KINETICS OF POPLAR SHORT ROTATION COPPICE

OBTAINED FROM THERMOGRAVIMETRIC AND DROP

TUBE FURNACE EXPERIMENTS

Paula Pamplona Côrte-Real Martins

Thesis to obtain the Master of Science Degree in

Mechanical Engineering

Supervisor: Prof. Mário Manuel Gonçalves da Costa

Examination Comittee

Chairperson: Prof. João Rogério Caldas Pinto

Supervisor: Prof. Mário Manuel Gonçalves da Costa

Member of the Commitee: Prof. Edgar Caetano Fernandes

May 2016

i

Dedication

To my parents

To my sister

To Ricardo

ii

Resumo

A presente tese examina as características da combustão de biomassa de choupo, proveniente de

talhadia de curta rotação, através de ensaios de termogravimetria e num reator de queda livre. Foram

estudadas três amostras de choupo, especificamente clones AF8 obtidos de duas plantações

diferentes, uma localizada em Itália (AF8-I) e outra localizada em Portugal (AF8-P), e o clone AF2,

oriundo de uma plantação localizada em Portugal. Inicialmente, o processo de combustão dos três

clones foi avaliado por termogravimetria. Para o processo de desvolatilização, os resultados da

termogravimetria originaram valores de energias de ativação aparentes de 118 kJ mol-1 para o clone

AF8-I, 117 kJ mol-1 para o clone AF8-P, e 109 kJ mol-1 para o clone AF2. Para o processo de

oxidação do resíduo carbonoso, os valores de energia de ativação aparentes foram 267 kJ mol-1,

251 kJ mol-1 e 264 kJ mol-1 para os clones AF8-I, AF8-P e AF2, respetivamente. No reator de queda

livre foi analisado somente o processo de combustão do clone AF8-I. Os testes incluíram medidas de

temperatura e oxidação das partículas ao longo do reator de queda livre para cinco temperaturas

diferentes (900, 950, 1000, 1050 e 1100 ºC). Os resultados obtidos para as energias de ativação

neste caso foram significativamente inferiores aos obtidos através da termogravimetria, revelando o

impacto significativo das condições experimentais nos dados cinéticos.

Palavras-chave: Termogravimetria; Reator de queda livre; Biomassa; Talhadia de curta rotação;

Culturas energéticas; Cinética da combustão.

iii

Abstract

The present thesis examines the combustion characteristics of poplar short rotation coppice obtained

from thermogravimetric and drop tube furnace experiments. Three poplar samples were studied;

specifically, clone AF8 from two different plantations, one located in Italy (AF8-I) and another located

in Portugal (AF8-P), and clone AF2 from a plantation located in Portugal. Initially, the combustion

behaviour of the three clones was evaluated by thermogravimetry. Under oxidizing conditions, for the

devolatilization stage, the thermogravimetric experiments yielded apparent activation energies of

117 kJ mol-1 for clone AF8-P, 109 kJ mol-1 for clone AF2 and 118 kJ mol-1 for clone AF8-I. For the

oxidation stage, the apparent activation energies were 251 kJ mol-1, 267 kJ mol-1 and 264 kJ mol-1 for

clones AF8-P, AF8-I and AF2, respectively. Subsequently, the combustion behaviour of the clone

AF8-I was examined in a drop tube furnace. The data reported includes gas temperature and particle

burnout measured along the drop tube axis for five furnace wall temperatures (900, 950, 1000, 1050

and 1100 ºC). Kinetic data was calculated using a model-fitting method and a numerical model,

yielding similar results for the apparent activation energy. In this case, the results obtained for the

activation energies were considerably lower than the results obtained by means of thermogravimetry,

showing that the experimental conditions can heavily influence the kinetic data.

Keywords: Thermogravimetry; Drop tube furnace; Biomass; Short rotation coppice; Energy crops;

Combustion kinetics.

iv

Acknowledgements

First, I would like to thank Professor Mário Costa for giving me the opportunity to participate in a work

that I so truly enjoyed doing, and also for all the help and guidance.

A big thanks to Sandrina Pereira, without whom it would have been so much harder. I learned a lot.

To all my laboratory companions, in special to Vera Branco, Ulisses Fernandes and Mr. Pratas, I

express my gratitude for all the assistance provided throughout my experimental work.

To my family: my parents, my godparents, my grandparents, and also Veronica, Tom, Frederico and

Leonor, my sincere thanks for all the support and motivation.

I would like to thank João Cunha, André Moço, João Gaspar, Mafalda Magro, João Magrinho, André

Antunes, Vitor Santos and Raquel Ramos for all the support, help and friendship provided throughout

these past months. In particular, I would to thank Leonor Neto, Joana Vieira and Soraia Ribeiro for

being there since day one.

Finally, I would like to thank Ricardo: it wouldn’t have been the same without you.

v

Index

Dedication ................................................................................................................................................i

Resumo .................................................................................................................................................... ii

Abstract .................................................................................................................................................. iii

Acknowledgements ............................................................................................................................... iv

List of Tables ........................................................................................................................................ vii

List of Figures ........................................................................................................................................ ix

Nomenclature ......................................................................................................................................... xi

Abbreviations ....................................................................................................................................... xiii

1. Introduction ........................................................................................................................................ 1

2. Literature Review ............................................................................................................................... 3

2.1. TGA ............................................................................................................................................... 3

2.2. Experimental reactors ................................................................................................................... 5

2.3. Comparative studies ..................................................................................................................... 7

3. Experimental ...................................................................................................................................... 8

3.1. Biomass fuels ................................................................................................................................ 8

3.2. TGA ............................................................................................................................................. 10

3.2.1. Apparatus and testing conditions ......................................................................................... 10

3.2.2. Methods ................................................................................................................................ 11

3.3. DTF ............................................................................................................................................. 12

3.3.1. Apparatus and testing conditions ......................................................................................... 12

3.3.2. Methods ................................................................................................................................ 14

4. Kinetic Modelling ............................................................................................................................. 16

4.1. TGA ............................................................................................................................................. 16

4.2. DTF ............................................................................................................................................. 19

4.2.1. Model-fitting method ............................................................................................................. 21

4.2.2. Particle-size model ............................................................................................................... 22

4.3. Weighted average activation energy .......................................................................................... 27

5. Results and Discussion .................................................................................................................. 29

vi

5.1. Experimental analysis ................................................................................................................. 29

5.1.1. TGA ...................................................................................................................................... 29

5.1.2. DTF ...................................................................................................................................... 33

5.2. Kinetic analysis ........................................................................................................................... 35

5.2.1. TGA ...................................................................................................................................... 35

5.2.2. DTF ...................................................................................................................................... 38

5.2.2.1. Model-fitting method ...................................................................................................... 38

5.2.2.2. Particle size distribution model ...................................................................................... 41

5.2.2.3. Comparison between methods ...................................................................................... 48

5.2.3. TGA versus DTF .................................................................................................................. 48

6. Conclusions and Future Work ....................................................................................................... 50

7. References ....................................................................................................................................... 51

vii

List of Tables

Table 3.1 - Properties of the biomass fuels. ............................................................................................ 9

Table 3.2 - TA INSTRUMENTS SDT 2960 simultaneous DSC-TGA specifications [29]. ..................... 11

Table 4.1 – Input experimental conditions for the model-fitting method. .............................................. 22

Table 4.2 – Input AF8-I data for the model-fitting method. .................................................................... 22

Table 4.3 - Input experimental conditions for the numerical model....................................................... 26

Table 4.4 - Input AF8-I data for the numerical model. ........................................................................... 26

Table 4.5 - Input gas properties for the numerical model. ..................................................................... 27

Table 4.6 - Input heat data for the numerical model. ............................................................................ 27

Table 5.1 - Combustion characteristics of each biomass fuel. .............................................................. 30

Table 5.2 - Combustion characteristics of biomass fuels from the literature (second stage). .............. 31

Table 5.3 - Combustion characteristics of biomass fuels from the literature (third stage) .................... 32

Table 5.4 - Burnout obtained for each DTF temperature along the reactor for the AF8-I. .................... 33

Table 5.5 - Kinetic parameters (TGA). .................................................................................................. 36

Table 5.6 - Poplar kinetic parameters reported in the literature (TGA). ................................................ 37

Table 5.7 - Kinetic parameters for the model-fitting method for the AF8-I (DTF). ................................. 39

Table 5.8 - Stage kinetic parameters for the model-fitting method for the AF8-I (DTF). ....................... 39

Table 5.9 - Apparent α for the AF8-I. ..................................................................................................... 40

Table 5.10 - Kinetic parameters for the DTF section between 200 to 700 mm for the model-fitting

method for the AF8-I (DTF). .................................................................................................................. 41

Table 5.11 - Predicted kinetic parameters for the AF8-I. ...................................................................... 41

viii

Table 5.12 - Kinetic data obtained for both scenarios. .......................................................................... 45

Table 5.13 - Kinetic data comparison for the AF8-I (DTF). ................................................................... 48

Table 5.14 - Kinetic data comparison under slow and fast combustion for the AF8-I. .......................... 49

ix

List of Figures

Figure 3.1 - Samples after crushing and sieving: (a) AF8-I, (b) AF8-P, (c) AF2. .................................... 8

Figure 3.2 - Malvern 2600 Particle Size Analyser [6]. ........................................................................... 10

Figure 3.3 - TA INSTRUMENTS SDT 2960 simultaneous DSC-TGA apparatus [6]. ........................... 10

Figure 3.4 - DTF with numbered components: (1) Biomass feeding system; (2) Furnace; (3) Flow

meters; (4) Probe and probe support. ................................................................................................... 12

Figure 3.5 - Schematics of the DTF with numbered components: (1) Biomass feeding system; (2)

Furnace; (3) Probe system for sample collection [24, 31]. .................................................................... 13

Figure 5.1 - TG curves. .......................................................................................................................... 29

Figure 5.2 - DTG curves. ....................................................................................................................... 29

Figure 5.3 - Temperature and burnout axial profiles for each DTF temperature. ................................. 34

Figure 5.4 - Kinetic plot for the AF8-P. .................................................................................................. 35

Figure 5.5 - Kinetic plot for the AF8-I. ................................................................................................... 35

Figure 5.6 - Kinetic plot for the AF2. ...................................................................................................... 35

Figure 5.7 - Model-fitting plots for the different sections of the DTF for the AF8-I. ............................... 38

Figure 5.8 - Model-fitting plot for the DTF section 200 to 700 mm for the AF8-I. .................................. 40

Figure 5.9 - Devolatilization error plot for the numerical model for the AF8-I. ...................................... 42

Figure 5.10 - Char oxidation error plot for the numerical model for the AF8-I. ..................................... 42

Figure 5.11 - Experimental and predicted burnout for the AF8-I. .......................................................... 43

Figure 5.12 - Oxidation error plot for the first scenario of the sensitivity analysis for the AF8-I. ........... 45

Figure 5.13 - Oxidation error plot for the second scenario of the sensitivity analysis for the AF8-I. ..... 45

Figure 5.14 - Experimental and predicted burnout for the AF8-I in the first scenario. .......................... 46

x

Figure 5.15 - Experimental and predicted burnout for the AF8-I in the second scenario. .................... 47

xi

Nomenclature Latin Symbols Defini tion

� Area

�� Pre-exponential factor for the oxidation

�� Pre-exponential factor

�� Pre-exponential factor for the devolatilization

� DTF point

� DTF temperature

� Char content

� Specific heat

Diffusion

� Diameter

� Activation energy

� � Activation energy for the oxidation

� � Activation energy for the devolatilization

� ,� Activation energy for the weighted average equation

� ,�� Activation energy for the weighted average equation in devolatilization

� Mass-flow rate of gas injected into the DTF

� Integral reaction model

� Gravity acceleration

� Heat

� Reaction rate constant

�� Reaction rate constant for the devolatilization

�� Reaction rate constant for the oxidation

� Weight

� Number of biomass particles

�� Nusselt number

� Reaction order

� Partial pressure

� Heat transfer

� Universal gas constant

����� Diffusion resistance

�ℎ Sherwood number

! Temperature

! Temperature of the particle

" Temporal quantity

xii

# Unburnt fraction

$ Volatile matter

$ Volume of the particle

% Stoichiometric ratio

% Velocity

&'( Fixed carbon content

&� Volatile matter content

) Molecular mass + Mass fraction

Greek Symbols Definition

, Conversion factor

- Heating rate

. Relative error

/ Emissivity

0 Thermal conductivity

1 Viscosity

2 Burnout

3 Density

4 Stephan-Boltzmann constant

5� Ash weight fraction in input biomass

56 Ash weight fraction in the collected sample

5� Mass fraction of class 7

xiii

Abbreviations GHG Greenhouse Gases

SRC Short-Rotation Coppice

TGA Thermogravimetric Analysis

DTF Drop Tube Furnace

CR Coats-Redfern

ASTM American Society for Testing and Materials

DSC Differential Scanning Calorimeter

DTG Derivative Thermogravimetry

EFR Entrained Flow Reactor

1

1. Introduction

Energy needs have changed throughout history. In the past 70 years, societies’ demand for energy

has augmented at a fast pace, confirming that the increased complexity of the modern ages and

energetic necessity growth are intrinsically connected. This necessity has lead mankind to an

unbearable fossil fuel need and it has had consequences. Statistics show that the majority of

greenhouse gas (GHG) emissions come from energy conversion [1], causing negative consequences

to the environment. Climatic changes have grown noticeable and air pollution became a hazard.

Energy crops are dedicated cultures grown and harvested in order to produce energy in different

forms such as a solid, liquid or a gas state. One interesting approach to energy crops are short-

rotation coppices (SRC). In essence, “coppicing” is a term that refers to uniformly felling crops

cyclically, allowing the trees to re-grow from the stump.

SRC poplar wood has been recognized as a favourable species for different reasons. The core

advantages of this culture are centred upon its extreme growth rate, its capability of propagation by

hardwood cutting and its genetic crossability, through either conventional breeding or biotechnology.

Its use has also been advocated by temperate climate regions [2].

Combustion is the most common thermo-chemical method of energy conversion undergone at an

oxidizing atmosphere [3]. Experimental techniques, such as the thermogravimetric analysis (TGA) or

laboratory-scale reactors, have been used to evaluate the burning performance of solid fuels in

boilers.

Chemistry kinetics studies the reaction rate of a reaction and what can influence this rate [4]. The

reaction rate depends on a reaction rate constant that can be calculated with the Arrhenius equation.

Two parameters stand out in this equation: the pre-exponential factor and the activation energy. A

chemistry reaction depends on both these parameters, since it will only occur if the collision between

molecules happens with enough energy.

One way of creating or improving energy conversion systems is to comprehend the combustion

process and its complexity. The main purpose of this thesis is to evaluate the kinetic properties of

SRC poplar wood and what can influence them and to contribute to the understanding of the

combustion process by analysing the differences between slow and fast combustion kinetics. Slow

combustion was assessed by means of the TGA while fast combustion was performed on a drop tube

furnace (DTF).

This thesis is structured in six chapters, this being the first.

2

Chapter two – Literature Review – overviews past studies most relevant to this thesis. Either under

slow or fast combustion, all referenced literature includes kinetic studies. For slow combustion, TGA

studies on energy crops or woody biomass with the same kinetic calculation method are emphasized.

Fast combustion studies performed in laboratory-scale reactors using biomass as fuel are highlighted.

Finally, comparative studies of slow and fast combustion are addressed.

Chapter three – Experimental – concentrates on the experiments. This chapter starts by presenting

the three samples of SRC poplar wood studied, including the chemical composition and particle-size

distribution. Subsequently, the two apparatus used, the test conditions and the methodology used are

fully described.

Chapter four – Kinetic Modelling – describes the formulation and the general assumptions for the

different kinetic calculation methods, including the equations, the input data and the calculation tools.

For slow combustion or TGA, the Coats-Redfern method (CR) is applied. For fast combustion or DTF,

a model-fitting method and a numerical model are used in the calculations. To close, the global kinetic

equation is addressed.

Chapter five – Results and Discussion – first presents the experimental results for the TGA and the

DTF. Subsequently, the kinetic data calculated for slow and fast combustion are assessed. For the

DTF, results obtained with each calculation method are presented and compared. The chapter ends

with comparisons between slow and fast kinetic data.

Chapter six – Conclusions and Future Work – draws the final remarks and foresees paths for future

research.

3

2. Literature Review

This chapter overviews past studies using experimental set-ups such as the TGA and laboratory-scale

reactors. The wide variety of available studies performed in TGAs caused this review to prioritize

experiments under oxidizing atmosphere and, more specifically, kinetics of energy crops. Fast

combustion studies performed in laboratory-scale reactors will focus on kinetic calculations with

biomass as fuel. A comparative review in line with this thesis is also presented.

2.1. TGA

Pioneering investigation on combustion was initially performed by means of thermogravimetry. This

technique allows measuring heat flows and weight loss of a sample with increasing temperature,

under controlled conditions [5, 6]. The results from TG indicate the existence of three different stages:

drying and heating, devolatilization and char oxidation [7].

Despite other techniques that will be address later in this state-of-the-art review, TGA continues to be

the most commonly used technique to study the properties and kinetic parameters of potential

biomass fuels of various natures, particularly under pyrolytic conditions [7]. Its versatility is of

paramount importance when it comes to fast chemical analysis and combustion simulation [8]. Its

simplicity and inexpensive characteristics are also seen as attractive features [8, 9].

There are several methodologies to calculate kinetic parameters of solid fuels. Calculations can be

carried out under isothermal or non-isothermal conditions, falling in one of two categories: model-free

and model-fitting. The different existing methods are classified and discussed in [10].

The Flynn-Wall and Ozawa, Kissinger and ASTM methods are examples of model-free non-isothermal

methods [11]. Kok et al. [12] investigated the combustion behaviour of Miscanthus, poplar and rice

husk using a differential scanning calorimeter (DSC) and TG at five heating rates (5, 10, 15, 25 and

50 ºC min-1). The kinetic analysis calculations were done using these methods. The study showed that

biomass combustion has two main stages: the combustion of the light volatiles and the combustion of

the carbonaceous residue. Comparisons between calculated activation energies showed that poplar

wood has the lowest activation energy of the three biomass fuels. Rice husk releases more heat than

Miscanthus and poplar, but has higher activation energy due to its higher ignition temperature.

Numerical methods to calculate the kinetic parameters have also been developed. In general, these

methods model the devolatilization and the oxidation processes independently. Karampinis et al. [9]

calculated the kinetic parameters of five energy crops (cardoon, Miscanthus, Paulownia, willow and

poplar) considering parallel first-order reactions during pyrolysis and accounting for char heterogeneity

4

on a power law model described by a single reaction (except for cardoon). The kinetic parameters

varied with the reaction profile. The authors concluded that woody biomass and cardoon yielded

higher char activation energies. The TGA was conducted at one heating rate (10 ºC min-1).

Model-fitting methods have the advantage of calculating the three kinetic parameters (apparent

activation energy, pre-exponential factor and order of reaction). However, calculating accurate

reaction orders is not possible, since a solid-state mechanism is assumed. Several studies [13, 14, 15]

summarized the different types of mechanisms. With model-free methods these issues vanish

because there is no need of assuming a model, but these do not allow the determination of the

apparent pre-exponential factor or reaction orders.

Different model-fitting methods have been developed. Jeguirim et al. [16] calculated the kinetic

parameters for two herbaceous energy crops (Miscanthus and giant cane – Arundo donax) with a

method described elsewhere [17]. Results showed similar activation energies for both biomass fuels,

reaction orders different than one and higher linear correlation for the devolatilization. Their study also

included TG and gas/particulate matter emissions. The TG study was conducted under an oxidative

atmosphere at one heating rate (5 ºC min-1). Three main decomposition stages were highlighted:

dehydration, devolatilization and oxidation. It is also noted that the shape of the TG and DTG curves

are equivalent. Despite this similarity, temperatures ranges, weight losses and thermal degradation

were different due to the chemical composition.

The Coats-Redfern (CR) kinetic method is a non-isothermal model-fitting method. The advantage of

this method is that it can be used to calculate the kinetic parameters for TGA studies conducted at

only one heating rate. Model-free methods are not able to do this – multiple heating rates are needed

to perform the calculations. Besides, the CR method has been widely used in biomass studies, in

particular in TGA conducted under inert atmospheres.

Yorulmaz et al. [15] studied the combustion kinetics of different waste wood samples (untreated pine

and treated MDF, plywood and particleboard) with a TGA at different heating rates (10, 20 and

30 ºC min-1). Different solid-state reaction mechanisms of the CR method were tested to examine the

performance of the different mechanisms. Three different stages of decomposition were considered

for each biomass and the choice of solid-state mechanisms varied from biomass to biomass. Overall,

the diffusion mechanisms are considered to be more effective in all stages. However, the first-order

mechanism is particularly accurate in the second stage.

López-González et al. [18] studied the combustion characteristics and kinetic parameters of the main

structural components of biomass (cellulose, xylan and lignin) and three biomass of woody nature (fir

wood, eucalyptus wood and pine bark) with a TGA-MS at different heating rates (10, 20, 40 and

80 ºC min-1). Decomposition was divided into two major stages: devolatilization and char oxidation. For

5

the estimation of the kinetic parameters the oxidation stage was further divided in sub-stages,

depending on the sample. For the lignocellulosic samples, the first-order mechanism was found

suitable.

Fang et al. [19] developed an average global process model based on the kinetic data calculated with

the CR method estimated for a first-order solid-state reaction mechanism. Four different biomass

samples were subject to one heating rate under air atmosphere (30 ºC min-1). Three stages were

identified: water evaporation, release and combustion of the volatile matter and char combustion.

To the author’s best knowledge, only López-González et al. [20] studied the kinetic parameters using

the CR method for energy crops of both woody (black spruce and willow) and herbaceous (common

reed, reed phalaris and switch grass) biomass with TGA and DSC (differential scanning calorimetry)

tests. Two main decomposition stages were considered and the kinetic data was estimated for both

stages, further dividing the oxidation stage in two different stages.

Apart from Slopiecka et al. [10], whose study focuses on SRC poplar pyrolysis and model-free kinetic

calculations, studies on SRC poplar combustion and model-fitting kinetic calculations have never been

attempted. This thesis applies the CR method to SRC poplar combustion.

All studies with relevant results and similar chemical composition to the samples studied in this thesis

are addressed in Chapter 5.

2.2. Experimental reactors

TGs are the most common approach to characterize fuels. However, the TG cannot be used to

simulate typical industrial boiler conditions. In order to do so, researchers usually use furnace

systems, typically called drop tube furnaces (DTF) or entrained flow reactors (EFR), depending on the

terminal velocity of the particle. These reactors can achieve high heating rates, high temperatures and

truthful combustion atmospheres, while the evolution of the particle is monitored [8, 21].

Jiménez et al. [22] carried out pyrolysis and combustion experiments on pulverized biomass in an EFR

with varying temperatures for devolatilization (800, 930, 1040, and 1175 ºC), oxidation (1040, 1175

and 1300 ºC) and oxygen concentrations. Samples were collected along the furnace so that particle

unburnt fraction could be determined using an ash-tracer method. The unburnt data was used to

derive the kinetic parameters for the devolatilization and oxidation processes using a numerical model

proposed in [23]. Devolatilization kinetic parameters were first obtained from experimental pyrolysis

and subsequently used to calculate the oxidation kinetic parameters. Comparisons between

experimental data and numerical predictions showed good agreement, indicating that a simple model

can represent truthfully the combustion behaviour. The authors also emphasized that an increase in

6

particle diameter can delay the devolatilization process and affect the kinetic parameters. This model

is described in detail in section 4.2.2.

Wang et al. [24] performed a comparative study on different biomass fuels and coal using a DTF. The

experimental work included measurements of temperature, burnout and gas species concentration

axial profiles for one DTF temperature (1100 ºC) and one-size particle distribution. Experimental data

was analysed using an in-house numerical model and a commercial code. In the numerical model,

devolatilization was described by a single-step devolatilization and an Arrhenius equation, while char

combustion takes into account external diffusion and apparent kinetics. Temperature measurements

indicated a fast increase from the initial temperature due to the combustion of the volatiles. Burnout

data indicated that volatile matter content and particle size distribution are critical parameters. Kinetic

parameters for coal were considerably higher than those for biomass during devolatilization, but for

char combustion were similar for both coal and biomass. The obtained values were in line with the

literature.

Model-fitting methods can also be used to perform faster kinetic calculations. Costa et al. [25] used a

simplified combustion model (cf. section 4.2.1), and the experiments were carried out for five DTF

temperatures (900, 950, 1000, 1050, and 1100 ºC). Experimental data included temperature and

particle burnout axial profiles. The burnout data was used to derive the kinetic parameters. The

combustion kinetics model described devolatilization by a single-step devolatilization law and an

Arrhenius equation, while oxidation was modelled on the outer particle surface and external diffusion.

Results showed higher apparent activation energy and pre-exponential factor in the oxidation sections

of the reactor, but lower linear correlations.

Farrow et al. [21] calculated the kinetic data using an Arrhenius plot, producing similar results to those

in the literature. A pyrolytic study on a DTF was conducted at different temperatures (900, 1100 and

1300 ºC) and atmospheres (CO2 and N2), with pinewood as fuel. Volatile yields were calculated with

an improved tracer method that, instead of using ash, uses silica. Results showed that higher

residence times and/or temperatures originate higher volatile yields.

Accurately describing the complexity of the combustion process is a difficult task [8]. The majority of

studies carried out on experimental reactors have been on coal or coal blends as fuels and not many

studies on biomass as fuel are available. In particular, SRC poplar experiments have never been

attempted. In this thesis, two different methods of calculating the kinetic parameters under fast

combustion are used.

7

2.3. Comparative studies

According to [26], comparing TGA and DTF results is a difficult task because results obtained with one

technique may not be extendable to the other.

Zellagui et al. [27] used a DTF to perform pyrolysis on coal and biomass at different temperatures

(600 – 1400 ºC) and atmospheres (N2 and CO2). TGA was also conducted on both solid fuels

(20 ºC min-1, N2 and CO2). The purpose was to compare the behaviour of both fuels under fast and

slow heating rates. DTF experimental data included temperature and burnout profiles. It is highlighted

that one of the most important differences in fast and slow pyrolysis is the volatile yield. Higher heating

rates originate faster and higher maximum volatile yield than slow heating rates. Due to high heating

rates, gases build up more pressure within the pore structure, resulting in increased conversion. Also,

TG/DTG curves of the chars produced by the DTF/TGA show that conversion happens at lower

temperatures for the DTF yields, explained by the structural differences of both chars.

8

3. Experimental

The experimental work reported in this thesis includes TGA and DTF experiments. The TGA was

applied to three biomass fuels. Since the TGA gave similar results for the three samples (cf. section

5.1.1), only one biomass fuel was examined in the DTF.

This chapter describes the experimental work performed in this thesis. First, the chemical

characteristics of the biomass samples are presented. Subsequently, the experimental set-ups (TGA

and DTF) and methods used are described in detail.

3.1. Biomass fuels

Figure 3.1 shows the three poplar samples (AF8-I, AF8-P and AF2) studied in this work. AF8-I is an

AF8 clone cultivated in Savigliano, Italy. AF8-P, which is also an AF8 clone, and AF2 were both

cultivated in Santarém, Portugal. All cultivations were irrigated. The Italian plantations received a pre-

emerge herbicide (pendimetalin or oxifluorfen), before planting and a post-emerge herbicide

(glufosinate ammonium) in March of the second year. The Portuguese plantations were fertilized with

N: 14:14 at 200 kg ha-1. The clones were crushed and sieved with a 1 mm sieve mesh.

(a) (b) (c)

Figure 3.1 - Samples after crushing and sieving: (a) AF8-I, (b) AF8-P, (c) AF2.

Table 3.1 shows the proximate, ultimate, ash analysis, lower heating value (LHV) and particle size

distribution for each biomass sample. AF8-I has the lowest moisture and ash content and the highest

volatile matter content. It can be observed that the ultimate analysis (C, H, N, S, and O) gives similar

values for the three biomass samples. The relatively high ash content of the AF2 is attributed to soil

contamination of the sample during the collection process.

9

Table 3.1 - Properties of the biomass fuels.

Parameter AF8-I AF8-P AF2

Proximate analysis (wt.%, as received)

Moisture content 8.9 9.1 10.1

Volatile matter 73.8 73.3 69.2

Ash 0.8 1.0 3.4

Fixed Carbon 16.5 16.6 17.3

Ultimate analysis (wt.%, daf)

C 45.80 46.70 49.10

H 6.50 6.10 6.50

N 0.30 0.10 0.10

S 0.03 0.03 0.03

O* 47.40 47.00 44.30

Ash analysis (wt.%, dry basis)

Al2O3 2.260 2.670 2.460

CaO 33.500 36.400 38.100

Cl 0.035 0.051 0.023

Fe2O3 0.892 0.578 0.820

K2O 10.600 11.800 10.500

MgO 12.200 16.800 22.500

MnO 0.140 0.180 0.780

Na2O 2.940 2.300 1.100

P2O5 23.900 18.700 13.000

SiO2 4.890 3.460 6.270

SO3 8.170 6.180 3.460

Others 0.470 0.880 0.990

LHV (MJ/kg) 18.2 19.2 17.5

Particle size (µm) (%)

Under 50 9.4 5.3 8.8

Under 100 14.4 8.3 14.3

Under 300 28.7 21.3 31

Under 500 43 35.1 45.4

Under 700 64.6 56.7 62.7

Under 1000 83.5 78.1 80.2

Under 1500 100 100 100

*Obtained by difference.

10

Figure 3.2 shows the Malvern 2600 Particle Size Analyser used for the determination of the particle

size distribution of the biomass samples (cf. Table 3.1). The instrument is based on the Fraunhofer

diffraction of a monochromatic parallel beam of light by particles in motion [28].

Figure 3.2 - Malvern 2600 Particle Size Analyser [6].

3.2. TGA

3.2.1. Apparatus and testing conditions

The TG tests were carried out in a TA INSTRUMENTS SDT 2960 simultaneous DSC-TGA apparatus,

which is capable of measuring temperature, heat flow and weight throughout time, in a controlled

environment. The samples were heated from 30 to 1100 ºC at constant heating rate (10 ºC min-1),

under air atmosphere. Figure 3.3 shows the apparatus used for the present tests and Table 3.2 lists its

main characteristics.

Figure 3.3 - TA INSTRUMENTS SDT 2960 simultaneous DSC-TGA apparatus [6].

11

Table 3.2 - TA INSTRUMENTS SDT 2960 simultaneous DSC-TGA specifications [29].

Parameter Value

Temperature range (ºC) Ambient to 1500

Maximum sample capacity (mg) 200

Balance sensitivity (µg) 0.1

Calorimetric accuracy (%) ± 2

Calorimetric precision (%) ± 2

Purge gas rate (L min-1) Up to 1

Temperature calibration 1 to 5 points

Thermocouples Platinum/Platinum-Rhodium (Type R)

Data collection rate (sec point-1) 0.5 to 1000

3.2.2. Methods

The TGA is usually performed by means of two curves. The TG represents the weight loss while the

DTG (derivative thermogravimetry) represents the weight loss rate, both as a function of the

temperature.

Different significant occurrences can be identified recurring to a DTG profile. The initial decomposition

temperature for the second stage (Tin) is defined as the point where the weight loss rate reaches

1 % min-1, for the first time, after the drying and heating stages of the particle [30]. The burnout

temperature (Tb) is defined as the point immediately before the weight loss rate reaches 1 % min-1 [8].

Both temperatures are also indicators of stage beginning and ending. The maximum weight loss rate

(Wi, max) and temperature at which this phenomenon occurs (Ti, max) are also identifiable.

In this study, several TGA tests were performed using different samples of each clone to ensure their

representability. The selection of the suitable experiments was based on the initial weight of each

sample (5 ± 1 mg). After this preliminary screening, three sets of sample data of each biomass were

considered valid. Final sample data was considered to be the mean values of the three sets of data for

each sample.

12

3.3. DTF

3.3.1. Apparatus and testing conditions

Figure 3.4 shows a photograph of the DTF, where the main components are highlighted. The main

components of the installation are the biomass feeding system, the furnace, the flow meters and the

probe system.

Figure 3.4 - DTF with numbered components: (1) Biomass feeding system; (2) Furnace; (3) Flow meters; (4)

Probe and probe support.

Figure 3.5 shows the schematics of the DTF. Again, the main components are highlighted.

13

The DTF combustion chamber is a cylindrical ceramic with an inner diameter of 38 mm and a total

length of 1.3 m. It is heated by electrical resistances with a total power of 5600 W and it is

continuously monitored by eight points of temperature reading (type-K thermocouples) along the DTF.

Figure 3.5 - Schematics of the DTF with numbered components: (1) Biomass feeding system; (2) Furnace; (3)

Probe system for sample collection [24, 31].

The biomass reservoir feeds the biomass into the DTF. This is achieved with the aid of two screws

connected to an engine that allows controlling the rotation velocity of it and thus to control the flow rate

of biomass injected into the DTF. A flow of transport air is used to carry the biomass into the water-

14

cooled injector placed on top of the DTF. This injector has a second inlet tube where the secondary air

is admitted.

The flow meters allow monitoring both the transport air flow and the secondary air flow to the DTF.

Also, when collecting particles, a flow meter is used to monitor the nitrogen flow rate to the probe

[25, 31] for quenching purposes.

Tests in the DTF were conducted for AF-I (cf. Table 3.1). The tests were conducted for five DTF

temperatures (900, 950, 1000, 1050 and 1100 ºC). The biomass flow rate was 23 g h-1 and the total air

flow rate was 4 L min-1.

3.3.2. Methods

Temperature measurements

The mean gas temperature along the DTF axis is measured with the aid of a thermocouple probe

connected to a data acquisition system, which includes a dedicated computer that uses a software

capable of reading instantaneous temperatures and calculated averages. The thermocouple used is a

type-R with a 76 µm diameter wire platinum and platinum/13% rhodium.

Equation 3.1 expresses an energy balance to the thermocouple bead under steady-state conditions

[24].

�� 0��9:;� <!�9:;� − !> ?@ + / 4 <!�9:;�B − !� ;;B @ = 0 (3.1)

where �� is the Nusselt number, 0 is the conductivity, ��9:;� is the thermocouple bead

diameter, !�9:;� is the temperature of the thermocouple, !> ? is the temperature of the flow gas, / is

the emissivity, 4 is the Stefan-Boltzmann constant and !� ;; is the temperature of the DTF combustion

chamber walls.

Equation 3.1 allows the calculation of the uncertainty of the temperature measurements as it accounts

for the radiation effects from the walls and convection with the gas [24]. It was estimated that there will

be a maximum uncertainty of ± 10% in the measured gas temperature reported for all five DTF

temperatures examined in this study.

15

Burnout measurements

Particle collection for burnout calculations along the DTF was performed isokinetically with the aid of a

water-cooled nitrogen-quenched probe (made of stainless steel with a length of 1.5 m and inner

diameter of 3 mm). Measurements were made along the DTF axis for all DTF temperatures. The

probe is connected to a device with a quartz filter, which is linked to a pump. The nitrogen flow rate

through the probe for quenching purposes was 4.7 L min-1. All procedures follow the European

standards (CEN/TS 14775) [32] and are described with more detail elsewhere [25].

The particle burnout, designated by 2, calculated with the aid of an ash-tracer method, is calculated

as follows:

2 = 1 − 5�561 − 5� (3.2)

where 5� is the ash weight fraction in the input biomass and 56 is the ash weight fraction in the

collected sample.

Uncertainties in particle burnout when using ash as a tracer can be due to ash volatility at high heating

rates and temperatures and ash solubility in water. These uncertainties were found to be negligible for

the conditions used in the present study [31].

16

4. Kinetic Modelling

Smith [33] divided the combustion process of solid fuels into two main stages: (i). devolatilization of

the majority of the biomass volatile matter and production of char; and (ii). homogeneous oxidation of

volatile matter and heterogeneous char oxidation originated during devolatilization. At this point, most

of the volatiles have been burned and there is mainly carbon and inorganic compounds that compose

the ash.

The estimation of the kinetic data is usually performed for each stage, i.e., devolatilization and char

oxidation stages. For each approach, this division can be explained by different reasons. In the TGA,

the stages are more easily identifiable and this division allows to fully understand the differences in

terms of kinetic data from one stage to the other, while maintaining the designated formulation. In the

DTF, the formulation differs and each stage has to be treated individually.

The reaction rate constant, common to the kinetic calculations of either experimental approach, is

described by �:

� = �� FG HIJK (4.1)

where �� is the pre-exponential factor, � is the activation energy, � is the universal gas constant and

! is the temperature. Equation 4.1 is more commonly known as the Arrhenius equation.

This chapter describes the adopted formulation for the kinetic calculations for both experimental

approaches used in this study. For the TGA, kinetic calculations were performed using the CR

method. For the DTF, kinetic calculations were performed with a model-fitting method and a numerical

model. Each section of this chapter, presents the equations, assumptions, input data and calculation

tools. The last section presents the equations for calculating the global kinetic parameters.

4.1. TGA

The TGA formulation adopted in this thesis follows [11], in which a detailed presentation can be found.

The conversion factor or the fraction of biomass that has reacted, is expressed by ,:

, = �L − ��L − �M (4.2)

where �L, �, and �M represent the initial, instantaneous and final stage mass.

17

The reaction rate is generally described by Equations 4.3 and 4.4. In Equation 4.3, the differential

reaction model is given by N(,) and, in Equation 4.4, the integral reaction model is given by �(,):

�,�" = � N(,) (4.3)

�(,) = � " (4.4)

where " is the temporal quantity.

This study considers a first-order reaction model since it is the most common method for describing

the biomass thermal decomposition, as follows [10, 15]:

N(,) = (1 − ,)Q (4.5)

where � is the reaction order.

Substituting Equation 4.1 into Equations 4.3 and 4.4 gives:

�,�" = �� FGHIJK N(,) (4.6)

�(,) = �� FGHIJK " (4.7)

Equation 4.8 can be adopted for non-isothermal experiments, where the non-isothermal reaction rate,

�,/�!, is expressed by:

�,�! = �,�" �"�! (4.8)

where �,/�" is the isothermal reaction rate and �!/�" is the heating rate, also designated by -.

Substituting Equation 4.6 into Equation 4.8 gives:

�,�! = ��- FGHIJK N(,) (4.9)

that expresses the differential form of the non-isothermal reaction rate. Integrating this equation gives:

18

�(,) = ��- S FGHIJKKL �! (4.10)

which expresses the integral form of the non-isothermal reaction rate, also known as the temperature

integral.

Equation 4.10 has to be reformulated into a more general form. To do so, the integration variable has

to be redefined. The new variable can be written as:

& = � �! (4.11)

Substituting Equation 4.11 into Equation 4.10 and rearranging becomes:

�(,) = ��� -! S FG6&T �&M

6 (4.12)

Assuming,

U(&) = S FG6&T �&M

6 (4.13)

The final form of the temperature integral becomes:

�(,) = ��� -� U(&) (4.14)

The Coats-Redfern method takes Equation 4.14 and uses asymptotic series expansion for

approximating U(&) in order to obtain the following expression:

ln �(,) = ln ���-� X1 − 2�!� Z − � �! (4.15)

Because the term TJKHI ≪ 1, it can be neglected, and the final equation can be written as:

ln �(,) = − � �! + ln ���-� (4.16)

where g(α) is given by:

�(,) = −^� (1 − ,)!T , � = 1 (4.17)

19

The plot of ln �(,) vs 1/! generally provides a straight line with a high correlation factor. The apparent

activation energy can be determined from the slope of the line and the pre-exponential factor �� by the

intercept term [34].

In this study, experimental input data include temperature and mass and the calculations were

performed with Matlab, where Equations 4.2, 4.16 and 4.17 were implemented.

4.2. DTF

The equations shared by the two methods used are first presented. The particle weight loss along the

DTF depends on the burnout and on the initial weight. In this sense, in each point, the particle weight

can be calculated in each point by [33]:

� = �L (1 − 2) (4.18)

The rate of devolatilized matter, �$/�", is described by Equation 4.19. The model is based upon a first

order reaction [23, 25]:

�$�" = −�� $ (4.19)

where �� is reaction rate constant for the devolatilization and $ is the volatile content. Equation 4.1

can now be rewritten as:

�� = �� FGHI_JK̀ (4.20)

where �� is the pre-exponential factor and � � is the activation energy for the devolatilization and ! is

temperature of the particle.

The char oxidation, ��/�", is described by Equation 4.21. The model is based upon the outer surface

area of the particles [22, 23, 25]:

���" = −� �� (4.21)

where � is the particle surface area and �� is the reaction rate constant for the char oxidation. The

reaction rate constant for the oxidation �� differs in each method.

20

Equation 4.1 can be rewritten as the reaction rate constant for the oxidation, ��:

�� = �� �ab,? FGHIcJK̀ (4.22)

where �� is the pre-exponential factor for the oxidation, � � is the activation energy for the char

oxidation and �ab,? is the oxygen partial pressure at the particle surface.

Coelho and Costa [4] mentioned how heterogeneous oxidation can be divided into different burning

regimes, depending if the reaction is being controlled chemically (kinetics) or physically (diffusion).

Mitchell et al [35] described each burning regime and pointed out that the physical differences that a

particle suffers. Regime I occurs at low temperatures and is mainly controlled by kinetics (reaction

rates) that limit the mass loss rate. This burning regime implies a process occurring at constant

particle size/diameter and that the apparent density is proportional to the mass loss. Regime II takes

place with increasing temperature. The reaction is now controlled by kinetics and diffusion. During this

regime, particle diameter and apparent densities vary with mass loss. Regime III occurs at higher

temperatures and the reaction rate is mainly controlled by diffusion. The apparent density remains

constant and its diameter varies according to a one-third power law. [4, 8, 35].

Under regime II, the particles’ diameter and density evolution along the combustion process can be

expressed as [33, 35]:

� = �L(1 − 2)d (4.23)

3 = 3L (1 − 2)e (4.24)

where � is the particle diameter, �L is the initial particle diameter, 3 is the particle density and 3L is

the initial particle diameter. For spherical particles, 3, + - = 1. When particles burn under regime I,

the diameter is constant (, = 0) and the density varies proportionally with the mass loss. For particles

burning under regime III the density remains approximately constant (- = 0) and , reaches its

maximum value of 0.33, representing the highest diameter variation. Regime II takes into account the

variation of density and diameter.

21

4.2.1. Model-fitting method

Methodology

This method requires the following simplifications:

• Partial oxygen pressure at particle surface (�ab,g) is considered to be constant and equal to the

partial oxygen pressure of the gas stream throughout the combustion process.

• Particle temperature is assumed to be equal to the gas temperature.

• A single representative diameter (mean diameter) for all particles, constant for the

devolatilization process and varied for the oxidation process.

Calculations with this method fit data with a linear regression for each considered section of the DTF,

as shown in Figure 3.5. The devolatilization process is assumed to fully occur in the upper section,

within section 200 < x < 700 mm, which is further discretized in three different sections:

200 < x < 300, 300 < x < 500, and 500 < x < 700 mm. The devolatilization rate, �$/�", described by

Equation 4.19, can be rewritten considering the experimental burnout as the devolatized matter [25]:

�$�" = −�� (1 − 2) (4.25)

Substituting Equation 4.25 into Equation 4.20 gives [25]:

ln �$�"1 − 2 = − � ��! + ln �� (4.26)

The oxidation process occurs in the rest of the DTF, i.e., 700 < x < 1100 mm of the DTF (cf. Figure

3.5). Substituting equation 4.21 into equation 4.22 gives [24]:

ln ���"−��ab,g= − � ��! + ln �� (4.27)

The kinetic data (� �, � �, ��, ��) can be obtained from Arrhenius plots. In Equation 4.23, parameter ,

can be seen as an apparent diameter variation and is considered to be a fitting parameter.

22

Input data

Input experimental data for the model-fitting method include the measured profiles of burnout, 2, the

temperature along the DTF axis for the conditions presented in Chapter 3 (section 3.3), and the

biomass mean diameter (SMD) obtained from the measured particle-size distributions [36].

Table 4.1 shows the experimental conditions inputs, and Table 4.2 shows the initial fuel data assumed

for AF8-I.

Table 4.1 – Input experimental conditions for the model-fitting method.

Parameter Value

Partial pressure of oxygen (Pa) [37] 2.13 x 104

Tube section (m2) 1.13 x 10-3

Total air flow rate (L min-1) 4

Table 4.2 – Input AF8-I data for the model-fitting method.

Parameter Value

Density (kg m-3) [38] 450

SMD (m) 5.62 x 10-4

Calculations for this method were performed with Excel, following Equation 4.18 through 4.27.

4.2.2. Particle-size model

Methodology

Ballester et al [23] proposed a numerical model that takes into account the combustion history of the

particle. Simplifications assumed in section 4.2.1 no longer apply, and the single film model is

assumed in order to calculate the new variables. Further details can be found elsewhere [22, 23].

The DTF’s kinetics study is done by sections. The devolatilization process is assumed to fully occur in

the upper section of the DTF, i.e., 200 < x < 700 mm of the DTF (cf. Figure 3.5), and the oxidation

process throughout the DTF, i.e., 200 < x < 1100 mm (cf. Figure 3.5).

23

Equations 4.19 and 4.20 describe the devolatilization process for this model. Equation 4.21 accounts

now for the number of biomass particles:

�i � = ���" = −� � �� (4.28)

where �i � is the carbon mass flow and � is the number of biomass particles kg-1 of biomass. Because

particle combustion has two resistance components, diffusion (�����) and kinetics (��) at the surface,

the equivalent electric analogous gives:

���" = −� � 11����� + 1�� �ab,> (4.29)

where ����� is given by:

����� = �ℎ %�� ab )ab� !> (4.30)

and �ℎ is the Sherwood number (�ℎ = 2), %� is the stoichiometric ratio of heterogeneous oxidation, ab

is the mass diffusivity of the oxygen, )ab is the molecular mass of oxygen and !> is the gas stream

temperature.

Carbon at the surface reacts with oxygen producing carbon dioxide [4]:

C + OT → COT (4.31)

The mass balance at the surface of the particle is based on [4]:

���" = �i (ab − �i ab (4.32)

where �i (ab is the carbon dioxide mass flow and �i ab is the oxygen mass flow. Since oxygen is

consumed in the carbon oxidation under stoichiometric proportions, the follow relationship applies:

�i ab = − 1%����" (4.33)

The carbon particle is a porous medium and oxygen diffuses into the particle according to Fick’s law:

�i ab = � m � �ℎ 3> ab(+ab,> − +ab,?) (4.34)

24

where � is the particle diameter, 3> is the gas stream density, and +ab is the mass fraction of the

oxygen for the gas stream or at the surface. Partial oxygen pressure varies throughout the DTF;

substituting the perfect gas equation into Equation 4.34 gives Equation 4.35.

� �ℎ � Lb)9b�!> <�ab,> − �ab,?@ = − 1%�

���" (4.35)

The equation above allows calculating the partial oxygen pressure at the particle surface. Because

oxygen in the gas stream is consumed throughout the length of the DTF due to combustion, in a non-

constant manner, Equation 4.36 describes the partial oxygen evolution with time.

)ab3>�!> � ��ab,>�" = − 1%� �$�" − 1%�

���" (4.36)

where � is the mass flow rate injected into the DTF and %� is the stoichiometric proportion of the

volatile combustion.

Particle temperature differs from gas stream temperature. Equation 4.37 translates the energy balance

equation at the particle surface. Different heat transfer mechanisms are admitted at the particle

surface, such as conduction (�i�9Q� = 0), radiation (�in �) and convection (�i�9Q�) towards the particle

and also heat from devolatilization (��i ) and heterogeneous combustion (�i�):

3 $ � �!�" = �in � + �i�9Q� − �i� + �i� (4.37)

�in � = m �T / 4 (!� ;;B − !B) (4.38)

�i�9Q� = m � �� 0> (!> − !) (4.39)

�i� = 1� �$�" �� (4.40)

25

�i� = 1� ���" �� (4.41)

where $ is the particle volume, � is the specific heat of the biomass, 0> is the thermal conductivity, ��

and �� are the heat of devolatilization and combustion.

Particle-size distribution is taken in account instead of a mean diameter, by grouping different sizes

into classes. Parameters such as proximate analysis, diameters, particle temperature, number of

particles and partial oxygen pressure at the particle surface depend on the size class. The unburnt

fraction, #, considering different classes is:

# = 1 − 2 (4.42)

# = o 5� #�� (4.43)

where 5� is the mass fraction of class 7 and,for this model, # is given as:

# = �$�" + ���" (4.44)

Since calculations are performed considering specific lengths to the point of injection, length,

residence time and velocity variation due to particle class need to be related as follows:

3,� $,� �%,��" = <3,� − 3>@ $,� � − 3 m 1 �,� (%,� − %>) (4.45)

where % is the particle velocity, � is the gravity acceleration, 1 is the viscosity and %> is the gas

stream velocity.

The calculated unburnt fraction is compared with the measured unburnt fraction. The global error

between the experimental and modelled value, ., is calculated by:

. ,p = (#(^)�6 − #(^)q9�) ,p (4.46)

where � and � represent the DTF point and the DTF temperature, respectively. The error for each pair

is estimated as the root mean square of the deviations (Equation 4.47). The pair with the lowest error

is selected as the best representing the kinetic parameters of the biomass fuel tested.

26

. = r 1�q 6�q 6 o o . ,pTst

(4.47)

Input data

Table 4.3 shows the input experimental conditions for the numerical model. Partial pressure of the

oxygen is no longer an input, and additional experimental conditions are included.

Table 4.3 - Input experimental conditions for the numerical model.

Parameter Value

Injection section (m2) 2.55 x 10-4

Tube section (m2) 1.13 x 10-3

Total air flow rate (L min-1) 4

Total work pressure (Pa) 1.01 x 105

Work temperature (ºC) 25

Mass flow rate (g h-1) 23

Table 4.4 shows the input AF8-I data for the numerical model. The mean diameter (SMD) is no longer

an input, and additional fuel data is included. Apart from the data shown in Table 4.4, proximate and

ultimate analysis and particle size distribution (cf. Table 3.1) of AF8-I are also inputs.

Table 4.4 - Input AF8-I data for the numerical model.

Parameter Value

, 0

Density (kg m-3) 450

Specific heat (kJ kg-1 K-1) [39] 2.3

Particle emissivity 1

Table 4.5 shows the input gas properties [40] for the numerical model, which were evaluated for film

temperature [22, 23]. In the model-fitting method, there was no need of assuming any property for the

gas stream, besides the oxygen partial pressure.

27

Table 4.5 - Input gas properties for the numerical model.

Parameter Value

Film temperature (ºC) 1000

Thermal diffusivity (m2 s-1) 2.46 x 10-4

Viscosity (Pa s) 4.82 x 10-5

Table 4.6 shows the input heat data for the devolatilization and oxidation [22]. Since particle

temperature is now calculated instead of assuming the measured temperature profile, the heat of

devolatilization and heterogeneous oxidation has to be included.

Table 4.6 - Input heat data for the numerical model.

Parameter Value

Devolatilization heat (J kg-1) 2.07 x 106

Oxidation heat (J kg-1) 9.78 x 106

The kinetic parameters are the inputs and the unknowns to be determined. A pair of activation

energy/pre-exponential factor values is established in order to predict the unburnt fraction for each test

temperature and measured point. When calculating the pair of activation energy/pre-exponential factor

for the devolatilization, the terms related to char oxidation process are set do zero.

The numerical calculations were performed with Mathematica, using Equations 4.19, 4.20 and 4.28

through 4.47.

4.3. Weighted average activation energy

In both model-fitting methods, a stage division of the DTF allow to fit data with high correlation factors.

The objective of this thesis also includes calculating global kinetic parameters, for better

understanding the kinetic data produced by the combustion process as a whole and for comparison

purposes, and so a weighted average process is used to calculate the apparent activation energy.

For the TGA, the weighted average activation energy equation described in [19] is used to calculate

the activation energy of the global combustion process:

� ,� = &�T&�T + &'(u � T + &'(u&�T + &'(u � u (4.48)

28

where &�T is the total volatile matter content, &'(u is the total fixed carbon content, � T is the apparent

activation energy for the second stage and � u is the apparent activation energy for the third stage.

For the DTF, a modified equation based on the previous idea is proposed. The weighted average

activation energy for the global devolatilization process is defined as � >� :

� >� = o &�Q&� � �Q (4.49)

where &�Q represents the fraction of volatile matter in the nth section of the DTF, &� the total volatile

matter and � �Q the apparent activation energy in the nth section of the DTF.

29

5. Results and Discussion

This chapter presents and discusses the results obtained for the TGA and the DTF. The first part of

the chapter is focused on the experimental results. The TGA experiments were performed for the

three biomass samples, whereas the DTF tests were only performed for the AF8-I biomass sample.

The second part of the chapter concentrates on the kinetic data calculated using the models described

in Chapter 4. The third and final part presents comparisons between the kinetic data obtained by

means of the TGA and DTF tests.

5.1. Experimental analysis

5.1.1. TGA

Figure 5.1 presents the TG curves relating weight loss (%) and temperature (ºC) and Figure 5.2

presents the DTG curves, relating the weight loss rate (% min-1) and temperature (ºC) for the three

poplar biomass samples.

The overall trend is similar for the three biomass samples. In case of the DTG curves, the evolution

shows two maxima weight loss rates (Wmax); the first maximum occurs during the second stage and

the second maximum occurs during the third stage. Major differences in the results are found for the

AF2 sample for temperatures above 400 ºC. In fact, AF2 reaches the second peak at lower

temperature and with lower weight loss rate. Note that AF2 has a higher percentage of residual ash

than the other samples.

Figure 5.1 - TG curves. Figure 5.2 - DTG curves.

The visual examination of both figures allows identifying three decomposition stages: (i) the heating

and drying of the particle (SI); (ii) the devolatilization (SII); and (iii) the char oxidation (SIII). These three

stages are considered in the analysis of the combustion process.

Temperature ( oC)

0 100 200 300 400 500 600 700 8000

10

20

30

40

50

60

70

80

90

100

Weig

ht lo

ss (

%)

AF8-P

AF8-I

AF2

0

5

10

15

20

25

DT

G (

%/m

in)

0 100 200 300 400 500 600 700 800Temperature ( oC)

AF8-P

AF8-I

AF2

30

Table 5.1 shows the combustion characteristics of each biomass obtained from the TG and DTG

curves, namely the initial temperature (Tin), the burnout temperature (Tb), the maximum weight loss

rate (W i, max) and the temperatures where this rate occurred (Ti, max).

Table 5.1 - Combustion characteristics of each biomass fuel.

Stage Parameter AF8-I AF8-P AF2

SI T1, in (ºC) 38 39 31

SII

T2, in (ºC) 230 238 233

W2, max (% min-1) 12.6 13.9 10.8

T2, max (ºC) 321 322 320

SIII

T3, in (ºC) 372 373 362

W3, max (% min-1) 21.3 22.8 17.4

T3, max (ºC) 423 429 404

Tb (ºC) 433 438 437

The first stage (SI) begins at temperatures between 31 ºC and 39 ºC (T1, in). The total weight losses of

AF8-P and AF8-I are similar (approximately 6.2% and 6.9%), but AF2 has a higher weight loss

(approximately 9.3%).

The second stage (SII) starts at temperatures between 230 ºC and 238 ºC (T2, in). The maximum

weight loss rate (W2, max) for each biomass takes place around a temperature of 320 ºC (T2, max),

confirmed by the maximum in Figure 5.2. AF8-P presents the highest weight loss rate (W2, max) with

13.9 % min-1. AF2 and AF8-I present 10.8 % min-1 and 12.6 % min-1, respectively. The percentages of

total weight loss for AF8-P and AF8-I are similar (approximately 69%) and higher than that of the AF2

(approximately 63.5%).

The third stage (SIII) is defined as the point where weight loss rate is minimum (Figure 5.2), after the

maximum of the second stage [6]. The corresponding temperatures range from 362 ºC to 373 ºC

depending on the biomass. The maximum weight loss rate (W3, max) is observed at temperatures

ranging from 404 ºC to 429 ºC (T3, max), in good agreement with the maximum observed in Figure 5.2.

The AF8-P sample has the highest weight loss rate of the whole combustion process during this

stage, (cf. Table 5.1).

The burnout indicates the end of stage three. The burnout temperatures (Tb) are between 433 ºC and

438 ºC. The AF8-P has the highest burnout temperature and AF8-I the lowest. The total weight loss

during this stage is lower for the AF2 sample (approximately 21.4%) when compared with the clones

31

AF8 (the total weight loss for the AF8-P is approximately 23.1% and for the AF8-I is approximately

22.4%). The final solid residue (ash) at the end of the third stage is similar for the two clones AF8

(approximately 1.7%) and higher for the AF2 (approximately 5.8%).

The degradation overlap of the structural components creates difficulties in evaluating the influence of

each component alone. In Figure 5.2, hemicellulose is identified by the curve’s shoulder and cellulose

is identified by the curve’s maximum. Lignin slowly decomposes over a wider range of

temperatures [6].

Table 5.2 shows the combustion characteristics of the second stage for woody energy crops samples,

with similar chemical analysis, at 10 ºC min-1 heating rates obtained from literature [9, 12]. Available

parameters such as fuel, temperature interval (Ti, int), maximum weight loss rate (W i, max) and

temperatures where this rate occurred (Ti, max) are indicated for comparative purposes

Table 5.2 - Combustion characteristics of biomass fuels from the literature (second stage).

SII

Reference Fuel T 2, int (ºC) T2, max (ºC) W2, max (% min -1)

[9] Poplar 220 - 400 360 10

Willow 220 - 400 370 10

[12] Poplar 248 - 376 350 -

The initial decomposition temperatures in Table 5.1 are in the range of the initial decomposition

temperatures in Table 5.2 (220 - 248 ºC). The stage ending temperatures in [9] are higher (up to 38 ºC

with a maximum relative error of approximately 10%) than those obtained in the present experiments.

The stage ending temperature in [12] is similar to the stage ending temperature in Table 5.1 (T3, in).

The maximum decomposition temperatures in Table 5.2 (T2, max) are higher (up to 50 ºC) when

compared to the same parameter in Table 5.1, leading to a maximum relative error of approximately

16% when compared to those obtained in the experiments.

The maximum decomposition rates in Table 5.1 (W2, max) are higher than the maximum decomposition

rates in Table 5.2 (up to 3.9 % min-1), leading to a maximum relative error of approximately 28% when

compared to those obtained in the experiments.

Table 5.3 shows the combustion characteristics of the third stage for woody energy crops samples,

with similar chemical analysis, at 10 ºC min-1 heating rates obtained from the literature [9, 12].

32

Table 5.3 - Combustion characteristics of biomass fuels from the literature (third stage)

SIII

Reference Fuel T 3, int (ºC) T3, max (ºC) W3, max (% min -1)

[9] Poplar 340 - 550 457 6.3

Willow 340 - 520 488 6.7

[12] Poplar 391 - 505 450 -

The stage beginning temperatures in Table 5.1 are in the range of the initial decomposition

temperatures in Table 5.3 (340 - 391 ºC). The burnout temperatures in Table 5.3 are higher than the

burnout temperatures in Table 5.1 (up to 117 ºC and maximum relative error of approximately 27%

when comparing the values in Tables 5.1 and 5.3).

The maximum decomposition temperatures in Table 5.3 are higher than those reported in Table 5.1,

with a maximum difference of 84 ºC, leading to a maximum relative error of approximately 17%.

The maximum decomposition rate values in Table 5.1 (W3, max) are higher than the maximum

decomposition rates in Table 5.3 (up to 16.5 % min-1), leading to a maximum relative error of

approximately 72%.

The maximum relative errors reveal more accurate results for the second stage. Differences can be

attributed to a number of factors. In references [9] and [12], initial sample weights are higher than in

this study. Smaller samples allow faster heating rates and shorter analysis times, while larger samples

can lead to combustion at higher temperatures. This may explain the overall differences in the

temperatures. Non-standard methodologies can also alter the analysis. In fact, most studies do not

explicitly indicate how the stage defining temperatures were chosen. As a result of this, the stage

defining temperatures may eventually be different.

According to [8], TG and DTG are empirical tests, with results strongly depending on testing conditions

(heat transfer, heating rate, particle size, sample mass and oxygen concentration) and also on

apparatus and sample characteristics. Solid fuels are not homogeneous and different parameters can

affect the results [12, 13], thus complicating literature comparisons. In order to be comparable, tests

must be conducted under identical operating conditions and in the same testing apparatus [8].

33

5.1.2. DTF

Figure 5.3 presents the experimental temperature and burnout axial profiles obtained for the clone

AF8-I for the various DTF temperatures examined, and Table 5.4 shows the burnout values for the

same conditions.

In regard to the temperature axial profiles it is seen in Figure 5.3 that the trends are rather similar. The

temperature maximum is found around x = 500 mm and, beyond this axial position, the temperature

drops reaching the lowest values at the end of the DTF. The drop in the temperature is due to the

environmental temperature.

Figure 5.3 also reveals that the burnout axial evolution is similar for the five DTF wall temperatures

studied. A closer examination of the data (cf. Table 5.4) reveals, however, that the initial combustion

intensity augments as the DTF wall temperature increases.

Table 5.4 - Burnout obtained for each DTF temperature along the reactor for the AF8-I.

Distance (mm)/burnout (%)

Temp. (ºC) 200 300 500 700 900 1100

900 48.22 56.22 70.13 81.25 89.57 95.09

950 50.86 59.35 73.65 84.41 91.62 95.30

1000 54.30 62.83 76.94 87.13 93.40 95.76

1050 59.44 67.66 80.98 90.11 95.05 95.82

1100 60.71 68.91 82.10 91.02 95.68 96.05

The ash tracer method can provide inaccurate results when calculating burnouts. The chemical

analysis of the clone AF8- I (see Table 3.1) indicates low ash content, which may cause scattering of

the results and, consequently, poor reproducibility [8]. The use of a water-cooled probe can also

modify the initial conditions and, consequently, the calculated burnouts. Studies available in the

literature indicate that probes can decrease local gas temperature up to 100 ºC [8]. In the present

study, the estimation of the experimental uncertainties led to maximum values of 5%.

34

Figure 5.3 - Temperature and burnout axial profiles for each DTF temperature.

Tem

pera

ture

(oC

)T

empe

ratu

re (

oC

)T

empe

ratu

re (

oC

)T

empe

ratu

re (

oC

)T

empe

ratu

re (

oC

)

Bur

nout

(%

)B

urno

ut(%

)B

urno

ut(%

)B

urno

ut (

%)

Bur

nout

(%

)40

50

60

70

80

90

100

40

50

60

70

80

90

100

40

50

60

70

80

90

100

40

50

60

70

80

90

100

40

50

60

70

80

90

100

600

700

800

900

1000

1100

1200

600

700

800

900

1000

1100

1200

600

700

800

900

1000

1100

1200

600

700

800

900

1000

1100

1200

600

700

800

900

1000

1100

1200

0 200 400 600 800 1000 1200

Axial distance (mm)

Axial distance (mm)

Air

Biomass

Burnout

Tw = 900 ºC

Tw = 950 ºC

Tw = 1000 ºC

Tw = 1050 ºC

Tw = 1100 ºC

35

5.2. Kinetic analysis

5.2.1. TGA

Figures 5.4, 5.5 and 5.6 show the kinetic plots for the three biomass fuels, calculated by means of the

CR method (section 4.1), and the corresponding linear regressions of the second and third stages for

the three samples. The trends for the devolatilization process are similar for the three samples.

Differences are mainly found for the char oxidation process, where the trend associated to the clone

AF2 clearly differs from the other two. Both AF8-P and AF8-I have similar kinetic profiles in both

stages.

Figure 5.4 - Kinetic plot for the AF8-P. Figure 5.5 - Kinetic plot for the AF8-I.

Figure 5.6 - Kinetic plot for the AF2.

Table 5.5 shows the kinetic parameters calculated with the methods described in sections 4.1 and 4.3,

and the correlation coefficients (R2) associated to the linear fitting.

0.0014 0.0015 0.0016 0.0017 0.0018 0.0019 0.002

1/T

-22

-20

-18

-16

-14

-12

-10

ln g

(α)

y = -30307x + 30.381

R² = 0.9048

y = -14122x + 10.907

R² = 0.9333

Devolatilization

Char oxidation-22

-20

-18

-16

-14

-12

-10

0.0014 0.0015 0.0016 0.0017 0.0018 0.0019 0.002 0.00211/T

ln g

(α)

Devolatilization

Char oxidation

y = -3200x + 9.4815

R² = 0.9394

y = -32168x + 33.358

R² = 0.9042

-22

-20

-18

-16

-14

-12

-10

0.0014 0.0015 0.0016 0.0017 0.0018 0.0019 0.002 0.0021

1/T

ln g

(α)

Devolatilization

Char oxidation

y = -14260x + 11.424

R² = 0.9363

y = -31788x + 33.979

R² = 0.9385

36

Table 5.5 - Kinetic parameters (TGA).

Stage Parameter AF8-I AF8-P AF2

SII

T2, int (ºC) 230 - 374 238 - 375 233 - 363

� (kJ mol-1) 109.74 117.41 118.55

�� (s-1) 1.03 x 1011 4.62 x 1011 7.83 x 1011

R2 0.94 0.93 0.93

SIII

T3, int (ºC) 374 - 433 375 - 438 363 - 437

� (kJ mol-1) 267.44 251.97 264.28

�� (s-1) 5.92 x 1021 2.84 x 1020 1.09 x 1022

R2 0.90 0.90 0.94

Global � ,� (kJ mol-1) 147.99 150.05 155.36

As shown in Table 5.5, the apparent activation energy in the second stage varies between

109.74 kJ mol-1 and 118.55 kJ mol-1. Both AF8-P and AF2 have similar apparent activation energies,

with values of 117.41 kJ mol-1 and 118.55 kJ mol-1. The apparent activation energy of the AF8-I is the

lowest, with a value of 109.74 kJ mol-1. The apparent pre-exponential factor and the correlation

coefficient for the linear approximations are similar for the three samples.

As seen in Table 5.5, the apparent activation energy in the third stage varies between 251.97 kJ mol-1

and 267.44 kJ mol-1. Both AF2 and AF8-I have similar apparent activation energies, with values of

264.28 kJ mol-1 and 267.28 kJ mol-1. The apparent activation energy of the AF8-P is the lowest, with a

value of 251.97 kJ mol-1. The apparent pre-exponential factor presents different orders of magnitude

for each sample, the highest being that for the AF2. The highest correlation coefficient was obtained

for the AF2.

Table 5.5 reveals that the weighted average activation energy (Global) ranges from 147.99 kJ mol-1

(AF8-I) to 155.36 kJ mol-1 (AF2).

Table 5.6 shows the calculated kinetic parameters of poplar wood under equivalent conditions

available in the literature [12]. Model-free methods were used in these calculations so that only

average values are presented.

37

Table 5.6 - Poplar kinetic parameters reported in the literature (TGA).

Reference Method Heating rate (ºC min -1) vw (kJ mol -1)

[12]

Kissinger

Various

129.20

FWO 141.25

ASTM I 138.10

ASTM II 131.90

The apparent activation energies obtained by [12] are comparable to the weighted average activation

energies obtained in the present study (cf. Table 5.5). The maximum relative error is approximately

equal to 16% and the minimum relative error is approximately equal to 0.4%. Considering that the

values given in [12] were determined with different methods, the relative errors validate the results

included in Table 5.5. It is worth noting that an apparent reaction order of one was assumed in the

present calculations (section 4.1). According to Di Blasi [41], this assumption can influence the

apparent activation energy, yielding higher values. As a result of this, several authors consider to be a

more reliable procedure to compare results with the same kinetic method [12, 15, 18].

When considering literature with calculations performed with the CR method, different apparent

activation energies for the second stage are reported, with values varying from 52 kJ mol-1 [15] to

140 kJ mol-1 [19]. For the third stage, high values for the apparent activation energy with the same

order of magnitude to those included in Table 5.5 can be found in the literature when applying the

present method [18, 34, 42].

The apparent pre-exponential factor values available in the literature show some scattering, varying

from 7.2x106 s-1 [42] to 1.5x1014 s-1 [19] for the second stage. For the third stage, the apparent pre-

exponential factors reach values as high as 5.7x1023 s-1 [18]. This means that the apparent pre-

exponential factor is expected to increase as the temperature increases.

Scattering of the results when using the present method have been reported elsewhere [14]. However,

the results in Table 5.5 are in line with the literature and demonstrate how strongly dependent these

data are on the experimental conditions, apparatus and sample characteristics. This problem had

been previously mentioned in section 5.1.1, even in situations when the same method was employed.

Experimental errors can also contribute to the scattering of the apparent kinetic data. In order to be

comparable, tests should be conducted in the same apparatus under similar testing conditions.

38

5.2.2. DTF

5.2.2.1. Model-fitting method

Figure 5.7 presents the linear regressions calculated with Equations 4.26 and 4.27 (section 4.2.1) for

the different sections of the DTF. As previously mentioned, devolatilization occurs throughout the

section 200 < x < 700 and oxidation occurs throughout the section 700 < x < 1100 mm of the DTF.

Figure 5.7 - Model-fitting plots for the different sections of the DTF for the AF8-I.

-17.3

-17.0

-16.7

-16.4

-16.1

-15.8

-18.1

-18.0

-17.9

-17.8

-17.7

-17.6

-17.5

-17.4

-17.9

-17.8

-17.7

-17.6

-17.5

-17.4

-17.3

-17.2

-19.1

-19.0

-18.9

-18.8

-18.7

-18.6

-18.5

-18.4

0.00070 0.00074 0.00078 0.00082 0.00086 0.00090 0.00094

y = -3303.3x - 16.089

R² = 0.9586

y = -4157.5x - 14.466

R² = 0.9831

y = -4687.9x - 13.821

R² = 0.9948

y = -8872.5x - 9.1546

R² = 0.9938

1100

700

500

300

200

ln((

dm

/dt)

/(-A

PO

2))

0

ln((

dm/d

t)/-

(1-ψ

))ln

((dm

/dt)

/-(1

-ψ

))ln

((dm

/dt)

/-(1

-ψ

))

39

Table 5.7 shows the estimated values of the apparent activation energy, the apparent pre-exponential

factor and the correlation coefficient obtained from linear regressions. The high correlation coefficients

indicate a good agreement between calculated kinetic parameters and experimental data.

Table 5.7 - Kinetic parameters for the model-fitting method for the AF8-I (DTF).

DTF section (mm) vw (kJ mol -1) x (s-1) R2

200 - 300 27.46 9.72 x 10-8 0.96

300 - 500 34.57 4.92 x 10-7 0.98

500 - 700 38.98 9.39 x 10-7 0.99

700 - 1100 73.76 9.98 x 10-5 0.99

Throughout the devolatilization zone (200 < x < 700 mm), the apparent activation energy increases

from 27.46 kJ mol-1 (in the first section) to 38.98 kJ mol-1 (in the third section). On the opposite side,

the pre-exponential factor decreases three orders of magnitude. The correlation coefficient is higher

for the second and third sections.

The oxidation zone (700 < x < 1100 mm) has the highest kinetic parameters values. The apparent

activation energy is higher since most of the volatile matter has been released, producing char, and

showing that it is necessary more energy and frequency of collisions for a reaction to occur.

The DTF sections with higher temperatures (Figure 5.3) have lower apparent activation energies. In

fact, when burning biomass the temperature decreases and the apparent activation energy increases

throughout the DTF, leading to the conclusion that the DTF temperature influences the apparent

activation energy.

Table 5.8 presents the overall kinetic parameters. The apparent activation energy for the

devolatilization process was calculated with Equation 4.49 (section 4.3) for the zone 200 < x < 700

mm.

Table 5.8 - Stage kinetic parameters for the model-fitting method for the AF8-I (DTF).

Process vw (kJ mol -1) xy (s-1)

Devolatilization 34.11 -

Oxidation 73.76 9.98 x 10-5

The apparent activation energies for coal combustion usually varies from 4.18 kJ mol-1 to

188.10 kJ mol-1 [8]. The same reference, i.e., [8], also provides apparent activation energies for the

40

zones in regime II between 49 kJ mol-1 and 114 kJ mol-1. For pinewood sawdust the apparent

activation energy values range from 24.20 kJ mol-1 to 34.70 kJ mol-1 [26]. The apparent pre-

exponential factors present some scattering [8], but usually present higher values than those reported

in Tables 5.7 and 5.8.

The results included in Table 5.8 are in good agreement with the apparent activation energies found in

the literature.

Table 5.9 shows the variation of α in the oxidation zone for all DTF testing temperatures. Different α

were tested in order to find the best fit of the experimental results. Results confirmed that α increases

with temperature. The highest α (0.28) is reached for a DTF temperature of 1100 ºC. At 900 ºC, α is

zero, meaning that the surface area remains constant. However, α is only seen as a fitting value and,

therefore, it is considered to be apparent.

Table 5.9 - Apparent α for the AF8-I.

Temperature ( °C) z

900 0

950 0.06

1000 0.15

1050 0.25

1100 0.28

Figure 5.8 shows the linear regression of the data in the DTF section 200 < x < 700 mm for the

devolatilization process. Scattering in the kinetic data originates a lower correlation coefficient

(R2 = 0.72).

Figure 5.8 - Model-fitting plot for the DTF section 200 to 700 mm for the AF8-I.

y = -804,1x - 15,943R² = 0,723

-16,7

-16,6

-16,6

-16,6

-16,6

-16,6

-16,5

-16,50,0007 0,0008 0,0008 0,0008 0,0009

ln((

dm/d

t)/-

(1-ψ

))

1/T

41

Table 5.10 shows the apparent kinetic parameters calculated for the DTF section between 200 to

700 mm. Results differ from those included in Table 5.8. This discrepancy proves the incoherence of

model-fitting methods in the DTF, especially when calculating pre-exponential factors. The combustion

process is complex and the heterogeneity of the samples complicates the problem. All the

assumptions taken into account in order to develop the simplified model do not describe the

combustion process accurately, even if the calculated apparent activation energy values are in good

agreement with the literature.

Table 5.10 - Kinetic parameters for the DTF section between 200 to 700 mm for the model-fitting method for the AF8-I (DTF). vw{ (kJ mol -1) x{ (s-1)

6.50 2.78

5.2.2.2. Particle size distribution model

Results

Table 5.11 presents the obtained values for the kinetic parameters. The pre-exponential factor is

considerably higher for the devolatilization process.

Table 5.11 - Predicted kinetic parameters for the AF8-I.

Process vw (kJ mol -1) x (s-1)

Devolatilization 12.80 90

Oxidation 69 3.80 x 10-4

Figure 5.9 shows the global error (.) obtained for 30x30 iterations of � �/�� pairs for the numerical

model (section 4.2.2.2). The darkest grey tone represents the area and boundaries of the minimum

global error, displaying the uncertainty in the estimate of the optimum pair. It is estimated that the � �

uncertainty is approximately equal to ±9% and the �� uncertainty is approximately equal to ±2%. The

maximum global error when comparing the experimental and the predicted burnouts is 0.0162.

Figure 5.10 presents the global error (.) obtained for 10x10 iterations of � �/�� pairs. Again, in

Figure 5.10 the darkest grey tone represents the area and boundaries of the minimum error,

displaying the uncertainty in the estimation of the optimum pair. It is estimated that the � � uncertainty

is approximately equal to ±2% and the �� uncertainty is approximately equal to ±10%. The global error

is 0.0065 when comparing the experimental and the predicted burnouts.

42

Figure 5.9 - Devolatilization error plot for the numerical

model for the AF8-I. Figure 5.10 - Char oxidation error plot for the numerical

model for the AF8-I.

Figure 5.11 shows the experimental and numerical predicted burnouts. The overall trends of the

predicted burnouts are similar and in good agreement with the experimental values. The global errors

are considerably small, confirming this.

43

Figure 5.11 - Experimental and predicted burnout for the AF8-I.

40

50

60

70

80

90

100

Bur

nou

t(%

)

40

50

60

70

80

90

100

Bur

nou

t(%

)

40

50

60

70

80

90

100

Bur

nou

t(%

)

40

50

60

70

80

90

100

Bur

nou

t(%

)

40

50

60

70

80

90

100B

urno

ut(

%)

0 200 400 600 800 1000 1200Axial distance (mm)

Tw = 900 ºC

Tw = 950 ºC

Tw = 1000 ºC

Tw = 1050 ºC

Tw = 1100 ºC

Experimental

Predicted

44

The burnout prediction of the char oxidation process strongly relies on the pair � �/��. Since these

values were calculated for the DTF section 200 < x < 700 mm as an approximation, predicted values

might not be completely reliable. A more accurate method of calculating � �/�� is needed.

Literature previously mentioned in section 5.2.2.1 confirms these results. In reference [8], the apparent

activation energies for coal vary between 4.18 kJ mol-1 and 188.1 kJ mol-1, but in [26] the results

reported for the apparent activation energy associated to the devolatilization process are higher.

In what concerns the apparent kinetic parameters, values are in good agreement with the

literature [22]. The apparent pre-exponential factors are expected to be higher than the apparent pre-

exponential factor determined for the oxidation process [8].

Data scattering due to experimental differences explained in section 5.2.2.1 also apply to this section.

In conclusion, the results obtained with this model are consistent with the literature [8, 22].

Sensitivity analysis

Since the experiments were not carried out under pyrolytic conditions and there is no literature on the

devolatization kinetic parameters in a drop tube furnace for poplar, it is important to perform a

sensitivity analysis.

The results in section 5.2.2.2 consider burning in regime I (α = 0) in order to optimize the results.

Since the analysis in section 5.2.2.1 considers burning in regime II within a certain section of the DTF,

a sensitivity analysis had to be done for α. However, the results obtained are similar so they are not

addressed here.

Two scenarios are considered for the estimation of the kinetic parameters for the oxidation process. In

the first scenario, calculations were performed considering the weighted average kinetic parameters1

estimated for the model-fitting method as inputs: � � = 34.1 kJ mol-1 and �� = 5.7 x 10-7 s-1 (section

5.2.2.1). In the second scenario, calculations were performed considering the devolatilization kinetic

parameters when model fitting is applied to the DTF section 200 < x < 700 mm: � � = 6.5 kJ mol-1

and �� = 2.78 s-1 (section 5.2.2.1).

Figures 5.12 and 5.13 present the global error (.) for 10x10 iterations of � �/�� pairs for the two

above mentioned scenarios. In the first scenario, the uncertainty of the pair is approximately

± 2%/± 20% and, in the second scenario, the uncertainty of the pair is approximately ± 0.8%/± 8%

1 In case of the pre-exponential factor, this was only done for case study purposes. Ideally, a model-fitting method for calculating the pre-exponential factor should have been applied [18].

45

Figure 5.12 - Oxidation error plot for the first scenario

of the sensitivity analysis for the AF8-I. Figure 5.13 - Oxidation error plot for the second scenario of the sensitivity analysis for the AF8-I.

Table 5.12 summarizes the calculated global error (.), apparent activation energy and pre-exponential

factor for both scenarios. The second scenario produces a lower global error and apparent activation

energy but a higher pre-exponential factor than that of the first scenario.

Table 5.12 - Kinetic data obtained for both scenarios.

Scenario | vw} (kJ mol -1) x} (1 s-1)

First 0.0144 82.50 3.70 x 104

Second 0.0132 64 5.50 x 103

Comparing with the values calculated in section 5.2.2.2, the global error for both scenarios is higher

than the global error previously calculated (. = 0.0065). Previously calculated apparent activation

energy (� � = 69 kJ mol-1) is closer to the apparent activation energy value calculated in the first

scenario. The previously calculated pre-exponential factor (�� = 3.6 x 10-4 s-1) is lower than the pre-

exponential factor calculated for both scenarios and included in Table 5.12. For higher � �/�� pairs

(first scenario), a higher � �/�� pair is to be expected while for lower � �/�� pairs (second scenario), a

lower � �/�� pair is to be expected.

Figure 5.14 shows the experimental and numerical predicted burnout for the first scenario and Figure

5.15 shows the experimental and predicted burnout for the second scenario.

46

Figure 5.14 - Experimental and predicted burnout for the AF8-I in the first scenario.

Bur

nou

t(%

)B

urno

ut

(%)

Bur

nou

t(%

)B

urno

ut

(%)

Bur

nou

t(%

)40

50

60

70

80

90

100

40

50

60

70

80

90

100

40

50

60

70

80

90

100

40

50

60

70

80

90

100

40

50

60

70

80

90

100

0 200 400 600 800 1000 1200Axial distance (mm)

Axial distance (mm)Tw = 950 ºC

Tw = 1000 ºC

Tw = 1050 ºC

Tw = 1100 ºC

Tw = 900 ºC

Experimental

Predicted

47

Figure 5.15 - Experimental and predicted burnout for the AF8-I in the second scenario.

The previous figure shows how the devolatilization kinetic data can impact the kinetic and burnout

data for the oxidation process. Comparing Figure 5.11 with Figures 5.14 and 5.15 it is possible to

40

50

60

70

80

90

100

Bur

nou

t(%

)

40

50

60

70

80

90

100B

urno

ut

(%)

40

50

60

70

80

90

100

Bur

nou

t(%

)

40

50

60

70

80

90

100

Bur

nou

t(%

)

40

50

60

70

80

90

100

Bur

nou

t(%

)

0 200 400 600 800 1000 1200Axial distance (mm)

Experimental

Predicted

Tw = 900 ºC

Tw = 950 ºC

Tw = 1000 ºC

Tw = 1050 ºC

Tw = 1100 ºC

48

observe that, in both scenarios, the predicted burnout curves are no longer in such a good agreement,

as they were previously, thereby increasing the global error.

5.2.2.3. Comparison between methods

Table 5.13 shows the apparent kinetic parameters for the devolatilization and char oxidation stages

estimated by the two previously considered methods for the same sections of the DTF (Tables 5.8 and

5.11). The devolatilization and oxidation apparent activation energies are higher when calculated by

the model-fitting method.

Concerning the apparent activation energies, comparison of the data included in Table 5.13 gives a

relative error of 62% for the devolatilization process and a relative error of 7% for the oxidation against

the values obtained with the model-fitting method. The apparent pre-exponential factor calculated by

both methods originates very distinct values.

Table 5.13 - Kinetic data comparison for the AF8-I (DTF).

Method vw{ (kJ mol -1) x{ (s-1) vw} (kJ mol -1) x} (s-1)

Model-fitting 34.11 - 73.77 1.00 x 10-4

Numerical 12.80 90 69 3.80 x 10-4

The results show that the model-fitting method can be used to obtain a fast estimation for the apparent

activation energies. The main drawback of numerical methods is the computation time. However, the

assumptions taken into account in the model-fitting method (section 4.2.1) do not describe the

combustion process accurately, even if calculating satisfactory apparent activation energy values and

in good agreement with the literature. The combustion process is not linear and should not be treated

as so. The assumptions in the particle-size model (section 4.2.2) replicate more accurately the

combustion process for a solid particle. For these reasons, the apparent kinetic data calculated with

the particle-distribution model is considered to be more accurate.

5.2.3. TGA versus DTF

Both experimental techniques reveal the presence of unburnt material. The TGA of AF8-I (section

5.1.1) indicated a final weight of approximately 1.7%, kept constant from a temperature of 430 ºC to

the end of the combustion process while the DTF (section 5.1.2) indicated unburnt samples with a final

weight up to 4% (from the experimental data). This leads to the conclusion that the heating rate and

analysis period influences the release of the volatile and/or inorganic matter.

49

Table 5.14 summarizes the kinetic data calculated for slow and fast combustion using the methods

previously described in Chapter 4. Apparent kinetic parameters obtained under slow and fast

combustion differ. As seen, TGA apparent kinetic data displayed in Table 5.14 is higher than the

calculated values for either DTF kinetic models included in the same table, leading to the conclusion

that the heating rate also influences the kinetic data.

Table 5.14 - Kinetic data comparison under slow and fast combustion for the AF8-I.

Method vw{ (kJ mol -1) x{ (s-1) vw} (kJ mol -1) x} (s-1)

CR 109.74 1.03 x 1011 267.44 5.92 x 1021

Model-fitting 34.11 - 73.77 1.00 x 10-4

Numerical 12.80 90 69 3.80 x 10-4

As previously discussed, the estimated apparent kinetic parameters present some differences with

those available in the literature. Each experimental approach present advantages when compared to

the other. On one hand, the TGA is a versatile, fast and easy way of simulating combustion in regime

I. Also, available kinetic data for the TGA is relatively easy to find because this method is the most

used. On the other hand, the DTF is known to better describe realistic boiler combustion conditions.

This experimental reactor is capable of simulating realistic surrounding combustion atmosphere, while

working at high temperatures and heating rates [27, 43]. Since experimental conditions strongly affect

the results, kinetic data obtained with the DTF are closer to that prevailing in industrial boilers.

Hesitations on the kinetic data calculated with the TGA have been addressed for different reasons [8]:

(i). unrealistic operating temperatures, (ii). slow heating rates, influencing the devolatilization and char

formation, (iii). difficulty in identifying the combustion stages with accuracy, and (iv). measured

temperatures are not necessarily identical to the sample temperature.

50

6. Conclusions and Future Work

This work focused on evaluating the burning capabilities of three short rotation coppice poplar wood

samples and calculating the kinetic parameters under slow and fast combustion conditions, while

understanding what can influence the obtained data. TGA was performed for the three samples at a

heating rate of 10 ºC min-1, and DTF experiments were performed at five temperatures (900, 950,

1000, 1050 and 1100 ºC) for one of the samples.

TG/DTG curves indicate the existence of three main combustion stages: drying and heating of the

particle, devolatilization and char oxidation. Calculated kinetic data showed good agreement with the

values reported in the literature.

DTF experiments showed that overall particle burnouts are similar at the exit of the DTF regardless of

the all DTF wall temperature. Kinetic data obtained through two distinct methods are in line with the

values reported in the literature.

Comparisons between the TGA and DTF data showed that the heating rate can influence significantly

the kinetic data: TGA experiments provide higher kinetic values than to those obtained from the DTF

experiments. The kinetic data obtained with the DTF is, however, more representative of industrial

combustion conditions.

In future, TGA tests conducted at different heating rates could be interesting to explore model-free

kinetic modelling and compare with model-fitting methodology. Also, a comparative study on air/inert

atmosphere can be useful.

Because short rotation coppice poplar has so much potential as an alternative fuel, it would be

interesting in future to evaluate its potential to be used in co-firing processes with coal in power

stations, particularly in regard to its environmental impact.

51

7. References

[1] European Commission. http://ec.europa.eu/.

[2] Afas, N.A., Marron, N., Dongen, S.V., Laureysens, I., Caulemans, R. Dynamics of biomass

production in a poplar coppice culture over three rotations (11 years). Forest Ecology and

Management, 255:1883-1891, 2008.

[3] McKendry, P. Energy production from biomass (Part 2): Conversion Technologies. ScienceDirect,

83:47-54, 2012.

[4] Coelho, P., Costa, M. Combustão. Orion, 2007.

[5] Ferreiro, A.I.M., Pyrolysis of pine bark, wheat straw and rice Husk: thermogravimetric analysis and

kinetic study. MSc Thesis, Instituto Superior Tecnico, Lisboa, 2015.

[6] Ferreira, P.T.F.A. Combustion of alternative biomass fuels in a domestic boiler and in a large-scale

furnace. PhD Thesis, University of Minho, 2016.

[7] White, J.E., Catallo, W.J., Legendre, B.L. Biomass pyrolysis kinetics: A comparative critical review

with relevant agricultural residue case studies. Journal of Analytical and Applied Pyrolysis, 91:1-33,

2011.

[8] Carpenter, A.M., Skorupska, N.M. Coal combustion – analysis and testing. IEA Coal Research,

1993.

[9] Karampinis, E., Vamvuka, D., Sfakiotakis, S., Grammelis, P., Itskos, G., Kakaras, E. Comparative

study of combustion properties of five energy crops and Greek lignite. Energy & Fuels, 26:869-878,

2012.

[10] Slopiecka, K., Bartocci, P., Fantozzi, F. Thermogravimetric analysis and kinetic study of poplar

wood pyrolysis. Applied Energy, 97:491-497, 2012.

[11] Khawam, A. Application of solid-state kinetics to desolvation reactions. PhD Thesis, University of

Iowa, 2007.

[12] Kok, M., Ozgur, E. Thermal analysis and kinetics of biomass samples. Fuel Processing

Technology, 106:739-743, 2013.

52

[13] Islam, M., Auta, M., Kabir, G., Hameed, B.H. A thermogravimetric analysis of the combustion

kinetics of karanja (Pongamia pinnata) fruit hulls char. Bioresource Technology, 200:335-341, 2016.

[14] Ebrahimi-Kahrizsangi, R., Abbasi, M.H. Evaluation of reliability of Coats-Redfern method for

kinetic analysis of non-isothermal TGA. Transactions of Nonferrous Metals Society of China, 18:217-

221, 2008.

[15] Yorulmaz, S.Y., Atimtay, A. Investigation of combustion kinetics of five waste woods samples with

thermogravimetric analysis. Environmental Earth Sciences, 90:511-520, 2011.

[16] Jeguirim, M., Dorge, S., Trouvé, G. Thermogravimetric analysis and emission characteristics of

two energy crops in air atmosphere. Bioresource Technology, 101:788-793, 2010

[17] Karaosmanoglu, F., Çift, B., Isigigur-Ergudenler, A. Determination of reaction kinetics of straw and

stalk of rapeseed using thermogravimetric analysis. Energy Sources, 23:767-774, 2001.

[18] López-González, D., Fernandez-Lopez, M., Valverde, J.L., Sanchez-Silva, L. Thermogravimetric-

mass spectrometric analysis on combustion of lignocellulosic biomass. Bioresource Technology,

143:562-574, 2013.

[19] Fang, X., Jia, L., Yin, L. A weighted average global process model based on two-stage kinetic

scheme for biomass combustion. Biomass and Bioenergy, 48:43-50, 2013.

[20] López-González, D., Avalos-Ramirez, A., Giroir-Fendler, A., Godbout, S., Fernandez-Lopez, M.,

Sanchez-Silva, L., Valverde, J.L. Combustion kinetic study of woody and herbaceous crops by thermal

analysis coupled to mass spectrometry. Energy, 90:1626-1635, 2015.

[21] Farrow. T.S., Sun, C., Snape, C.E. Impact of CO2 on biomass pyrolysis, nitrogen partitioning, and

char combustion in a drop tube furnace. Journal of Analytical and Applied Pyrolysis, 113:323-331,

2015.

[22] Jiménez, S., Remacha, P., Ballesteros, J.C., Giménez, A., Ballester, J. Kinetics of devolatilization

and oxidation of a pulverized biomass in an entrained flow reactor under realistic combustion

conditions. Combustion and Flame, 152:588-603, 2008.

[23] Ballester, J., Jiménez, S. Kinetic parameters for the oxidation of pulverized coal as measured

from drop tube tests. Combustion and Flame, 142:210-222, 2005.

53

[24] Wang, G., Silva, R.B., Azevedo, J.L.T., Martins-Dias, S., Costa, M. Evaluation of the combustion

behaviour and ash characteristics of biomass waste derived fuels, pine and coal in a drop tube

furnace. Fuel, 117:809-824, 2014.

[25] Costa, F., Wang, G., Costa, M. Combustion kinetics and particle fragmentation of raw and torrified

pine shells and olive stones in a drop tube furnace. Proceedings of the Combustion Institute, 35:3591-

3599, 2014.

[26] Authier, O., Thunin, E., Plion, P., Porcheron, L. Global kinetic modelling of coal devolatilization in

a thermogravimetric balance and drop-tube furnace. Energy & Fuels, 29:1461-1468, 2015.

[27] Zellagui, S., Schonnenbeck, C., Zouaoui-Mahzoul, N., Leyssens, G., Authier, O., Thunin, E.,

Porcheron, L., Brilhac, J.-F. Pyrolysis of coal and woody biomass under N2 and CO2 atmospheres

using a drop tube furnace – experimental study and kinetic modelling. Fuel Processing Technology,

148:99-109, 2016.

[28] SHIMADZU. www.shimadzu.com.

[29] College of Chemistry and Molecular Engineering. www.chem.pku.edu.cn.

[30] Yuzbasi, N.S., Selçuk, N. Air and oxy-fuel combustion characteristics of biomass/lignite blends in

TGA-FTIR. Fuel Processing Technology, 92:1101-1108, 2011.

[31] Wang, G., Zander, R., Costa, M. Oxy-fuel combustion characteristics of pulverized-coal in a drop

tube furnace. Fuel, 115:452-460, 2014.

[32] BSI. www.bsigroup.com.

[33] Smith, I. The combustion rates of coal chars: A review. Proceedings of the Combustion Institute,

19:1045-1065, 1982.

[34] Ferreira, A., Soares Dias, A., Silva, C. M., Costa, M. Evaluation of thermochemical properties of

raw and extracted microalgae. Energy, 92:365-372, 2015.

[35] Mitchell, R.E., Ma, L., Kim, B. On the burning behaviour of pulverized coal chars. Combustion and

Flame, 151:426-436, 2007.

[36] HORIBA. www.horiba.com.

54

[37] Foulkes, F.R. Physical Chemistry for Engineering and Applied Sciences. Taylor & Francis Group,

LL, 2013.

[38] Workshop. https://cedarstripkayak.wordpress.com/.

[39] WOODPRODUCTS. http://www.woodproducts.fi

[40] Incropera, F.P., DeWitt, D.P., Bergman, T.L., Lavine, A.S. Fundamentals of Heat and Mass

Transfer. John Wiley & Sons, 2006.

[41] Di Blasi, C. Modelling chemical and physical processes of wood and biomass pyrolysis. Progress

in Energy and Combustion Science, 34:47-90, 2008.

[42] Shen, D.K., Gu, S., Luo, K.H., Bridgwater, A.V., Fang, M.X. Kinetic study on thermal

decomposition of woods in oxidative environment. Fuel, 88:1024-1030, 2009.

[43] Pottmaier, D., Costa, M., Farrow, T., Oliveira, A.A.M., Alarcon, O., Snape, C. Comparison of rice

husk and wheat straw: From slow and fast pyrolysis to char combustion. Energy & Fuels, 27:7115-

7125, 2013.