kW/rpm) 162/2500

Maximum torque (Nm/rpm) 820/1500

Compression ratio 17.3

Aspiration Turbocharged & after cooled

Fuel injection Common rail

After treatment systems None

Emissions certification Euro III

7.2 MATERIALS AND METHODS

7.2.1 Engine and fuel specification

This experimental study was performed in a heavy duty 6 litres, six cylinders,

turbocharged after cooled, common rail diesel engine typically used in medium size

trucks. Test engine is the same as used in Pham at el. (2013). Table 7-1 shows

specification of the test engine. Engine was coupled to a water brake dynamometer,

and both of them are connected to an electronic control unit (ECU). Engine was

operated at 1500 rpm (maximum torque speed) and at 2000 rpm (intermediate

speed), and four different loads 25%, 50%, 75%, & 100% for each engine speed.


Maximum load at any particular engine speed depends upon the type of fuel used,

therefore for each fuel at first maximum load was measured when engine was in full

throttle for a particular speed. This measured load is then considered as 100% load

for that speed and other loads were determined based upon measured 100% load.

An ultra-low sulphur diesel (sulphur content < 6ppm) and four biodiesels with

different physicochemical properties were used to run the engine. All four biodiesels

were used at three blending ratio i.e. 100% biodiesel (B100), blends of 50% diesel

and 50% biodiesel (B50), and blends of 80% diesel and 20% biodiesel (B20). 7-2

shows the fatty acid profile of used biodiesels as found using gas chromatography

mass spectrometry (GCMS) analysis. Biodiesel samples were analysed using Perkin

Elmer clarus 580GC-MS equipped with Elite 5MS 30m x 0.25mm x 0.25um column

with a flow rate of 1mL/min. Before analysing, each biodiesel was diluted with n-

hexane (1:100 v/v). Initial temperature was 120 0C for 0.5 minutes, then raised to 310 0C for 2 minutes at 10 0C/min and kept at 310 0C for 2 minutes. The mass selective

detector was optimised using calibrating standards with reference masses at m/z (35-

40). Among four biodiesels, C810 is fully saturated and composed of 52% and 46%

caprylic acid and capric acid ester respectively. C1214 is also dominated by saturated

compounds but has comparatively longer carbon chain length fatty acid ester i.e.

48% lauric, 19% % myristic, 10% palmitic and 18% oleic acid ester. On the other

hand both C1618 and C1822 are dominated by long chain unsaturated fatty acid

esters. C1618 is composed of 21% palmitic, 9% stearic, 58% cis-oleic and 10%

linoleic acid ester where C1822 has 10% more oleic and linoleic acid ester. C1822

also has small amount (4%) of trans- oleic acid ester.

Some important properties of all used fuels related to combustion and emissions are

shown in Table 7-3. Among the used biodiesels physical properties varied with the

variation in chemical composition i.e. carbon chain length and degree of

saturation/unsaturation. Viscosity, heating value, iodine value and oxygen content

increased with the increase of carbon chain length and degree of unsaturation, where

saponification value decreased. Density of all four biodiesels was found higher than

diesel, and no trend observed among biodiesels either with the carbon chain length or

degree of unsaturation. Surface tension also increased with the carbon chain length in


biodiesels, although no significant change was observed with the degree of

unsaturation. For example, there is almost no difference in surface tension between

C1618 & C1822, although C1822 contains much higher percentage of unsaturated

compounds compared to C1618. Surface tension and cetane value of diesel were

found to be lower than all four used biodiesels where calorific value was higher.

Viscosity of diesel was higher than C810 but lower than the rest of the three

biodiesels.

Table 7-2: Fatty acid profile of used biodiesels

Common name Lipid number Biodiesels

C810 C1214 C1618 C1822

Caprylic acid C8:0 52.16 0 0 0 Capric acid C10:0 46.38 0.17 0 0 Lauric acid C12:0 1.38 47.8 0.1 0 Myristic acid C14:0 0 18.89 0.06 0.03 Pentadecylic acid C15:0 0 0 0.03 0.02 Palmitic acid C16:0 0 10.19 21 4.45 Palmitoleic acid C16:1 0 0 0 0.12 Margaric acid C17:0 0 0 0.06 0 Stearic acid C18:0 0 2.55 9.47 2.53 Oleic acid C18:1cis 0 18.53 58.72 68.13 Elaidic acid C18:1trans 0 0 0 3.96 Linoleic acid C18:2 0 1.76 9.98 18.69 Arachidic acid C20:0 0 0.08 0.3 0.49 Gadoleic acid C20:1 0 0 0.24 1.03 Behenic acid C22:0 0 0.03 0.03 0.17 Glycerol 0.08 0 0 0

7.2.2 Exhaust sampling and measurement system

The Dekati ejector diluter was used to partly sample raw exhaust from the engine

exhaust pipe and then dilute it with particle free compressed air. A second Dekati

diluter was connected in series with the first one to further increase the dilution ratio

in order to further decrease concentration. A HEPA filter was used to provide

particle free compressed air for the diluters. The purpose of the dilution was to bring

down the temperature as well as the concentration of gases and PM within the

measuring range of the instruments. Diluted exhaust was then sent to different

gaseous and particle measuring instruments. A CAI 600 series CO2 analyser was


used to measure the CO2 concentration directly from the raw exhaust. A second CO2

meter (SABLE, CA-10) connected via a three way valve between the two diluters

was used to record the CO2 concentration from the diluted exhaust. Background

corrected CO2 was used as tracer gas to calculate the dilution ratio for each stage.

After first stage dilution, CAI 600 series CLD NOx analyser was used to measure the

NOx. PM2.5 emissions were measured by a TSI DustTrak (Model 8530). DustTrak

readings were converted into a gravimetric measurement by using the tapered

element oscillating microbalance to DustTrak correlation for diesel particles

published by Jamriska et al.(2004). It is worth noting this conversion can introduce

significant uncertainties if the optical properties of particles significantly changes.

The particle number size distribution for C810, C1214, C1618 and their blends was

measured by a scanning mobility particle sizer (SMPS). This SMPS consisted of a

TSI 3080 electrostatic classifier (EC) and a TSI 3025 butanol based condensation

particle counter (CPC). Due to technical problems a new SMPS had to be used for

the reference diesel and C1822. This SMPS system consisted of a 3085 classifier

with a nano-DMA (differential mobility analyser). As the measurement range of the

two SMPS’s used was different, we have used a fitting procedure (see Section 7.3.2)

to recalculate the total PN and make the measurements comparable. A TSI 3089 nm

aerosol sampler (NAS) was used in conjunction with a Tandem Differential Mobility

Analyser (TDMA) to collect preselected particles on Transmission Electron

Microscopic (TEM) grids for morphological analysis. The EC in the TDMA

preselected the size of the particles, which deposited on the TEM grid in the NAS.

An Aethalometer (Magee Scientific) was also connected after second stage dilution

for black carbon (BC) measurement. Results from TEM analysis and BC data will be

published in a separate paper.


Table 7-3: Important physicochemical properties of tested fuels

Relevant properties Fuels

C810 C1214 C1618 C1822 Diesel

Average formula C9.5H19.7O2 C9.5H19.7O2 C9.5H19.7O2 C9.5H19.7O2 CxHya

Average unsaturation (AU) 0 0.22 0.7892 1.11 ‐ Oxygen content (wt%) 18.72 13.25 10.74 10.83 0 a

Stoichiometric air fuel ratio 11.12 12.05 12.50 12.48 14.5 Relative density(kg/l) 0.877 0.871 0.873 0.879 0.8482 Viscosity (mm2/sec) 1.95 4.37 4.95 5.29 3.148 Surface tension (mN/m) 26.184 28.41 29.9 29.966 26 Cetane value 62.96 65.57 61.06 53.65 48.5 Iodine number 1 max 8 65 105 Saponication value 330 233 195 185 Acid value 0.9 0.4 0.8 0.4 <0.05 Boiling point (0C) 190 >150 165.6 >150 >190 a

Gross Calorific value(MJ/kg)

35.335 38.409 37.585 39.825 44.365

Sulphur content(mg/kg) 0 0 0 0 2.5 a Values with superscripts have been taken from literature(Surawski, Miljevic, et al.

2011c) and (Lapuerta, Armas and Rodriguez-Fernandez 2008).

Figure 7-7-1: Schematic diagram of used engine exhaust measurement system



7.3.1 Specific PM emissions

All four biodiesels that were used, disregarding the variations in physical properties

and chemical composition, reduced PM emissions in comparison to petroleum diesel.

Figure 7-2a and b shows brake specific PM emissions at engine operating speeds of

1500 rpm and 2000 rpm, respectively. It was found that as the biodiesel percentage

in the diesel-biodiesel blends increased, PM emissions decreased consistently. The

maximum reduction in PM was observed for 100% biodiesel blends, an observation

common in the literature (Lapuerta, Armas and Rodríguez-Fernández 2008a;

Surawski, Miljevic, et al. 2011b; Xue, Grift and Hansen 2011). Noticeable variations

in PM emissions were also observed among the four biodiesels and their blends. In

the case of using 100% biodiesel, a massive 98% reduction in PM was observed for

biodiesel C810, where C1214, C1618 and C1822 reduced PM 83%, 70% and 76%

respectively. Similar trends in PM emissions were also found for B50 and B20

blends although there was a difference of PM reduction proportion in these. For the

B50 blend, PM reduction among biodiesels C810, C1214, C1618 and C1822 was

88%, 75%, 70% and 76% respectively. B20 was slightly lower, measuring 66%,

57%, 42% and 48% respectively. PM emissions from other tested engine loads are

shown in the appendix (Figure A7-1). Similar trends in PM emissions were also

observed for these loads at 2000 rpm engine speed, although at 1500 rpm, PM

emissions from B20 (C1618) were found to be slightly higher than for the diesel.

These variations in PM emissions among biodiesels could be due to either their

chemical composition or their physical properties. Among the biodiesels, PM

emissions increased consistently with biodiesel carbon chain length with the

exception of C1822. This blends carbon chain length was similar to C1618 but its

degree of unsaturation was higher and its PM emissions were less. Pinzi et al.(2013)

also reported reduction of PM emission with the increase of degree of unsaturation

but same carbon chain length in FAME. Opposing observations were also reported in

the literature, which suggests that unsaturated compounds have a tendency to act as

soot precursor (Salamanca et al. 2012; Benjumea, Agudelo and Agudelo 2011). In

addition, important physical properties of C1822 in regards to particle emissions i.e.

viscosity and surface tension were also higher. This slight reduction in PM emissions


from C1822 might be attributed to its high iodine or low cetane value. Fuels with low

cetane value undergo prolonged premixed combustion phases that are responsible for

less soot formation. In addition, NOx emissions from C1822 were highest among the

fuels that were favourable for soot oxidation. This could also be responsible for

comparatively low PM emissions from C1822.

Figure 7-7-2: Brake specific PM emission at

(a) 1500 rpm 100% load and (b) 2000 rpm 100% load. The PM emissions shown are

calculated based on DustTrak measurement.

7.3.2 Specific PN emissions

Variations that were observed in PN emissions were similar to the fuels used. There

were however, slight differences in proportion compared to PM emissions. All PN

emissions were calculated for the size range from 10.2-514 nm. As the measurements

for neat diesel and C1822 were done using the nano-DMA, in the size range from

4.6nm-156nm, a fitting procedure was used to recalculate the PN concentration to the

same size range as used in the other measurements (Heintzenberg 1994). As shown

in Figure 7-3, PN emissions from B100 were found to be lower than diesel for all

biodiesels. Among the biodiesels used, C810 reduced PN most and C1618 reduced


PN the least compared to neat diesel, at 90% and 20% respectively. Reductions from

C1214 and C1822 were measured at 60% and 35% respectively. For B50, in the case

of C810, C1214 and C1822, the PN emissions remained lower than diesel although

C1618 increased approximately 10%. Similar to B100, the lowest PN emissions were

observed with C810 for B50 with C1214 and C1822 following the trend for B100.

PN emissions from C1214 increased 15% with a large standard error at 2000 rpm,

while at 1500 rpm it remained almost same to C1822. Apart from B100 and B50, PN

emissions from B20 were found to be slightly less than diesel and almost the same

among the biodiesels with the exception of around 15% increase from diesel at 1500

rpm. Brake specific PN emissions from other engine loads at both rpm are shown in

appendix (Figure A7-2). PN emissions from all other lodes and engine speeds

showed a similar trend with the exception of 1500 rpm 50% load where PN emission

from B20 appeared to have a different trend as compared to the rest of the results.

Figure 7-7-3: Brake specific PN emissions at 1500 rpm 100% load (a) and 2000 rpm

100% load (b).


7.3.3 Particle number size distribution

Particle size distribution (PSD) was always found to be unimodal with a single peak

in the accumulation mode despite the variations in the fuel and the condition of

engine operation (appendix: Figures A7-3 to A7-5). Variations in PSD among the

biodiesels were more prevalent for B100, followed by B50. Comparatively, PSD

from B20 was found to be similar to petroleum diesel regardless of the variations of

biodiesel. Another important feature is that biodiesel reduced a higher proportion of

large particles (mobility diameter >100 nm) compared to nanoparticles (mobility

diameter <50 nm). Nanoparticle emissions from biodiesel however, did not exceed

that of diesels, which have been reported in few studies (Shi et al. 2010; Surawski,

Miljevic, et al. 2011a). The presence of second peak (nucleation mode) in PSD is

responsible for increased nanoparticle emission which we didn’t observe in this

study. Presence of excessive volatiles and semi volatiles in exhaust which partitioned

into particles upon cooling down are the primary contributor to nucleation mode

peak. In addition, impurities in biodiesel especially glycerol doesn’t undergo

complete combustion due to their high viscosity, poor atomisation and mixing

property. They form partially oxidised volatiles and semi volatiles which can be a

major contributor to nucleation mode peak. Biodiesels used in this study was free

from glycerol and other impurities, which might facilitate the absence of nucleation

mode peak in PSD.

7.3.4 Particle median size

Particle size also varied among used fuels in a similar way to PM and PN. In case of

diesel, the median size of the particles in the SMPS size distribution was 61 nm and

56 nm at 1500 and 2000 rpm respectively. For 100% biodiesel, particle median size

was always found to be smaller than for neat diesel and diesel-biodiesel blends

(Figure 7-4a and 7-4b). Among four used biodiesels, C810 produced the smallest

particle median size i.e. 40 nm and 43 nm at 1500 and 2000 rpm respectively,

followed by C1822 which was 53 nm and 44 nm. C1214 and C1618 gave almost the

same particle size as neat diesel with slight difference between two engines operating

speeds. Particle size from B50 was found to be larger than B100 but smaller than

B20 blends. Interestingly, particle emitted from all B20 blends were found to be

larger than diesel with the largest particle median size observed for C1618 B20


blend. Similar trends in particle size were also observed at other engine loads which

are shown in appendix (Figures A7-6 and A7-7). To gain some insight of the

variation in particle sizes among different biodiesels and its blends, particle median

size from all measurement was plotted against the particle number concentrations. As

can be seen on Figure 7-4c a moderate positive correlation, with a Pearson

correlation coefficient of 0.61 was found between particle median size and total

number concentration. This indicates that total PN number concentration through

coagulation could be one of the key parameters influencing the overall particle size.

Higher the particle number emissions, larger the particle size can be. The other

factors may be the biodiesel viscosity, surface tension and especially oxygen

contents which ensure the presence of more oxygen functional groups on the surface

of particles responsible for enhance particle oxidation and subsequent size reduction

(Wang et al. 2009; Zhu, Cheung and Huang 2011).

Figure 7-7-4: Variations in particle median size among used fuels at 1500 rpm 100%

load (a) and 2000 rpm 100% load (b), while (c) shows particle median size variation

with total number concentration.

7.3.5 NOx emissions

NOx emissions were also found to be dependent on biodiesel carbon chain length

and degree of unsaturation (see Figure. 7-5). Biodiesels with higher degree of


saturation and shorter carbon chain length emitted less NOx than biodiesels with

relatively longer carbon chain and higher degree of unsaturation. Interestingly NOx

emissions from C810 and C1214 were found to be less than for diesel especially at

higher blend percentages. An interesting trend in NOx emissions was also observed

among the different blends used. The usual trend, as reported in most of the

literature, is to observe the increase in NOx emissions with the increase of biodiesel

blend percentage (Bakeas, Karavalakis and Stournas 2011; Hoekman and Robbins

2012). While we have observed a similar trend for long chained biodiesels with

higher degree of unsaturation i.e. C1618 and C1822, however for saturated and short

chained biodiesels i.e. C810 the opposite trend was observed. NOx formation mostly

depends on the duration of premixed combustion phase and in cylinder temperature.

Biodiesels with higher degree of unsaturation have low cetane number, which leads

to prolonged premixed combustion favourable for thermal NOx formation. So the

higher NOx emissions from C1618 and C1875 are expected and are due to their

higher unsaturation as well as their higher heating value. On the other hand higher

degree of saturation, higher cetane number and lower heating value of C810 and

C1214, may cause shorter premixed combustion and lower in cylinder temperature

responsible for less NOx emissions. Therefore the discrepancy in reported (Redel-

Macías et al. 2012) NOx emissions, among different biodiesel studies in the

literature, may be due to biodiesel chemical composition. The generally adopted

concept of increase in NOx emissions for biodiesels does not always stand. Rather,

whether biodiesels will increase or reduce NOx emissions depends upon their

chemical composition.


Figure 7-7-5: Brake specific NOx emission at (a) 1500 rpm 100% load and (b) 2000

rpm 100% load.

7.3.6 Influence of fuel physical properties and chemical composition on particle emissions

To understand what is the relative influence of fuel physical properties as well as

chemical composition on particle emissions, PM and PN emissions for all used fuels

were plotted against fuel viscosity, surface tension and oxygen contents. Variations

in PM and PN emissions with fuel viscosity, surface tension and oxygen contents at

100% load are shown in Figure 7-6, where the other loads are shown in appendix

(Figures A7-8 to A7-10). As shown in Figure 7-6, particle emissions increased with

the increase of fuel viscosity and surface tension but only within a specific blend. For

higher blend percentages (B50 and B100) there was almost a linear relationship

between surface tension, viscosity and particle emissions. On the other hand, for the

same viscosity and surface tension, particle emissions also found significantly

different among fuel/fuel mix. It is evident from the literature, both viscosity and

surface tensions have noticeable influence on fuel atomisation process (Ejim, Fleck

and Amirfazli 2007), which is a key parameter relative to in-cylinder soot formation.

Lower the viscosity and surface tension of fuel, more easily they evaporate, atomise


and mix into in-cylinder air, and more complete their combustion are (Lee et al.

2002; Chen et al. 2013). In this case, the used engine was employed with common

rail injection system where fuel injection pressure was high (around 200 bars). Such

a high injection pressure might be minimised the effect of small variation in fuel

viscosity and surface tension on fuel atomisation and subsequent particle emissions.

On the other hand, a more consistent negative relationship was observed between

fuel oxygen content and particle emissions. This relationship did not depend on the

blend percentage. Similar reduction in particle emissions with fuel oxygen content

has also been reported in the literature (S.S. Gilla 2011; Rahman et al. 2013; Xue,

Grift and Hansen 2011). Therefore, this is a clear indication that the fuel chemical

composition, particularly the oxygen content, could be more important than its

physical properties in terms of engine exhaust particle emissions.

Figure 7-7-6: Variation in specific PM and PN emissions with used fuel surface

tension, viscosity and oxygen content

((a), (b), (c) for PM and (d), (e), (f) for PN), Ordinate of (a), (b), (c) and (d), (e), (f) are same where abscissa of (a), (d) and (b), (e) and (c), (f) are same.


7.3.7 Comparison of engine performance and particle emissions among used biodiesels

A comparison between engine performance parameters and particle emissions is

shown in Figure 7-7. In this figure, the vertical axis represents the percentage change

of engine power, break specific fuel consumption and specific PM/PN emissions

while the horizontal axis indicates biodiesel proportion in the blends. Neat diesel was

used as a reference fuel to calculate the percentage changes. There is a significant

difference among all four biodiesels used. For example, C810 provides the highest

reduction in particle mass and number but the penalty for that is also highest, around

25% reduction in engine brake power and an additional 25% increase of specific fuel

consumption due to that reduced engine power. This fuel and power penalty is lowest

for C1618 but particle mass and number reduction is also the lowest in this fuel

blend. Therefore it is necessary to make a trade-off between particle emission

reduction, fuel and power penalty, ensuring maximum benefit; not just for emission

levels but for engine power and fuel economy as well. Considering the

aforementioned factors, C1822 seems to have advantage over the rest of the fuels, as

it maintains the lowest power and fuel penalty regardless of the blending ratio to

diesel and a reasonable reduction in engine exhaust particle emissions. The evidence

suggests that biodiesels with a longer carbon chain length and higher degree of

unsaturation might be a solution to reduce particle emissions to a certain extent with

less fuel and engine power penalty.


Figure 7-7-7: Comparison of engine performance (power, BSFC) and particle

emissions (PM, PN) among biodiesels and their blends where petroleum diesel was

used as a reference fuel.

7.4 CONCLUSIONS

In conclusion, biodiesel fuels with shorter carbon chain lengths and higher degrees of

saturation have more potential to decrease engine exhaust particle emissions. With

the increase of carbon chain length and degree of unsaturation, particle emissions

also increase. Particle size also depends on type of fuel used. Fuel or fuel mix

responsible for higher PM and PN emissions was also found to be to have a larger

particle median size. This indicates that Coagulation plays a role in overall engine

exhaust particle size. Particle emissions increase linearly with fuel viscosity and

surface tension for higher diesel- biodiesel blend percentages (B100, B50). It reduces

consistently with fuel oxygen content regardless of the proportion of biodiesel in the

blends. High fuel injection pressure by common rail injection systems might

minimise the effects of small variation in fuel viscosity and surface tension on

particle emissions. Fuel oxygen content increases with the decrease of FAME carbon

chain length; therefore it is not clear whether FAME carbon chain length or oxygen

content is the driving force that decreases particle emission. The results support the

view that chemical composition of biodiesel is more important than its physical

properties in regards to reducing engine exhaust particle emissions.

Power BSFC PM PN

-40

-30

-20

-10

0

10

20

30

40

50

60

70

80

90

100

Incr

ease

d(%

)R

educ

ed(%

)

B100

Power BSFC PM PN

B50

Power BSFC PM PN

B20

C810 C1214 C1618 C1822


APENDIX A7

Particle emissions from biodiesels with different physical properties and chemical

composition

Figure A7-1: Brake specific PM emissions at 75%, 50% and 25% loads respectively

while the engine operated at 1500 and 2000 rpm respectively.

C810 C1214 C1618 C18220.00

0.02

0.04

0.06

0.08

0.10

Diesel

Bra

ke s

peci

fic P

M (

g/kW

-hr)

Biodiesel type

B20 B50 B100

1500 rpm 75% load

C810 C1214 C1618 C18220.00

0.01

0.02

0.03

0.04

0.05Diesel

2000 rpm 75% load

Bra

ke s

peci

fic P

M (

g/kW

-hr)

Biodiesel type

B20 B50 B100

C810 C1214 C1618 C18220.00

0.02

0.04

0.06Diesel

1500 rpm 50% load

Bra

ke s

peci

fic P

M (

g/kW

-hr)

Biodiesel type

B20 B50 B100

C810 C1214 C1618 C18220.00

0.01

0.02

0.03

0.04

0.05

Diesel

2000 rpm 50% load

Bra

ke s

peci

fic P

M (

g/kW

-hr)

Biodiesel type

B20 B50 B100

C810 C1214 C1618 C18220.00

0.02

0.04

0.06

0.08

0.10

0.12

Diesel

1500 rpm 25% load

Bra

ke s

peci

fic P

M (

g/kW

-hr)

Biodiesel type

B20 B50 B100

C810 C1214 C1618 C18220.00

0.01

0.02

0.03

0.04

0.05

0.06

0.07

Diesel

2000 rpm 25% load

Bra

ke s

peci

fic P

M (

g/kW

-hr)

Biodiesel type

B20 B50 B100


Figure A7-2: Brake specific PN emissions at 75%, 50% and 25% loads respectively

while the engine speed was 1500 and 2000 rpm

C810 C1214 C1618 C18220.0

3.0x1013

6.0x1013

9.0x1013

1.2x1014

1.5x1014

1.8x1014

Bra

ke s

peci

fic P

N(#

/kW

-hr)

Biodiesel Type

B20 B50 B100

1500 rpm 75% load

Diesel

C810 C1214 C1618 C18220.0

2.0x1013

4.0x1013

6.0x1013

8.0x1013

1.0x1014

1.2x1014

1.4x1014

Bra

ke s

peci

fic P

N(#

/kW

-hr)

Biodiesel Type

B20 B50 B100

2000 rpm 75% load

Diesel

C810 C1214 C1618 C1822

2.0x1013

4.0x1013

6.0x1013

8.0x1013

1.0x1014

1.2x1014

1.4x1014

Bra

ke s

peci

fic P

N(#

/kW

-hr)

Biodiesel Type

B20 B50 B100

1500 rpm 50% load

C810 C1214 C1618 C18220.0

4.0x1013

8.0x1013

1.2x1014

1.6x1014

2.0x1014

Bra

ke s

peci

fic P

N(#

/kW

-hr)

Biodiesel Type

B20 B50 B100

2000 rpm 50% load

Diesel

C810 C1214 C1618 C18220.0

5.0x1013

1.0x1014

1.5x1014

2.0x1014

2.5x1014

3.0x1014

Bra

ke s

peci

fic P

N(#

/kW

-hr)

Biodiesel Type

B20 B50 B100

1500 rpm 25% laod

Diesel

C810 C1214 C1618 C18220

1x1014

2x1014

3x1014

4x1014

5x1014

Bra

ke s

peci

fic P

N(#

/kW

-hr)

Biodiesel Type

B20 B50 B100

2000 rpm 25% load

Diesel


Figure A7-3: Particle number size distribution for B100, B50, and B20 at 1500 rpm

100% load (a, b, c respectively) and 2000 rpm 100% load (d, e, and f respectively)



1010 20 30 40 50 60 70 8090100 200 300

2.40x1064.80x1067.20x1069.60x1061.20x1071.44x1071.68x1071.92x1072.16x107

dN/d

logd

p(cm

-3)

1010 20 30 40 50 60 70 8090100 200 300200 300

Diesel C810 C1214 C1618 C1822

1010 20 30 40 50 60 70 8090100 200 300

2.40x1064.80x1067.20x1069.60x1061.20x1071.44x1071.68x1071.92x1072.16x107

(b)

10 20 30 40 50 60 70 8090 200 300

(e)

dN

/d lo

gdp(

cm-3)

1010 20 30 40 50 60 70 8090100 200 300

2.40x1064.80x1067.20x1069.60x1061.20x1071.44x1071.68x1071.92x1072.16x107

(c)

Particle electrical mobility diameter(nm)Particle electrical mobility diameter(nm)1010 20 30 40 50 60 708090100 200 300

(f)

(d)dN

/d lo

gd p

(cm

-3)

(a)

10 20 40 60 80 100 2000.0

5.0x106

1.0x107

1.5x107

2.0x107

2.5x107

3.0x107

dN/d

log

d p(#

/cm

3)

10 20 40 60 80 100 2000.0

5.0x106

1.0x107

1.5x107

2.0x107

2.5x107

3.0x107

(f)

(e)(b)

(d)

Diesel C810 C1214 C1618 C1822

(a)

10 20 40 60 80 100 2000.0

5.0x106

1.0x107

1.5x107

2.0x107

2.5x107

3.0x107

dN/d

log

d p(#

/cm

3)

10 20 40 60 80 100 2000.0

5.0x106

1.0x107

1.5x107

2.0x107

2.5x107

3.0x107

10 20 40 60 80 100 2000.0

5.0x106

1.0x107

1.5x107

2.0x107

2.5x107

3.0x107

(c)

dN/d

log

d p(#

/cm

3)

Particle electrical mobility diameter(nm)

10 20 40 60 80 100 2000.0

5.0x106

1.0x107

1.5x107

2.0x107

2.5x107

3.0x107





Figure A7-6: Variations in particle median size among fuels at 1500 rpm 75%, 50%

and 25% loads

10 20 40 60 80 100 2000.00

7.60x106

1.52x107

2.28x107

3.04x107

3.80x107

dN/d

log

d p(#/c

m3 )

10 20 40 60 80 100 2000.00

7.60x106

1.52x107

2.28x107

3.04x107

3.80x107

(f)

(e)(b)

(d)

Diesel C810 C1214 C1618 C1822

(a)

10 20 40 60 80 100 2000.00

7.60x106

1.52x107

2.28x107

3.04x107

3.80x107

dN/d

log

d p(#/c

m3 )

10 20 40 60 80 100 2000.00

7.60x106

1.52x107

2.28x107

3.04x107

3.80x107

10 20 40 60 80 100 2000.00

7.60x106

1.52x107

2.28x107

3.04x107

3.80x107

(c)

dN/d

log

d p(#

/cm

3)


10 20 40 60 80 100 2000.00

7.60x106

1.52x107

2.28x107

3.04x107

3.80x107



Figure A7-7: Variations in particle median size among fuels at 2000 rpm 75%, 50%

and 25% loads


Figure A7-8: Variation in specific PM emissions with fuel oxygen content at 75%,

50% and 25% load


Figure A7-9: Variation in specific PM emissions with fuel surface tension at 75%,

50% and 25% load


Figure A7-10: Variation in specific PM emissions with fuel viscosity at 75%, 50%

and 25% load

Chapter 8: Automobile diesel engine testing with Beauty leaf biodiesel 205

Chapter 8: Automobile diesel engine testing with Beauty leaf biodiesel

8.1 INTRODUCTION

Based on the work reported in this thesis, a number of Australian native plants were

found to be potential candidates for second-generation biodiesel feedstock. However,

this potential is still largely unexploited, mainly due to uncertainties related to the

quality of second-generation biodiesel as engine fuel and concern regarding engine

warranties and performance. The majority of current vehicle engines are not

optimised for the use of biodiesel and the different chemical and physical properties

of biodiesel (compared to petroleum diesel) will eventually effect fuel combustion

performance, exhaust emissions and engine durability. Therefore, biodiesel from new

feedstocks may not be suitable for direct use in conventional diesel engines and

engine system modification may be required. Moreover, quality standards are

becoming more crucial in relation to the commercial use of any fuel product, for the

assessment of safety risks and environmental pollution. As a result, any new

biodiesel needs to satisfy these quality standards and allow for smooth operation in

conventional diesel engines, before it can be considered as a sustainable fuel.

Therefore, the main aim of the work reported in this chapter was to experimentally

investigate the properties BOME, and to explore its suitability for use in unmodified

conventional automotive diesel engines, in terms of engine performance and

emissions.

206 Chapter 8: Automobile diesel engine testing with Beauty leaf biodiesel

8.2 INSTRUMENTATION AND METHODOLOGY

In this study, Beauty leaf biodiesel (BOME) was tested and compared with neat

regular petroleum diesel. The BOME was blended with petroleum diesel in two

different ratios by volume: 5% biodiesel with 95% diesel and 10% biodiesel with

90% diesel. The preparation of biodiesel from Beauty leaf seeds and its chemical

composition are presented in the previous chapters. The physical properties of diesel

and BOME were tested according to standard test procedures and the results are

presented in Table 8-1. In order to determine the fuel properties of diesel and BOME,

tests were conducted in the BERF fuel testing facility at QUT, Karlsruhe Institute of

Technology (KIT), Germany, and Caltex Refinery Laboratory in Wynnum, Brisbane.

The calorific value (HHV) of BOME was 13.3% lower than that of petroleum diesel.

Moreover, the higher kinematic viscosity, density, cetane number, surface tension,

flash point temperature and acid value of BOME compared to petroleum diesel were

also investigated as a possible cause of differences in engine performance and

emissions.

Table 8-1: Properties of Beauty leaf fatty acid methyl ester (BOME) and petroleum diesel

Properties Unit Test Method

Biodiesel Standard Diesel BOME Australian ASTM

D6751-12 EN14214

Kinematic Visocity @40 oC mm2/sec ASTM D445

3.5-5 1.9-6 3.5-5 2.64 4.54

Density @15 oC g/cm3 ASTM D4052

0.86-0.90 n/a 0.86-0.90 0.838 0.881

Higher Heating Value Mj/kg - n/a n/a n/a 45.93 39.82

Cetane Number - DIN 51773

51 min 47, min 51 min 50.5 64.03

Surface tension mPa.s - - - 26 29.85

Acid number 0.8 max 0.5 max 0.5 max 0 1.39

Flash point temperature °C ASTM D93

120, min 93, min 120, min 71 157

Lubricity @60 oC (wsd 1.4) mm IP 450 - - - 0.406

Cloud Point °C IP 309 Report Report Report 4 10.4

Sulphur mg/kg ASTM D7039

10 max 15 max 10 max 5.9

Experiments were conducted in the engine testing laboratory at the University of

Queensland (UQ) using a typical four-cylinder, turbocharged diesel car engine. The


engine specifications are shown in Table 8-2 and the experimental setup is shown in

the Figure 8-1. The speed and load of the test engine was controlled by a Froude

Hofmann AG 150 eddy current dynamometer. The dynamometer was embedded with

TEXCEL-Vl2 software for precise digital control and data acquisition. The

measurement accuracy for the torque and speed were ±1.25Nm (± 0.25% of full scale

load) and ± l rpm, respectively. A Kistlerpiezostar (Type 6056A42) pressure

transducer was used to measure in-cylinder gas pressure, which was recorded for

every 0.5 °CA. The variation of cycle-to-cycle cylinder pressure was recorded for

100 consecutive cycles and the mean was used for analysis. The engine was operated

at 2000 rpm with four different loads: 25%, 50%, 75% and 100%. The maximum

load at any particular engine speed depends on the type of fuel used, therefore, first

maximum load was measured for each fuel when engine was in full throttle for a

particular speed. This measured load was then considered as 100% load for that

speed and other load conditions were determined based upon the measured 100%

load. For each engine operating condition, the gaseous exhaust NOx, particle mass

(PM) and particle number (PN) were measured following a similar procedure and

using the same equipment as described in Section 7-2-2.

Figure 8-8-1: Experimental setup

Compressed air Dekati dilutor

4-cylinder DI diesel engine

Dynamometer

Sable

DustTrak

Crank angle encoder

Pressure transducer

Control

Compressor

Turbocharger

Air filter

DMS 500

Exhaust

Exhaust gas analyser

Computer

Computer


Table 8-2: Test engine specification

Model Peugeot 308 2.0 HDi

Cylinders 4 in-line

Capacity (L) 2

Bore x Stroke (mm) 85 x 88

Maximum power (kW/rpm) 100/4000

Maximum torque (Nm/rpm) 32/2000

Compression ratio 18

Aspiration Turbocharged & Inter cooled

Fuel injection Common rail (Multiple fuel injection)

Injection pressure (bar) 160


8.3.1 Engine power

Variations in brake power and indicated power for the different fuels under full and

part loads are shown in Figure 8-2a and 8-2b, respectively. The results show that the

measured engine power of Beauty leaf biodiesel was less than that of diesel fuel and

as biodiesel percentage in the diesel-biodiesel blends increased, engine power

consistently decreased. The maximum reduction in engine brake power for 5% and

10% BOME with diesel blends at 100% load were 1.3 kW and 2.8 kW, whereas for

indicated power, the reductions were 1.4 kW and 3.3 kW, respectively. These results

were expected due to the lower heating value of BOME compared to diesel. Similar

results were reported in the literature (Utlu and Koçak 2008; Karabektas 2009;

Hansen, Gratton and Yuan 2006; Kaplan, Arslan and Sürmen 2006; Murillo et al.

2007) where reductions in engine output power were also explained by the lower

heating value of biodiesel. A detailed explanation for variations in power output from

the use of biodiesel in diesel engines has already been given in Section 2.3.1.


(a) (b)

Figure 8-8-2: Variation of power output for neat diesel and biodiesel blends (a) brake

power; (b) indicated power

8.3.2 BTE and BSFC

Brake thermal efficiency (BTE) and brake specific fuel consumption (BSFC) are

important indicators for evaluating diesel engine performance. These parameters

indicate the effectiveness of the engine in transferring the chemical energy of fuel for

use by the engine. BTE is the ratio of brake power of engine output shaft in a given

time to the input energy of the fuel supplied during the same time, whereas BSFC is

the fuel consumption, in mass, to produce unit energy output for a given period of

time. Figure 8-3a and 8-3b shows the respective variation in BTE and BSFC with

engine load for neat diesel engine and Beauty leaf fuel blends. It can be seen from

Figure 8-3a that the BTE of BOME-diesel blends and neat diesel was quite similar at

lower engine loads. At higher loads, the BTE decreased with the increase of biodiesel

percentage in the blend. The maximum reduction in engine BTE for 10% and 5%

BOME with diesel blends at 100% load were 1.43% and 0.55%, respectively. The

small reduction in BTE with the increase of BOME percentage might be due to the

higher viscosity, higher volatility, poor air fuel mixture, poor spray characteristics

and lower heating value at high loads (Nabi, Rahman and Akhter 2009;

Suryanarayanan et al. 2008). However, at a low load, this decrease was not observed,

probably due to the increased lubricity of the biodiesel blends compared to diesel

fuel (Ramadhas, Muraleedharan and Jayaraj 2005). In contrast to BTE, the BSFC

decreased with higher engine loads and increased with the biodiesel blend ratio, as

shown in Figure 8-3b. This trend was likely due to the fact that biodiesel mixtures


have a lower heating value than neat diesel fuel, and thus, more of the biodiesel

mixture was required for the maintenance of a constant power output (Nabi, Rahman

and Akhter 2009). At full load, the BSFCs increased by 2.5% and 5.6% for the

blending of 5% and 10% BOME compared to neat diesel. Apart from the lower

heating value of biodiesel, the increase in BSFC with biodiesel may be due to a

change in combustion timing caused by biodiesels’ higher cetane number, as well as

the injection timing change (Buyukkaya 2010). Similar observations were also

reported by many authors while conducting experiments with various biodiesel fuels

in diesel engines (Qi et al. 2010; Nabi, Rahman and Akhter 2009; Ilkılıç et al. 2011;

Utlu and Koçak 2008; Kumar 2009a; Haşimoğlu et al. 2008; Canakci 2007; Lin and

Li 2009a; Lapuerta, Armas and Rodriguez-Fernandez 2008; Zheng et al. 2008;

Raheman and Phadatare 2004; Labeckas and Slavinskas 2006).

(a) (b)

Figure 8-8-3: (a) Brake thermal efficiency (BTE) and (b) Brake specific fuel

consumption (BSFC) for neat diesel and biodiesel blends

8.3.3 Cylinder pressure

The variation of cylinder pressure with crank angle for diesel and BOME blends at

different engine loads is shown in Figure 8-4(a-d). It can be seen from the figures

that the peak cylinder pressure decreased with the increase of BOME in the diesel

and biodiesel blend, which is more visible for lower engine loads. The peak cylinder

pressure of 18,025, 17,860 and 17,850 kPa were recorded for neat diesel, 5% BOME

and 10% BOME blends at full load, respectively. Although the combustion process


for the test fuels was similar and consisted of a premixed combustion phase

followed by of diffusion combustion phase, the physical properties of the fuel also

play an important roles in atomisation rate and air fuel mixing quality (Senthil

Kumar et al. 2005; Canakci, Ozsezen and Turkcan 2009; Devan and Mahalakshmi

2009). Therefore, the lower peak cylinder pressure of Beauty leaf biodiesel is likely

due to its higher viscosity and lower volatility compared to standard diesel. Under

higher engine load conditions, the difference in cylinder pressure may be due to

better combustion of the oxygen content in biodiesel at high loads.

(a)

(b)

(c) (d)

Figure 8-8-4: Engine cylinder pressure for diesel and Beauty leaf biodiesel blends,

(a) full load; (b) 75% load; (c) 50% load; and (d) 25% load


8.3.4 Nitrogen oxide (NOx) emission

The variation in specific NOX emissions from the addition of 5% and 10% BOME in

petroleum diesel are shown in Figure 8-5. It can be seen from the figure that NOX

emissions increased for both increases in engine load and percentage of BOME in

diesel. On average, the addition of 5% and 10% BOME in diesel increased specific

NOX emissions by 24.41% and 32.73%, respectively. This increase in NOX is not

unusual, with most researches reporting that the main reason behind such an increase

is related to the fuel injection mechanism (Senatore et al. 2000; Tat and Van Gerpen

2003b; Szybist, Kirby and Boehman 2005; Monyem and H Van Gerpen 2001;

Cardone et al. 2002; Yamane, Ueta and Shimamoto 2001). Because of the higher

viscosity, higher density and lower compressibility of biodiesel, the pressure rises

more quickly in the pump and also progress more quickly towards the injector.

Therefore, an advanced fuel injection (early needle opening) is observed with

biodiesel compared with diesel, resulting in a higher pack temperature and NOx

formation rate. However, for common-rail engines, where physical properties have a

significant effect on fuel injection, researchers have reported another explanation for

the increase in NOX emissions from biodiesel. When conducting an experiment with

soybean oil biodiesel and regular diesel, whilst maintaining constant fuel injection

timing, Cheng et. al. (2006) attributed the observed increase in NOx emissions with

biodiesel to the reduction of shoot formation with biodiesel, which reduced radiation

heat dissipation, resulting in a higher adiabatic flame temperature and increased NOx

formation. The other arguments frequently put forward to explain the higher NOx

emissions from biodiesel include: (1) higher oxygen availability in the combustion

chamber favours the NOx formation reaction when using biodiesel (Schmidt and Van

Gerpen 1996; Song et al. 2004) and (2) higher cetane number which serves to

advance combustion by shortening the ignition delay (Monyem and H Van Gerpen

2001).


Figure 8-8-5: NOX emission for diesel and BOME blend for different engine load

conditions

8.3.5 Particle mass (PM) and particle number (PN)

Figure 8-6 shows the variation in particle emissions, in terms of specific particle

mass (PM) and specific particle number (PN) at different loads, for diesel and

BOME blends. A clear trend of decreasing particle emissions was observed with the

increase in BOME percentage and also for engine load (Figure 8-6a). The maximum

reduction of specific particle mass was found to be 54.6% with the engine running at

full load using 10% BOME with diesel. When the engine operated under part load

conditions, the reduction of particle emissions was 33-37% for the same BOME

blend. The higher oxygen content in BOME compared to neat diesel might be the

main reason for reductions in PM emissions. For example, Frijters and Baert (2006)

found a good correlation between PM emission reduction and the oxygen content of

fuel when conducting experiments using various biodiesel blends. The excess oxygen

allows for more complete combustion and also promotes oxidation of the already

formed soot. Several other authors reported similar reasons to explain the reduction

in PM emissions when using biodiesel or biodiesel blends (Lapuerta, Armas and

Ballesteros 2002; Wang et al. 2000; Schmidt and Van Gerpen 1996; Rakopoulos et

al. 2008). Apart from the excess oxygen effect, the absence of soot precursors, such

as aromatics and sulphur content in biodiesel, may be another reason for the

reduction in PM (Choi, Bower and Reitz 1997; Wang et al. 2000; Schmidt and Van

Gerpen 1996; Chang and Van Gerpen 1997). On the other hand, no significant

change was found for specific particle number with the addition of BOME in diesel


fuel. However, at high engine loads (50%, 75% and 100%), a small increase in

specific PN was observed. Therefore, the reduction in particle mass and increase in

particle number shown in Figure 8-6 indicates the production of smaller particles

which using BOME compared with that of pure diesel. Many authors also reported

an increase in the number of small particles while testing diesel engines with various

biodiesel fuels (Krahl et al. 1999; Lapuerta, Armas and Rodriguez-Fernandez 2008;

Young et al. 2012). A brief explanation of small particle formation when using

biodiesel as a diesel engine fuel is presented in Section 7.3.3.

(a) (b)

Figure 8-8-6: Particle emission for diesel and BOME blend in different engine load

condition (a) Brake specific particle mass (PM); (b) brake specific particle number

8.4 CONCLUSION

The performance and emissions of a four-cylinder common rail diesel engine were

experimentally investigated using neat diesel and biodiesel produced from Beauty

leaf oil. The important performance and emission indicators of diesel engines, in

terms of brake power, indicated power, brake thermal efficiency, brake specific fuel

consumption, cylinder pressure, NOx emission, specific PM emission and specific

PN emissions were measured and presented graphically. Results indicate that 5% and

10% blends of Beauty leaf oil biodiesel with diesel fuel can be used in conventional

diesel engines without engine modification. However, variations in engine

performance and emissions were observed, due to the different physicochemical


properties of commercial diesel and biodiesel produced from Beauty leaf oil, as well

as the fact that the test engine was designed for use with petroleum diesel only.

Beauty leaf biodiesel reduced the engine power, as well as brake thermal efficiency

and increased brake specific fuel consumption at higher engine loads. The cylinder

peak pressure decreased with increasing Beauty leaf biodiesel in the blend and this

was more visible for lower engine loads. Specific PM decreased sharply with the

increase in Beauty leaf biodiesel under all engine load conditions. On the other hand,

slightly higher specific PN was found with Beauty leaf biodiesel compared with neat

diesel. In addition, specific NOx emissions also increased when the engine was run

with Beauty leaf biodiesel. The variations in engine emission and performance using

neat diesel and Beauty leaf biodiesel blends were not surprising and similar findings

have been reported in the literature when testing diesel engines with commercially

available biodiesels. However, the use of higher Beauty leaf biodiesel blends or pure

biodiesel is recommended in order to obtain a more in-depth analysis.

Chapter 9: Conclusions 217

Chapter 9: Conclusions

9.1 CONCLUSIONS ARISING FROM THIS THESIS

Biodiesel is a potential source of renewable alternative energy that is capable of

replacing the conventional petroleum fuel currently used for CI engine applications.

It is commonly anticipated that biodiesel will play a significant role in providing

energy requirements for transportation fuel in the near future, due to the many socio-

economic advantages it has over fossil fuel. Therefore, numerous research studies

have been conducted over the past few decades, with the aim of improving biodiesel

technology. As a consequence, biodiesels are now produced globally on an

industrials scale, most of which are obtained from edible vegetable oil feedstocks

such as soybean, palm, canola, sunflower etc. In recent years, these types of biodiesel

have been the subject of discussions in relation to its impact on rising food prices and

creating pressure on agricultural land, and therefore, it is considered unsustainable in

the long term. There is now an urgent need to promote further research, in order to

overcome the drawbacks of current biodiesel technology and investigate the

suitability of second-generation biodiesel obtained from non-edible feedstocks. To

that end, this research assessed the potential of selected Australian native plants as a

source of second-generation biodiesel for use in internal combustion engines. The

aim of this study was achieved by using a systematic approach, starting with a

detailed literature review on current biodiesel technology and culminating in an

experimental investigation of the performance of conventional diesel engines fuelled

with second-generation biodiesel obtained from the native Australian Beauty leaf

plant. In order to understand its physico-chemical characteristics, a range of

experimental investigations were carried out using conventional biodiesels and

artificially prepared biodiesel with a controlled composition. In addition, various

numerical tools were used to achieve the objectives of this study, including: artificial

neural networks (ANN), principle component analysis (PCA), preference ranking

organisation methods and geometrical analysis for interactive assistance

(PROMETHEE-GAIA) design of experiment (DOE), the response surface

methodology (RSM) and analysis of variance (ANOVA). This research has provided

218 Chapter 9: Conclusions

enough information to reach a number of innovative conclusions, which are briefly

described in the following paragraphs.

In general, biodiesels are methyl esters of fatty acids produced through the

transesterification of vegetable oils or animal fats. However, clear differences in the

chemical composition of biodiesel, in terms of fatty acid structure, have been found

within feedstocks, as well as from one feedstock to the next, which ultimately

determines many important fuel properties of biodiesel. Fuel quality, which is known

to effect fuel combustion performance, exhaust emissions and engine durability, is

more sensitive in modern diesel engines, as the use of high pressure (about 75,000

bars) common rail fuel injection systems has increased. Therefore, biodiesel from

any feedstock needs to fulfil the quality standards for fuel properties, in order to

qualify for commercial use as an IC engine fuel. Internationally recognised standards

include EN14214 (Europe) and ASTM D-6751 (USA), whereas many other countries

have defined their own standards, which are frequently derived from these two. They

serve as guidelines for production, assure customers that they are buying high-quality

fuels, and provide authorities with approved tools for a common approach to

transport, storage and handling. The most important indicators of a fuels’ properties

include kinetic viscosity, density, cetane number, calorific value, flash point,

oxidation stability, cold temperature properties and iodine value. Experimental

investigations of these properties require specialised equipment, skilled technicians

and biodiesel production on a pilot scale. These things are often costly, especially for

biodiesel from new sources, which is one barrier to the establishment of biodiesel

from new feedstock. Therefore, numerical modelling is a possible alternative to

replace costly experimental studies and hence, accelerate the development of second-

generation biodiesel technology.

Multivariate data analysis (using PCA tools) confirmed a complex relationship

between the chemical composition of biodiesel and individual fuel properties. This

study also indicated that individual methyl esters of fatty acids, average chain length

(ACL), average number of double bonds (ANBD), weight percentages of oxygen

(O), hydrogen (H), carbon (C), saturated fatty acids, mono-unsaturated fatty acids

(MUFA), poly-unsaturated fatty acids (PUFA), mono-glycerol and free fatty acids


(FFA) in the chemical composition of biodiesel have a different level of influence on

fuel properties. For instance, ACL has a very positive influence on cetane number,

whereas it has a moderate or lesser influence on other fuel properties, except

oxidation stability, where no significant influence was found. The most influential

parameters of chemical composition that effected all biodiesel properties in this study

were the presence of PUFAs and ANBDs in the chemical composition of biodiesels.

The findings of the correlation study between chemical composition and biodiesel

properties assisted in the selection of input variables for a particular fuel property

when developing the ANN models. The parameters of chemical composition, which

have a significant influence on this property, were used in the input layer of the ANN

model. MatLab R2012a software was used to train, validate and simulate the ANN

model on a personal computer and the developed ANN prediction models ran

simulations using test data sets, in order to evaluate the estimation performance.

From the simulation results, it was found that the absolute fraction of variance (R2)

was close to unity, ranging from 0.8932 to 0.9622, with Root-Mean-Squared (RMS)

errors ranging from 0.011 to 4.171 and a maximum average error percentage

(MAEP) ranging from 1.86% to 5.53%. The results of this study also show that

ANNs have the ability to learn and generalise a wide range of experimental

conditions for biodiesel.

The reason for developing the ANN models was to estimate the fuel properties of

biodiesel obtained from Australian native plants based on its chemical composition,

instead of having to conduct costly and time consuming experimental studies. For

this purpose, biodiesels were produced on a laboratory scale from eleven non-edible

oil seeds plants, which were Beauty leaf, Candle nut, Blue berry lily, Queen palm,

Castor, Bidwilli, Karanja, Whitewood, Cordyline, Flame tree and Chinese rain. Most

of the native plant biodiesel contained much higher free fatty acids (FFA) compared

to the recommended level (5% by weight) from alkali-trans-esterification. The flame

oil contained 36.7% FFAs, which was highest among the bio-oils, followed by

Beauty leaf (22%), Queen palm (15%) and Blue berry lily (13.1%). Therefore, the

FFA content in non-edible plant feedstock could be one of the major issues inhibiting

the success of biodiesel production from native species. The chemical composition of

biodiesel obtained from native oil seeds were similar to that of conventional


biodiesel and they were mostly rich in triglycerides of Oleic (C18:1) acid, followed

by Stearic (C18:0), Linoleic (C18:2), Palmitic (C16:0) and Linolenic (C18:3) fatty

acids, with the exception of Queen palm and Castor. Queen palm was rich in short

chain saturated fatty acids (C8:0, C10:0, C12:0 and C8:0) and Castor biodiesel was

rich in unsaturated long chain fatty acids (C20:1). Queen palm contained an

exceptionally higher amount of oxygen, accounting for 14.19% on a per weight

basis. The oxygen content of other methyl esters ranged from 10.25 to 11.82%.

Based on their chemical composition, the important fuel properties of biodiesels

were estimated using ANN models and compared with Australian, European and

American biodiesel standards. Results indicated that most of the biodiesel

investigated in this study performed well against biodiesel standards for all quality

indicators, except oxidation stability (OS). Queen palm biodiesel was the only

sample that fulfilled biodiesel standards in terms of OS, due to its very high saturated

fatty acid content. Based on the dry seed production capability, level of difficulty

processing seeds, bio-oil content in the seed kernel, amount of free fatty acids and

estimated fuel properties of biodiesel, the native species feedstock were evaluated

and compared with each other using a multi-criteria decision method (MCDM)

software PORMETHEE-GAIA. In addition, sensitivity analysis of the ranking of

native plant species was conducted by changing the weighting of three important

criteria, being OY, OS and CFPP, which produced significant changes in the ranking

of feedstocks. This study found that Beauty leaf was the top ranked feedstock for

biodiesel production, followed by Queen palm, Castor and Karanja. Overall, Beauty

leaf and Queen palm biodiesel were found to be a good choice for second-generation

biodiesel production in tropical/sub-tropical regions, however, the opposite is true for

cold weather conditions, where Castor, Cordyline or Flame tree might be a better

choice.

As Beauty leaf was found to be one of the most promising feedstock, a detailed

experimental investigation of the species was undertaken at the Central Queensland

University (CQU), using about 140 kg of ground-dried Beauty leaf seeds. Overall, it

was found that Beauty leaf bio-oil extraction using an oil press resulted in a low oil

yield. This drawback was overcome by using a chemical oil extraction method with

n-hexane as the oil solvent. Furthermore, the oil yield increased 3-4% when using

accelerated solvent extraction (ASE) methods, with high pressure and temperature


extraction. The highest oil yield was found to be 51.5% for dry kernels. Overall,

when comparing the quality of non-edible bio-oil with edible vegetable oils, in terms

of acid value, density, kinematic viscosity, surface tension and higher heating value,

Beauty leaf oil showed much higher acid values, resulting from its high free fatty

acid content. Although this made Beauty leaf oil inappropriate for direct base-

catalysed trans-esterification, a two-step esterification process was used in this study:

acid-catalysed pre-esterification and base-catalysed trans-esterification. Due to the

lack of samples, a response surface method (RSM) based on a Box-Behnken design

was employed in order to determine an experimental plan to optimise the Beauty leaf

oil to biodiesel conversion procedure. The effect of reaction parameters such as

methanol to oil molar ratio, catalyst loading and reaction temperature were

investigated in terms of the reduction of FFA content in pre-esterification and ester

content in trans-esterification. The optimal conditions for pre-esterification were 30:1

methanol to oil molar ratio, 10 wt% sulphuric acid catalyst and 75 °C reaction

temperature, which reduced the FFA content to 1.8 wt%. With the aid of statistical

modelling, predicted optimal conditions for the transesterification methanol to oil

molar ratio, catalyst concentration and reaction temperature were 7.5:1, 1% and 55

°C, respectively. Based on these conditions, the highest achievable ester content of

FAME predicted by the model was found to be approximately 93%. In terms of a

linear effect on FFA reduction for the first step, methanol to oil molar ratio was

found to be highly significant and reaction temperature moderately significant. For

trans-esterification, catalyst concentration was found be the most dominant variable

for achieving high ester contents.

Beauty leaf biodiesels mostly comprise esters of saturated Hexadecanoic (C16:0) and

Octadecanoic (C18:0) acid, mono-unsaturated 9-Octadecenoic acid (C18:1) and

poly-unsaturated 9, 12-Octadecadienoic (C18:2) acid. This biodiesel is rich in

saturated methyl esters compared with commercial biodiesels, except the biodiesel

from palm oil. This makes Beauty leaf oil biodiesel preferable in terms of most fuel

properties, including kinematic viscosity, density, higher heating value, oxidation

stability, iodine value, cetane number, flash point and linoleic acid content. On the

other hand, Beauty leaf biodiesels perform relatively poorly in terms of cold

temperature properties and free fatty acid content. However, Beauty leaf biodiesel

foes meet the American, European and Australian biodiesel standards. The


multivariate data analysis using PROMETHEE-GAIA software indicated that

biodiesel produced from Beauty leaf bio-oil could be a better option for use in

automobile engines, compared with many other commercial biodiesels, including

biodiesel from cotton seed, sunflower and soybean oil, especially in tropical/sub-

tropical regions.

The influence of a biodiesels’ chemical composition and physical properties on

diesel particle emissions was experimentally investigated using four biodiesels with

different fatty acid carbon chain length and degrees of unsaturation. The aims of this

investigation were to correlate the findings of these results with the second-

generation biodiesel produced from Australian native plants. This study found a

consistent reduction in particle emissions with increased fuel oxygen content.

Therefore, it can be expected that particle emissions may be lower for an engine

running on Queen palm biodiesel, because of its significantly higher oxygen content

(14.19% by weight). Although the oxygen content of other native plant biodiesels

was found to be similar, the ACL of Castor and Bidwilli biodiesel was found to be

19.90 and 18.52, respectively, which is much higher compared with other biodiesels.

Therefore, these two biodiesel may produce a higher amount of particle emissions.

This study also found that particle emissions decreased linearly with kinematic

viscosity. Overall, it is evident from this section of the study that the chemical and

physical properties of biodiesel may be important parameters impacting on the

performance and emission characteristics of automobile engines when native plant

biodiesels. The last chapter of this thesis (Chapter 9) went on to investigate the use of

Beauty leaf biodiesel in a four-cylinder automobile diesel engine. The important

performance and emission indicators for diesel engines, in terms of brake power,

indicated power, brake thermal efficiency, brake specific fuel consumption, cylinder

pressure, NOx emissions, specific PM emissions and specific PN emissions were

measured and presented graphically. Results indicated that 5% and 10% blends of

Beauty leaf oil biodiesel with diesel fuel can used in conventional diesel engines

without engine modification. Beauty leaf biodiesel reduced the engine power, brake

thermal efficiency, cylinder peak pressure and specific nitrogen oxide (NOx) particle

mass (PM) emissions. At the same time, brake specific fuel consumption and particle

emissions (in terms of number) were found to be higher for Beauty leaf biodiesel

compared to conventional diesel. The variation in engine emissions and performance


using neat diesel versus Beauty leaf biodiesel blends was not surprising and similar

findings have been reported in the literature when testing diesel engines with

commercially available biodiesels. Nevertheless, for a better understanding of the

performance and emission characteristics of Beauty leaf and other native biodiesels,

it may be necessary to run automobile diesel engines using the biodiesels in a pure

form.

9.2 LIMITATIONS AND RECOMMENDATIONS FOR FUTURE WORK

The higher fuel economy, better performance and durability of diesel engines are

making them a dominant power source in both mobile and stationary applications. In

addition, the use of alternative fuels (i.e. biodiesel) is also expanding around the

world. Therefore, more inclusive research initiatives, with collaboration between

different branches of science and engineering, are needed to develop sustainable

biodiesel technologies, in order to produce a secure energy supply in the future.

Having a vast land area and naturally grown non-edible oil seed crops, Australia has

the unique opportunity to become a major supplier of second-generation biodiesel. In

order to take full advantage of this opportunity, multi-disciplinary research needs to

be undertaken. This study explored the potential of eleven Australian native plants as

a feedstock for second-generation biodiesel production from an engineering and

application point of view. For the further development and initiation of the

commercial production of biodiesels, a socio-economic assessment of the use of

these plant species will be required. Investigations from an agricultural point of view

and proper planning for effective land use for oil seed plants will also be essential.

Therefore, more extensive research studies should be undertaken, in collaboration

with plant scientists, engineers, economists and policy makers.

This study found that seed processing methods significantly influenced oil extraction

and yield. Manual seed cracking and kernel extraction is a very time-consuming and

laborious task and given that the size and physical condition of the seeds varies


significantly, even for the same species, further research is required to design

automated seed crushers for native oil seeds. The physical properties of dry seeds

also need to be investigated in terms of geometry, and tensile and compression

strength. After analysing these physical properties, a 3D Cad model can be

developed and simulated for optimising the design. The design of automated seed

crushers should be optimised in such a way that it would not destroy or smash the oil

bearing kernels.

Since the current study only focused on the bio-oil content of Australian native oil

seeds, further research should be conducted for investigating the by-products

obtained during oil extraction. For instance, the seed husk and kernel residue can also

be used for bio-oil, bio-gas and bio-char production through pyrolysis. This would

certainly add the more value to the use of Australian native plants in fuel production.

Moreover, a life cycle assessment of those plants, in terms of energy production and

greenhouse gas reductions, would also produce interesting outcomes.

This study found a good relation between the chemical composition of biodiesel and

various important fuel quality parameters. This research can be further extended for

additional fuel properties such as lubricity, copper strip corrosion, distillation

temperature, fire point, cloud point, pour point etc. Moreover, the physico-chemical

properties investigated in this study indicated that a particular feedstock may be

better for certain quality parameters but not for others. Therefore, biodiesels from

several feedstocks could be blended in order to obtain the desired fuel quality and it

would be interesting to find out the optimum blend ratios for achieving a higher level

of fuel quality.

The developed ANN models for estimating important biodiesel properties showed

good estimation accuracy. However, the performance of ANN models can be further

improved by providing additional data sets while training the ANN networks. The

data sets should cover as much of a range as possible, in order to ensure the most

robust ANN models are developed. Moreover, further research would be worthwhile


for developing a universal ANN model that would be able to predict the combustion

performance of versatile automobile engines and fuel types.

This thesis found that, when using biodiesels and their blends, particle emissions

decreased with the decrease in ACL and increase in oxygen content of biodiesel.

However, biodiesel ACL and oxygen content are interrelated. Oxygen content

increases with a decrease in ACL and vice versa. Therefore, whether oxygen content

or FAME carbon chain length is the dominant reason for particle emission reductions

is not evident from the results reported in this thesis. It would be interesting to design

experiments that will evaluate the effect biodiesel carbon chain length keeping the

oxygen content constant or vice versa.

Due to the limited number and type of samples, this study conducted engine

experiments fuelled with 5% and 10% Beauty leaf biodiesel blended with petroleum

diesel. However, Beauty leaf biodiesel in its pure form needs to be used in diesel

engines, in order to gain for better understanding of its combustion performance. It is

also recommended to test automobile engine performance using the pure form of

other native plant biodiesels.

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266 Appendices

Appendices

APPENDIX A: MatLab code for ANN models training

%Md Jahirul Islam; BERF, QUT, Brisbane %date: August 18, 2014 %** Preparation of training data. clc, clear all; close all; load('C:\Users\n7325819\Desktop\Matlab Codes_Kajol\Data_raw.mat'); Data=Data_raw; % get the data set as you like from initial total data size % percentage of data set for training, testing & validation training_set=0.6 ; validating_set=0.1; testing_set=0.3; %(%) % call the data_Preparation.m file for split the data for training, testing & validation; And prepare the data %for NN traing DataVec = size(Data,2); No_of_Samples = length(Data); %SampleIndex = [1:No_of_Samples]'; % use this if random sample is not allowed SampleIndex = randperm(No_of_Samples)'; % Use this if random sampling is allowed. data_Preparation % call the data preparation m file cd([pwd,'\Results']); save Training_data % save the whole workspace % Prepare the data here for training, testing and validation. %Call the matrix 'Data' with size n*m. First m-1 columns are inputs and last column is the target.) %=================================================================================================== function [TrainSet ValdSet TestSet] = datasplit(A, ptr, pvd, pts) rows = max(size(A)); nVald = round(pvd * rows); % number of samples for validation nTest = round(pts * rows); % number of samples for test nTrain = rows - nVald - nTest; TrainSet = A(1:nTrain,:); ValdSet = A(nTrain+1:nTrain+nVald,:); TestSet = A(nTrain+nVald+1:end,:); %Data=data_PI_dis; %========================================================================== [Index_TrainSet Index_ValdSet Index_TestSet] = datasplit(SampleIndex,training_set,validating_set,testing_set); % Split Data into Test, Validation, and Testing for j = 1:size(Index_TrainSet,1) % Index for Training Set k = Index_TrainSet(j);

Appendices 267

TrainSet(1:DataVec,j) = Data(k,1:DataVec); end for j = 1:size(Index_ValdSet,1) % Index for Validation Set k = Index_ValdSet(j); ValdSet(1:DataVec,j) = Data(k,1:DataVec); end for j = 1:size(Index_TestSet,1) % Index for Testing Set k = Index_TestSet(j); TestSet(1:DataVec,j) = Data(k,1:DataVec); end %=============================================================================================== Data=Data_raw; % get the data set as you like from initial total data size (10,998) %Data=data_NN_PI_ND; % percentage of data set for training, testing & validation training_set=0.7 ; validating_set=0.1; testing_set=0.2; %(%) % call the data_Preparation.m file for split the data for training, testing & validation; And prepare the data %for NN traing DataVec = size(Data,2); No_of_Samples = length(Data); %SampleIndex = [1:No_of_Samples]'; % use this if random sample is not %allowed SampleIndex = randperm(No_of_Samples)'; % Use this if random sampling is %allowed. data_Preparation %=============================================================================================== I = [1:DataVec-1]; % Inputs O = [DataVec]; % Output %=============================================================================================== % Training Set pTr = TrainSet(I,:); tTr = TrainSet(O,:); [pTr,pTrMin,pTrMax,tTr,tTrMin,tTrMax] = premnmx(pTr,tTr); % Preprocessing data with converting the range from -1 to 1. % Validation data pVd = ValdSet(I,:); tVd = ValdSet(O,:); pVd = tramnmx(pVd,pTrMin,pTrMax); tVd = tramnmx(tVd,tTrMin,tTrMax); % Test data pTs = TestSet(I,:); tTs = TestSet(O,:); pTs = tramnmx(pTs,pTrMin,pTrMax); tTs = tramnmx(tTs,tTrMin,tTrMax); MinMax = minmax([pTr pVd pTs]); %===================================================================================================

268 Appendices

Data=Data_raw; % get the data set as you like from initial total data size (10,998) %Data=data_NN_PI_ND; % percentage of data set for training, testing & validation training_set=0.6 ; validating_set=0.1; testing_set=0.3; %(%) % call the data_Preparation.m file for split the data for training, testing & validation; And prepare the data %for NN traing DataVec = size(Data,2); No_of_Samples = length(Data); %SampleIndex = [1:No_of_Samples]'; % use this if random sample is not %allowed SampleIndex = randperm(No_of_Samples)'; % Use this if random sampling is %allowed. data_Preparation % Training the NN to generate *** model % define the training, testing and validating data set %========================================================================= %clear all load('C:\Users\n7325819\Desktop\Matlab Codes_Kajol\Data_raw.mat'); val.P =pVd; val.T=tVd; % Define the validation data test.P = pTs; test.T=tTs; % Define the test data % define the network structure % pTr and tTr are for training data set %========================================================================= Ntotal=10; % total NN models Neur=[6*ones(1,Ntotal/10), 8*ones(1,Ntotal/10), 10*ones(1,Ntotal/10), 12*ones(1,Ntotal/10), 14*ones(1,Ntotal/10), 16*ones(1,Ntotal/10),18*ones(1,Ntotal/10),22*ones(1,Ntotal/10),24*ones(1,Ntotal/10),26*ones(1,Ntotal/10)]; % neuron size in hidden layer MAPE=[]; RMSE=[]; SSE=[]; for netopt=1:Ntotal clear Net1 an MAPE1 RMSE1 SSE1 neur=Neur(netopt) mout=1; % the number of moel output Net1=newff(minmax(pTr),[neur mout],{ 'tansig' 'purelin'}, 'trainbr'); % tansig= tan sigmoid transfer function for hidden neuron input % purelin=linear output function for hidden neuron % Set training parameters % Net.trainParam.epochs=500; % Maximum number of epochs Net1.trainParam.show=100; % Period of showing calculation progress Net1.trainParam.lr=0.1; % Algorithm learning rate Net1.trainParam.goal=.01; % Optimisation goal Net1.trainParam.min_grad=1e-10; % Minimum gradient Net1.trainParam.mem_reduc=1; % Memory reduction parameter Net1.trainParam.max_fail=1000; time0 = cputime; % Use the 'train' command to start the training process. The trained % network will be saved in the structure Net. pTr and tTr are input and

Appendices 269

% targets for the training data set. val and test are the validation and testing data sets respectively. %Net = train(Net,pTr,tTr); [Net1,tr]=train(Net1,[pTr,pVd,pTs],[tTr,tVd,tTs],[],[], val,test); %========================================================================= % Simulate the network with the testing (normalized) data. an = sim(Net1,pTs); % Un-normalize the network prediction data % convert the data as real values a = postmnmx(an,tTrMin,tTrMax); Output=postmnmx(tTs,tTrMin,tTrMax); close all figure(3); plot(a,Output);ylabel('Actual'); xlabel('Predicted'); figure(4); plot(Output); hold; plot(a,'m'); legend('Actual','Predicted'); MAPE1 = mean(abs((tTs-an)./tTs))*100 RMSE1=sqrt((sum((tTs-an).^2))/size(tTs,2)); SSE1=sum((tTs-an).^2); MAPE=[MAPE,MAPE1]; RMSE=[RMSE,RMSE1]; SSE=[SSE,SSE1]; NNtrain(netopt)=struct('NNmodel',Net1, 'trecord', tr, 'MAPE',MAPE1,'RMSE1',RMSE,'SSE1',SSE,'actual',Output,'Predicted',a); end %========================================================================== cd([pwd,'\Results']); save NNtrain %==========================================================================

270 Appendices

APPENDIX B: The eigenvalue for each of the PCs

CN KV Density HHV OS CFPP FP IV

PC 1 13.8644 13.3400 10.0326 12.0796 11.5437 12.0405 11.1067 13.3676

PC 2 4.8300 5.5200 6.7413 5.3383 6.7804 6.0536 5.7753 6.0168

PC 3 1.8400 2.7050 1.7298 2.2664 2.1142 2.0142 2.5236 1.5142

PC 4 0.8820 1.0232 1.8750 1.7064 1.5755 1.3542 1.7066 1.1954

PC 5 0.6046 0.2555 1.8756 1.1426 0.7002 0.7764 0.5986 0.3072

PC 6 0.4692 0.1122 0.4364 0.2334 0.1409 0.4972 0.3698 0.1960

PC 7 0.0951 0.0311 0.1827 0.1396 0.0843 0.2662 0.2962 0.1509

PC 8 0.0962 0.0226 0.0738 0.0564 0.0341 0.0267 0.3302 0.1750

PC 9 0.0759 0.0149 0.0157 0.0120 0.0072 0.0057 0.0701 0.0372

PC 10 0.0710 0.0110 0.0115 0.0088 0.0053 0.0042 0.0516 0.0273

PC 11 0.0643 0.0102 0.0107 0.0082 0.0050 0.0039 0.0480 0.0255

PC 12 0.0516 0.0071 0.0075 0.0057 0.0035 0.0027 0.0335 0.0178

PC 13 0.0365 0.0045 0.0047 0.0036 0.0022 0.0023 0.0287 0.0152

PC 14 0.0132 0.0003 0.0003 0.0003 0.0002 0.0012 0.0147 0.0078

PC 15 0.0059 0.0025 0.0026 0.0020 0.0012 0.0009 0.0117 0.0062

PC 16 0.0011 0.0011 0.0022 0.0017 0.0010 0.0008 0.0098 0.0052

PC 17 0.0005 0.0005 0.0016 0.0012 0.0007 0.0006 0.0078 0.0041

PC 18 0.0003 0.0003 0.0015 0.0012 0.0007 0.0005 0.0075 0.0040

PC 19 0.0002 0.0002 0.0011 0.0009 0.0005 0.0004 0.0055 0.0029

PC 20 0.0002 0.0002 0.0008 0.0006 0.0003 0.0003 0.0040 0.0021

PC 21 0.0001 0.0001 0.0005 0.0001 0.0000 0.0000 0.0000 0.0000

PC 22 0.0000 0.0000 0.0002 0.0000 0.0000 0.0000 0.0000 0.0000

PC 23 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000

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