uncertainty in lifecycle ghg emissions from camelina jet fuel

7/28/2019 Uncertainty in Lifecycle GHG Emissions from Camelina Jet Fuel

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Uncertainty Analysis of Life Cycle Assessment

for Camelina Jet Fuel Production

By Musab Qureshi

Photo credit: United States Federal Aviation Administration

A thesis submitted in conformity with the partial requirements for the degree of

Bachelors of Applied Science.

Department of Mechanical Engineering

University of Toronto

Toronto, Ontario, Canada

15 April, 2013


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Abstract

This thesis studies the presence of uncertainty in the well-to-pump GHG emissions of

Camelina oil based jet fuel. The first part of the thesis identifies Camelina oil as a promising

candidate for Hydro-processed Renewable Jet Fuel. Camelina HRJ fuel was found to be compatible

with aviation infrastructure and thus its use will not require much change in current aviation

practices. Camelina also has agronomic and economic advantages over its competitors; it has higher

lipid oil content and lower unit production cost. Moreover it also satisfies a key criterion of being a

non food based crop.

The second part of this thesis studies the uncertainty present in GHG emissions from the

production of Camelina based HRJ fuel. LCA analysis is carried out using the GREET model

developed by Argonne National Laboratory. Uncertainties addressed include parameter uncertainty

in Camelina crop characteristics and fuel processing, as well as scenario uncertainty concerning how

Camelina co-products are accounted for. The work of this thesis, motivated by lack of uncertainty

analysis in LCA studies, uses a Monte Carlo approach to estimate range of expected values for

GHG emissions by incorporating parameter and scenario uncertainty with distribution functions.

Results show that large uncertainties exist in GHG emissions, mainly due to uncertainty in

parameters of lipid content and the percentage of final HRJ fuel formed. The type of co-product

allocation methodology adopted also has a significant effect on the uncertainty. Findings of this

thesis agree with earlier studies on the renewable and environmentally sustainable nature of

Camelina oil jet fuel. It was found that regardless of uncertainty, WTP LCA results are GHG

emission negative. However, results obtained do emphasize the need to reduce uncertainty in GHG

emissions and highlight the importance of integrating uncertainty into the interpretation of results.

Presenting LCA results as ranges, and not as single values, will be more effective in giving decision

makers a holistic view point on the expected performance of jet fuel produced from Camelina oil.


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Acknowledgements

I would like to extend my sincere thanks and regards to my advisors Dr. Heather Maclean and

Dr. Bradley Saville for providing me the opportunity to work on this project. They kindly accepted

me despite my minimal experience with biofuel LCA. The research definitely gave me sufficient

knowledge of biofuel systems. In addition, I also learned how to approach LCA problems and reach

its goals with scientific insight.

I would like to mention my special appreciation for Jason Luk for having guided me through

the GREET Model. I would also like to thank my family and friends for giving me the support to

work on my thesis and helping me achieve my goal. Finally I would like to thank God for giving me

the strength to pursue such a wonderful opportunity.


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Table of Contents

List of Tables

List of Figures

List of Abbreviations

Chapter 1: Introduction...... 1

1.1Aviation Industry..... 11.2Impact of Conventional Jet Fuel......21.3Renewable Jet Fuel...... 41.4Uncertainty in Life Cycle Assessment.....51.5Goal and Scope.... 51.6Thesis Structure... 6

Chapter 2: Renewable Jet Fuel Selection...... 7

2.1Candidate Fuels....72.2System Compatibility .....92.3Camelina based HRJ fuel.....11

Chapter 3: Goals and Objectives....14

Chapter 4: Literature Review.....15

4.1 Camelina HRJ Fuel......15

4.2 Life Cycle Assessment.....17

4.3 Uncertainty Analysis....18

Chapter 5: Materials and Methods.... 24

5.1 Methodology....24

5.2 GREET Model.....25

5.3 Scope of Thesis....25


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5.4 Monte Carlo Method....31

Chapter 6: Results and Discussion..... 33

6.1 Scenario Uncertainty....33

6.2 Parameter Uncertainty..... 36

6.3 Comparison with Conventional Jet Fuel Emissions.... 39

6.4 Implications of Uncertainty..... 39

6.5 Limitations of Study and Future Work.... 42

Chapter 7: Conclusion.....44

References..... 46

Appendices.....49


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List of Figures

Figure 1: Graph showing price of jet fuel between 1970 and 2010....... 3

Figure 2: Chart showing the projected decline in global petroleum production...... 4

Figure 3: Camelina production test sites in Canada and USA.......16

Figure 4: LCA uncertainty in energy return ratios for algal biofuel systems..... 23

Figure 5: Methodology adopted by the thesis........ 24

Figure 6: Biofuel systems available for analysis in the GREET Model.....25

Figure 7: System boundary for the Camelina jet fuel LCA analysis......26

Figure 8: Plot showing a single histogram of values obtained form the Monte Carlo analysis.. 32

Figure 9: Chart showing the combined uncertainty in WTP GHG emissions for individual allocation

methods........ 33

Figure 10: WTP GHG Emissions results showing individual parameter uncertainties for the Mass Based,

Energy Based and Economic allocation methods........ 37

Figure 11: Chart comparing WTP GHG emissions from conventional jet fuel with WTP emissions from

Camelina based HRJ Fuel....... 39

Figure 12: Uncertainty analysis results obtained in this thesis...... 40


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List of Tables

Table 1: List of candidate renewable jet fuels for the aviation industry.....7

Table 2: Table summarizing the system compatibility of candidate jet fuels considered.....10

Table 3: Jet fuel candidates satisfying the system compatibility criterion.... 11

Table 4: Uncertainty classifications by previous studies...... 20

Table 5: Types of uncertainties and their introduction points in LCA...... 22

Table 6: Uncertainty types that were studied by past biofuel LCA uncertainty papers..... 23

Table 7: Key Camelina cultivation, processing and fuel production parameters.... 27

Table 8: Final co-product percentages formed during Camelina jet fuel production.. 27

Table 9: Distribution of the Canadian Electricity mix used by the GREET model... 28

Table 10: Uncertainty ranges for the lipid content and moisture content parameters. .. 29

Table 11: Uncertainty ranges for the farming energy and oil use parameters .... 31

Table 12: Uncertainty ranges for the co-product formation parameters..... 31

Table 13: Main coproducts form the Camelina fuel production process along with mass, energy and market

values....... 34


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List of Abbreviations

CH4: Methane

CO: Carbon Monoxide

CO2: Carbon Dioxide

CCS: Carbon Capture and Sequestration

DOE: Departmetn of Energy

HC: Hydrocarbon

GHG: Green House Gas

GREET: The Greenhouse Gases, Regulated Emissions, and Energy Use in Transportation Model

HRJ Fuel: Hydroprocessed Renewable Jet Fuel

IPCC: Intergovernmental Panel on Climate Change

g per MJ: gram per Mega Joule

N20: Nitrous Oxide

NG: Natural Gas

N/A: Not Applicable

PATNER: Partnership for AiR Transportation Noise and Emission Reduction

PM: Particulate Matter

SOx: Sulphur Oxides

VOC: Volatile Organic Compound

WTP: Well-to-Pump

WTW: Well-to-Wake


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Chapter 1: Introduction

It is widely acknowledged that man-made emissions, a large portion from carbon based fuels,

are causing major changes to the earths climate. The worlds transportation is heavily dependent

upon crude oil and the aviation sector is no exception. Majority of the jet engines currently in use

run on kerosene based fuel, which is an extract from crude oil. Aviation accounts for over 3 percent

of the global greenhouse gas (GHG) emissions and is one of the fastest growing sectors (Air

Transport Association 2008). Controlling this growth in GHG emissions is seen as an important part

of reducing emissions from the aviation sector. With environmental, economic and social concerns

on the rise, there has been a lot of focus towards shifting from a crude oil based fuel to more

renewable and cleaner jet fuels.

1.1 Aviation Industry

Aviation is a critical part of the overall global economy, providing for the movement of people

and goods from one location to another, enabling economic growth. The aviation industry carries

approximately 2.3 billion passengers and 38 million metric tons of freight annually, while

contributing 8 percent of the global gross domestic products and 2 percent of the global carbon

dioxide (CO2) emissions (Air Transport Association 2008). These numbers are at a rise since the

aviation industry is expanding at a rapid rate. Worldwide air traffic is expected to grow annually by

an average of 5.1 percent for passengers and 5.6 percent for cargo by 2030 (Daggett et al. 2008).

The advantage of covering long distances in short durations of time has made travel easier. By

making business and tourism easier, aviation contributes significantly to the overall global

economy. Aviation also plays an important role in national security and has extensive military

applications.


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For over 100 years, aviation has been driven by fossilized fuels. Current day jet fuel is a

mixture of a variety of hydrocarbons. The type of hydrocarbons in the final mixture depends on the

properties required for the final product fuel, for example, the freezing point or smoke point. For

commercial aviation Jet A and Jet A-1 are the most commonly used fuels whereas for military

applications, JP-8 and JP-4 are more widely used as fuels for the jet engines. The commercial and

military jet fuel counterparts differ only by the amounts of a few additives; Jet A-1 is similar to JP-8

and similarly Jet B is similar to JP-4 (American Society for Testing and Materials. 2006).

The aviation industry is facing political and social pressure to reduce its GHG emissions.

Market based measures such as The EU Emissions Trading System, which came into place in 2007,

have put restrictions on the GHG emissions from jet fuels. The United States Obama administration

is also considering implementing a cap and trade system to limit global warming. In Canada,

political concern has been rising steadily with British Columbia in 2008 implementing a carbon tax

of 10 dollars per ton of carbon dioxide equivalent emissions (Bureau of Economics 2010).

1.2 Impact of Conventional Jet Fuel

There are two main motivations for a move away from crude oil based jet fuel; environmental

impacts and fuel prices. There is a broad agreement in the scientific community that GHG emissions

from the combustion of fossil fuels lead to a rise in global temperature. This rise in temperature

contributes to overall climate change and ecological damage. In Antarctica, warmer temperatures

may result in more rapid melting of ice which increases sea levels and compromises the

composition of surrounding waters. Rising sea levels can also impede processes ranging from

settlement, agriculture and fishing.

Aviation accounts for 3% of the global greenhouse gas emissions and is one of the largest

growing sources of emissions in the transportation sector. Research has found that aviation


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emissions have a greater climate impact than the same emissions made at ground level. It has been

reported that emission from aircrafts flying at cruising altitude between 8km to 13km affect

atmospheric contribution in a height region where through changes in the chemical and physical

processes, the climate change impact is more significant (Baughcum 1996).

High price of jet fuel increases the cost of aviation. As shown by Figure 1 jet fuel prices have

been on the rise and have overtaken labour as the primary expense for airlines. In addition, the price

of jet fuel has a great deal of volatility associated with it. As can be seen from Figure 1, price of jet

fuel increased almost seven folds form 2002 to 2009. This complicates airline planning for future

fuel purchase since the trajectory of future fuel price is uncertain.

Figure 1: Graph showing price of jet fuel between 1970 and 2010 (Air Transport Association 2010).

Another issue with continued use of fossil fuels is that these are a non renewable source of

energy and are expected to dry out eventually. Its been argued that global petroleum production and

the supply of comparatively cheap, available oil will soon peak and then decline as shown in Figure

2. Some economists believe this has already happened. After the turning point ofpeak oil,

remaining reserves will be difficult to reach and expensive to extract. The result will be heavier oils

that are harder to process.
http://www.climateandenergy.org/Explore/EnergySecurity/Index.htmhttp://www.climateandenergy.org/Explore/EnergySecurity/Index.htm


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Figure 2: Chart showing the projected decline in global petroleum production highlighting the concept of

peal oil (Fargione et al2008).

1.3 Renewable Jet Fuel

Renewable Jet Fuel is a broad category that encompasses those aviation fuels that are derived

from renewable sources of energy. Renewable jet fuels can be broadly split into four main

categories (Marker 2005).

i. Synthetic jet fuel from thermochemical processes involving lignocellulosic biomass-to-liquid (BTL) such as Fischer-Tropsch (F-T) synthesis and pyrolysis;

ii. Advanced fermentation, catalytic, and other means of converting sugar, including those inlignocellulosic materials, and starches to jet fuel.

iii. Hydroprocessing of renewable oils to synthetic jet fuel known as hydroprocessed renewablejet (HRJ), also known as hydroprocessed esters and fatty acids jet (HEFA-J) fuels;

iv. Conversion of calorific liquids from micro-organisms to synthetic jet fuel [5].Other alternative jet fuels such as those derived from unconventional sources of petroleum (oil

sands, oil shale), coal-to-liquid process and natural gas-to-liquid process are not considered since

these fuels are derived from fossil fuels and their usage could potentially lead to the same economic

and environmental issues being faced by conventional jet fuels. The long-term viability and success


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of an aviation fuel depends on both economic and environmental sustainability. Renewable Jet fuels

have the potential to reduce the world demand for petroleum, consequently reducing the world price

of oil and products derived from it and therefore benefiting aviation. By being based on a renewable

source of energy (biomass), they also offer the potential to reduce life-cycle GHG emissions and

therefore reduce aviations contribution to global climate change.

1.4 Uncertainty in Life Cycle Assessment

Environmental Life-Cycle Assessment (LCA) is a tool that has found widespread use in the

analysis and comparison of environmental impacts from fuel systems. It considers the full life-cycle

of a fuel from extraction to disposal. Uncertainty in LCA results is seen as something that arises due

to lack of knowledge. There can be many types of uncertainty. The focus of this thesis will be on

parameter uncertainty and scenario uncertainty. Other sources of uncertainty were left out primarily

due to reasons of feasibility. Moreover, past studies have found parameter and scenario uncertainty

to have a more significant impact on LCA results (Huijbregts 1998, Weber 2012).

Uncertainty might be due to lack of knowledge of a particular parameter or a process.

(parameter uncertainty) and due to the normative choices that one has to make (scenario

uncertainty). Treating LCA uncertainties represents a challenge at different levels. However

quantification of uncertainties is important since it adds credibility to LCA results. It is only natural

that decision makers and life cycle experts be interested in the credibility of the results of LCA.

1.5 Goal and Scope

The main goal of this study is to quantify the effect of uncertainty in the well-to-pump GHG

emissions of Camelina oil based jet fuel. Possible implications as a result of this uncertainty in

emissions are also studied. A key objective is to demonstrate the need for employing uncertainty


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analysis before drawing conclusions from LCA analysis. In addition, the thesis also identifies

critical parameters that LCA of Camelina jet fuel is most sensitive to.

The 2012 GREET version is used to conduct the LCA analysis. Parameter uncertainty resulting

from Camelina crop characteristics and fuel processing are studied. In addition to parameter

uncertainty, scenario uncertainty also contributes to the overall uncertainty of the life cycle study.

The thesis studies scenario uncertainty resulting from choices in co-product allocation methods.

1.6 Thesis Structure

This thesis is organized in six chapters, each consisting of multiple sub chapters. The current

chapter, Chapter 1 introduces the core relevant topics along with the motivation for carrying out the

thesis. Chapter 2 provides a listing of the different biofuels available for use as jet fuel. A set of

criteria are established which are then used to identify promising biofuel options for jet fuel

applications. Chapter 3 defines the goals and objectives the thesis is aiming to achieve. Chapter 4

provides a literature review of the topics at the core of the thesis.

Chapter 5 outlines the methods used by the thesis to obtain the results. The chapter details the

LCA and simulation tools used to formulate the results. The scope of the thesis, LCA uncertainties

and data collection methods are also defined. In Chapter 6 the main results are presented and

discussed. Results from scenario and parameter uncertainty are analyzed and possible implications

are studied. Limitations of the study are presented along with areas for future work. The thesis

closes with concluding remarks and recommendations in Chapter 7.


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Chapter 2: Renewable Jet Fuel Selection

For a perspective aviation fuel to be used safely and effectively in the air-transportation

industry it must meet a set of important criteria. This section provides a listing of the different

biofuels available for use as jet fuel. System compatibility is established as a primary constraint for

any alternative fuel. A set of criteria are established which are then used to identify promising

biofuel options for jet fuel applications.

2.1 Candidate Fuels

Table 1 summarizes the list of candidate fuels considered in this thesis. These fuels are derived

from one of three sources: Fischer Tropsch process, Renewable Oils and Biomass.

Table 1: List of candidate renewable jet fuels for the aviation industry

Fuel Source

Fischer-Troph Jet Fuel FT synthesis of biomass

HRJ FuelHydroprocessing of plant oil to create anoxygen-free jet fuel

BioalcoholsFermentation of sugars, grains and treatedcellulosic feedstocks

BiodieselChemically reacting lipids with an alcohol

2.1.1 Fischer-Troph Jet Fuel

The Fischer-Troph process is a collection of chemical reactions that converts a mixture of

carbon monoxide and hydrogen into liquid hydrocarbons (Nigel et al. 1999). The input into the

process is a carbon containing feedstock (coal, natural gas or biomass) and the output is a liquid

hydrocarbon that can be used as jet fuel. The focus of this thesis will be on renewable feedstock.

Hence, coal and natural gas feedstocks will not be considered. The biomass feedstocks examined

include switch grass, corn stover and forest residue.


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All jet fuels produced by the FT process have similar characteristics, regardless of the

feedstock used. Since feedstock does not govern fuel properties, FT jet fuels share common

characteristics with regard to system compatibility and aircraft emissions. However, feedstock

choice does have a strong influence on production potential and life-cycle GHG emissions.

2.1.2 Hydroprocessed Renewable Jet Fuel

Renewable oils can be processed into a fuel which has properties that are similar to FT fuels.

The process involves hydrotreatment to deoxygenate oil with subsequent hydrocracking (Krbitz

1999). The output is liquid hydrocarbons that fill the distillation range of jet fuel. As in the case of

FT fuels, all HRJ jet fuels have similar system compatibility regardless of feedstock. The difference

comes about in production potential and GHG emissions. This thesis examines feedstock sources

including Soybean oil, Palm oil, Rapeseed oil, Algae oil, Jatropha oil and Camelina oil.

2.1.3 Bioalcohols

Biologically produced alcohols, most commonly ethanol and butanol, are produced by the

action of microorganisms and enzymes through the fermentation of sugar or starches or cellulose.

Ethanol is a two-carbon alcohol while butanol is a four-carbon alcohol (Malca 2006). Because of

this fundamental chemical difference they each have unique properties. However since both are

typically made by fermentation of sugar, they share similarities in production potential and GHG

emissions. In North America, corn grain is used as the primary feedstock for fuel-alcohol

production.

2.1.4 Biodiesel

Biodiesel is a vegetable oil or animal fat based diesel fuel consisting of long-chain alkyl esters.

It is typically produced by chemically processing the fatty acid from plant or animal source with

methanol. Although similar to diesel in many ways, biodiesel is different from diesel in two ways.


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First, biodiesel contains oxygen, and second, the length of the carbon chains in biodiesel is inherited

from the feedstock (Nigel 1999). Common feedstocks for biodiesel include soybean oil, rapeseed oil

and coconut oil. As discussed earlier, only plant based renewable feedstocks are considered in this

thesis.

2.2 System Compatibility

For a prospective fuel to have an impact on aviation, the most important criterion is

compatibility with current aviation systems such as fuel delivery, storage and energy density. The

fuel must be able to drop-in or directly be able to be used in existing fleet of aircrafts without any

significant modifications. The use of a fuel should not significantly degrade safety or adversely

affect aircraft operation. Air Transport Association (2008) has identified a set of criteria for safe

aircraft operation. To enable the safe operation of current aircraft, an alternative fuel must possess

an array of characteristics including:

i. High energy density, which facilitates long-range flightii. High flash point, which is the temperature above which fuel produces vapours for ignition.

This is an essential safety consideration

iii. Low freezing point and vapour pressure, which enable high-altitude flightiv. High Thermal Stability, which enables the fuel to cool engine components without a change

in its chemical properties. This increases the overall aircraft performance.

Compatibility with current aviation infrastructure is an important criterion and a fuel not

meeting this criterion would not be feasible for use as a jet fuel. Hence in this thesis, system

compatibility is considered a constraint. Only fuels meeting this criterion are considered for further

evaluation. Table 2 summarizes the system compatibility characteristics of the fuels examined by

the thesis.


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Table 2: Table summarizing the system compatibility of candidate jet fuels considered.

Fuel Verdict Note

FT Fuel Compatible Compatible on blending

Biodiesel IncompatibleLow freeze point temperature, breaks down

during storage/operation

HRJ Fuel Compatible Compatible on blending

Bioalcohol Incompatible Low flash point temperature and high volatility

2.2.1 FT Fuels

In comparison to conventional jet fuels, FT fuels have lower lubricity and do not contain

aromatic compounds (Jet Fuel Network 2007). The absence of aromatic compounds can cause leaks

in certain types of aircraft fuel systems. However, both these issues can be resolved by blending the

fuel with conventional jet fuel and with appropriate use of additives. Blends that are 50 percent FT

fuels have been used by airlines leaving the International Airport in Cape Town, South Africa.

2.2.2 Biodiesel

Research on biodiesel has indicated that biodiesel is not appropriate for use as jet fuel (ASTM

2006, Kalnes et al. 2009). Biodiesel poses a risk of breaking down during storage or during use in

aircraft fuel systems, leaving that could compromise flight safety and performance. Moreover, pure

biodiesel freezes at temperatures typical of high-altitude flight. Even tests of light biodiesel with

conventional jet fuel have seen the problems persist. Based on these factors, biodiesel cannot be

considered as a drop-in fuel and hence is not considered for further analysis.

2.2.3 HRJ Fuel

Properties of HRJ fuel are similar to those of FT jet fuel; reduced lubricity and low aromatic

content and high thermal stability. As for FT fuel, problems due to reduced lubricity and aromatic


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content can be addressed by use of conventional jet fuel blends and use of additives. HRJ Fuel has

been tested by several airlines and companies showing promising potential for use as a jet fuel.

2.2.4 Bioalcohols

The two types of bioalcohols considered by the thesis are ethanol and butanol. While alcohols

may be an attractive automotive fuel, they are not suitable for use in aircrafts (Bernesson et al.

2006). Ethanol has a low flash point, making it dangerous to handle and poses a risk to aircraft

occupants. It is highly volatile and could cause problems during high altitude flight. Moreover, its

energy content per unit mass is approximately 40 percent less than that of jet fuel. While not as

incompatible as ethanol, butanol still poses unacceptable safety risks due to its high volatility and

low flash point.

Hence, through the above analysis, HRJ fuel and FT fuel were found to meet the system

compatibility criteria. It has to be noted that in the above analysis feedstock types were not

considered. This is because the effect of feedstock type does not affect properties significantly

enough to influence jet fuel safety and performance.

2.3 Camelina based HRJ fuel

The thesis arrives at the following fuels that show promising potential for use as jet fuel.

Table 3: Jet fuel candidates satisfying the system compatibility criterion

Fuel Feedstock Source

Fischer-Troph Jet Fuel Switch grass and Corn stover

HRJ Fuel Soy bean oil, Rapeseed oil, Palm oil, Algae

oil, Jatropha oil and Camelina oil

The options listed above are all promising jet fuels and currently extensive research is ongoing

on all of the above. In recent years however, there has been heighted interest in use of Camelina


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feedstock for production of jet fuel. In addition to its high compatibility with existing aviation

infrastructure, Camelina has some agronomic and economic advantages over its competitors.

Moreover it also satisfies a key criterion of being a non food based crop.

2.3.1 Agronomic Advantages

Camelina has several advantageous agronomic characteristics which make favorable for use as

a renewable jet fuel feedstock. With high seed oil content as well as high yield of oil per hectare,

Camelina feedstock can be efficiently converted into high quality renewable jet fuel. Camelina is

also well adapted to production in a variety of climatic zones. It germinates at low temperature, and

seedlings are very frost tolerant. Camelina crops perform well under drought stress conditions and

compared to most other oil seed crops, have shown better performance in low rainfall regions

(Frohlich 2005).

2.3.2 Unit Production Cost

Camelina is particularly attractive as an alternative feedstock for jet fuel production as a result

of its low unit production cost when compared with other fuel options. A recent study done by

School of Agricultural Engineering at Purdue University (Agusdinata 2011) found Camelina and

Corn Stover have the lowest total unit cost whereas Algae has the highest (refer appendix A). The

same study reports that Camelina-derived jet fuel will become financially viable at year 2015 and

algae by 2040.

2.3.3 Competition with Food Crops

Biomass sources like soybean and rapeseed are excellent candidates for biofuel feedstock.

However, these are also extensively used in the food industry for production of vegetable oil. A

dilemma facing decision makers and researchers is that of choosing to use the crop as food or fuel


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(Food and Agricultural Policy Research Institute 2008). There have been fears that adopting food

crops as a source of feedstock for biofuel could lead to high food price levels and volatility. A study

for the International Centre for Trade and Sustainable Development found that market driven

expansion of ethanol in the United States increased maize prices by 21 percent in 2009.

A key advantage of Camelina crop is that it is not predominantly a food based crop (Frohlich

2005). Although Camelina is also used in the food industry, its consumption is comparatively

limited and adoption of Camelina as a fuel is not expected to have significant effect on food prices.


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Chapter 3: Goals and Objectives

This thesis examines the uncertainties associated with life cycle GHG emissions of Camelina

based HRJ Jet Fuel. The research follows a well-to-pump approach, analyzing emission

uncertainties associated with the up stream feedstock and fuel processing stages. Emissions from the

use stage of the jet fuel are not considered. The GREET model developed by the Argonne National

Laboratory is used to conduct the LCA analysis. Uncertainty ranges of individual parameters are

modeled and scenario emissions are simulated by conducting a Monte-Carolo analysis using the

ModelRisk software.

The objectives of this thesis are not to compare the results with other systems or references,

rather it is meant to provide insight into the emission uncertainties associated with Camelina HRJ

fuel production practices. At the highest level the question is: To what extents do LCA uncertainties

in Camelina HRJ fuel affect final GHG emissions? In particular, the following questions are

examined:

1) What is the nature of uncertainty in estimates of life cycle GHG emissions of CamelinaHRJ Fuel? That is, quantitatively speaking, what is the extent of uncertainty in final

resultant emissions?

2) What parameters significantly affect the final outcome? On which parameters shouldefforts be focused on to reduce/address emission uncertainty?

3) What are the possible implications of the presence of uncertainty in emissions? Possibleimplications on decision making are examined.


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Chapter 4: Literature Review

This section reviews the literature pertinent to the thesis topic. It is split into three sections.

Firstly, Camelina crop cultivation and fuel processing is studied. This followed by an analysis of the

life cycle assessment approach towards biofuel systems. Finally, Uncertainty Analysis is studied by

looking at the sources and types of uncertainty.

4.1 Camelina HRJ Fuel

4.1.1 Camelina Crop

Camelina is an ancient oilseed crop that belongs to the family Cruciferae. Some examples of

this family include oilseeds like mustard, rapes, canola, crambe and vegetables like cabbage,

cauliflower and broccoli (Frolich 2005). Camelina is more commonly known as false flax. Camelina

sativa (most common Camelina species) is an annual summer or wintering plant which has branched

smooth or hairy stems that become woody at maturity and reach heights ranging from 1-3 feet.

Camelina is a short-seasoned (85-100 days) crop, and can be grown under different climatic and soil

conditions with the exception of heavy clay and organic soil (Zubr, 1997). Camelina is a low-input

crop with minimum nutrient requirements and can grow well in low-fertility or saline soils when

compared to other oilseed crops like canola, soybean or sunflower.

4.1.2 Geographical Distribution

Camelina sativa originated in Germany and its cultivation spread to Central Europe (Budin et

al. 1995). From the beginning of 20th century up to the 1930s Camelina sativa was grown

sporadically in France, Belgium, Holland, the Balkans and Russia. In 1950s Camelina was still

grown in Sweden and it was an important crop in the USSR (Zubr 1997).

Today, Camelina is found in almost all regions of Europe, Asia and North America. It has

especially seen rapid growth in North America. Montana State University (MSU) Agricultural


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Experiment Research Centers (2004) conducted a study on nine different oilseed crops for biofuel

production and Camelina emerged as a potential oilseed crop for production across Montana and the

Northern Great Plains. Figure 3 shows the test sites for Camelina production for the year 2004 in US

and Canada by Sustainable Oils, Montana, USA.

Figure 3: Camelina production test sites in Canada and USA

4.1.3 Camelina Oil Extraction

Camelina oil is the main product from camelina seeds and the average yield of oil from the

seeds is 35-45% (Rode 2002, Zubr 2003). It is a golden yellow color liquid with a mild nutty and

characteristic mustard aroma. Extraction of oil from oilseeds yields a number of co-products out of

which Camelina oilcakes/meal are the most dominant. Other co-products such as glycerine and

propane are formed in minor quantities. Camelina meal consists of 5-10 % residual oil, 45 % crude

protein, 13 % fiber, 5 % of minerals and some minor levels of vitamins (Zubr 1997). Mikersch

(1952) reported the residual oil content as 13 %, ash (6.6%), crude fiber (11.7%), protein (32.8%)

and non-nitrogenous matter (27.2%) in Camelina meal. Because of the high crude protein content,

Camelina meal is considered economically important and can be used as nutritive supplement in

animal feed formulations.


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4.1.4 Camelina Oil to HRJ fuel

Before the Camelina oil is fit for use as a jet fuel it requires further processing. The processing

involves hydrotreatment to deoxygenate the oil with subsequent hydrocracking to create

hydrocarbons that fill the distillation range of jet fuel (Hileman et al. 2009). The output from this

process, Hydroprocessed Renewable Jet fuel is considered a drop-in fuel meaning that the fuel is

compatible with existing production, storage, distribution, and combustion infrastructure. In the

process of producing HRJ fuel from Camelina naphtha range co-products are produced. The naphtha

can be upgraded for use as high-octane gasoline or can be directly used as process fuel for hydrogen

production feed.

4.2 Life Cycle Assessment

Life cycle assessment (LCA) is a tool to help assess the total resource use and environmental

effects associated with products throughout their entire life cycle, from extraction (or cultivation),

through production, transportation, use, and disposal (ISO 2006). Initial studies on life cycle aspects

of products and processes date back from the late sixties and early seventies and the major focus

was on quantifying the material and energy consumption of a product (Bjorklund 2012). The oil

crisis of 1970s, energy debate, the environmental debate on waste disposal and the more recent

climate change concerns are considered to be the potential drivers behind LCA (Baumann et al.

2004). According to ISO standard 14040 (ISO 2006), LCA can assist in:

identifying opportunities to improve the environmental performance of products at variouspoints in their life cycle,

informing decision-makers in industry, government or non-government organizations (e.g.forthe purpose of strategic planning, priority setting, product or process design or redesign),


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the selection of relevant indicators of environmental performance, including measurementtechniques, and

marketing (e.g. implementing an eco-labelling scheme, making an environmental claim, orproducing an environmental product declaration).

It must be emphasized that to determine the holistic impact of a product, an LCA must be taken

into account as opposed to simply considering just a single stage such as use stage within product.

In this study, the LCA is used to assess the GHG emissions from a Camelina based jet fuel. The

focus will primarily be on quantifying the uncertainties that are inherently present in LCA

outcomes.

4.3 Uncertainty Analysis

Uncertainty in LCA results is seen as something that arises due to lack of knowledge (Heijungs

1996). This might be due to lack of knowledge of a particular parameter or a process. Treating LCA

uncertainties represents a challenge at different levels. However quantification of uncertainties is

important since it adds credibility to LCA results. It is only natural that decision makers and life

cycle experts be interested in the credibility of the results of LCA.

4.3.1 Uncertainty in Life Cycle Assessment

The LCA methodology has a wide range of applications and is no doubt a very useful tool for

the critical assessment of a product over its entire life cycle from cradle-to-grave(Tukker 1999).

However, the methodology is not without limitations. The limitations of the LCA methodology,

especially regarding uncertainty in the resultant outcomes, are the subject of many publications

(Heijungs 1996, Huijbregts 2003).


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Results from an LCA are usually presented as point estimates, which overestimate its reliability

and do not address the uncertainty inherent in input variables. This can lead to decisions that are

unnecessarily costly or may mislead perception about the environmental profile of the product.

Treating uncertainty is a challenge at different levels. Currently, the analysis of the uncertainty in

LCA studies, even though crucial, is rarely done (Ross, 2002), because of the lack of simple

methods allowing its quantification. However, when data and resources are available, integrating

uncertainty of both human and natural systems is important since it provides decision makers useful

information to assess the reliability of LCA-based decision and also helps guide future research

towards reducing uncertainty.

Before proceeding to define the different types and sources of uncertainty, a contrast with

variability should be made. Variability is understood here as originating from inherent variations in

the real world. On the other hand uncertainty relates to lack of knowledge (inaccurate

measurements, lack of data etc.). Consider this example; the yield rate of a crop at a specific

farming location may be subject to uncertainty while the overall yield rate at a typical location may

be subject to variation. Variability stems from quality of data that is heterogeneous in nature while

uncertainty stems from lack of knowledge.

4.3.2 Types of Uncertainty

There are many ways of classifying uncertainty. Classification of uncertainty types has a

subjective element to it and different authors have adopted different approaches for classification.

Without going in details of defining these categories, Table 4 lists out how some past studies have

classified uncertainty.


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Table 4: Uncertainty classifications by previous studies

Bevington & Robinson (1992) Morgan & Henrion (1990)

Hofstetter (1998)

Huijbregts (2001)

systematic errors

random errors

statistical variation

subjective judgmentlinguistic imprecisionvariability

inherent randomnessdisagreement

approximation

parameter uncertainty

model uncertaintyuncertainty due to choicesspatial variability

temporal variabilityvariability between sources and

objects

Funtowicz & Ravetz (1990) Bedford & Cooke (2001) US-EPA (1989)

data uncertaintymodel uncertainty

completeness uncertainty

aleatory uncertaintyepistemic uncertainty

parameter uncertaintydata uncertaintymodel uncertainty

ambiguityvolitional uncertainty

scenario uncertaintyparameter uncertainty

model uncertainty

The focus of this study will be on parameter uncertainty and scenario uncertainty. Other

sources of uncertainty were left out primarily due to reasons of feasibility. Moreover, past studies

have found parameter and scenario uncertainty to have a more significant impact on LCA results.

4.3.3 Parameter Uncertainty

Parameter uncertainty reflects the incomplete knowledge about the true value of a parameter.

To give an example, consider the case of a crop based biofuel. When conducting an LCA to study

GHG emissions from this biofuel, parameter uncertainties could stem from aspects such as

uncertainty in crop yield, irrigation water consumption and required amount of fertilizer. Parameter

uncertainties mainly arise due to imprecise measurements, estimations, assumptions or lack of

quality data.

Monte Carlo simulation is a technique to quantify parameter uncertainty. The advantage of

conducting a Monte Carlo simulation is that it propagates known parameter uncertainties into an

uncertainty distribution of the variable output (Sonnemann et al. 2003). Hence the uncertainties


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present in the input parameters are carried on to the final output result. To perform Monte Carlo

simulation, each uncertain input parameter has to be specified as an uncertainty distribution.

However, LCA studies generally involve many parameters and it is often unfeasible to characterize

ranges for all of these parameters. Hence it becomes important to identify parameters that have a

significant impact on the final LCA results and thereafter carry out a detailed uncertainty analysis of

these specific parameters.

4.3.4 Scenario Uncertainty

Scenario uncertainties result from the normative choices that are unavoidable in LCA studies.

Considering the example of a crop based biofuel again, scenario uncertainties could stem from

aspects such as choice in the type of co-product allocation method (energy, mass, market,

displacement) and choice in the approach to dealing with land use changes. Choosing one scenario

option over the other could lead to uncertainty because different choices may generate different

LCA outcomes. Treatment of scenario uncertainty entails two steps. First step is to identify the

normative choices in the LCA study and secondly, to quantify the consequences of these normative

choices in terms of output uncertainty.

4.3.5 Sources of Uncertainty

When discussing uncertainties, one of the first things that could arise in ones mind is the

actual source of the uncertainty, its presence in the LCA itself and where exactly is its point of

introduction in the LCA. Although a fully satisfying classification may be difficult to agree upon,

uncertainty can be roughly split into three following sources (Huijbregts 1998):

data for which no value is available data for which an inappropriate value is available data for which more than one value is available


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Table 5 presents an overview of the types of uncertainties and the points of their introduction in

the life cycle assessment. There is a general consensus that the most important points of introduction

of uncertainties are the data inventory phase of the LCA and the phase of characterization of the

uncertainties by the user (Huijbregts 1998, Heijungs 2004, Peereboom et al. 1999).

Table 5: Types of uncertainties and their introduction points in LCA

4.3.6 LCA Uncertainty in Biofuel Systems

Although quantifying uncertainty in life cycle studies is not a new idea, its use in LCA for

biofuel systems is relatively new. From literature review two main research papers were identified

that studied uncertainty in biofuel systems; Uncertainty in LCA of Rape-seed Biodiesel (Malca

2010) and Uncertainty Analysis of LCA for Algal Biofuel Production (Sills et al. 2012). Both

these papers highlight the high degree of uncertainty in LCA results for biofuel systems. Figure 4

shows the degree of uncertainty in Energy Return ratios from previous algal biofuel systems studied

by Sills et al. The authors suggest that similar uncertainty ranges can be expected for other LCA

results including GHG emissions. Malca in his study of uncertainty in LCA of rapeseed oil

reinforces the same point of high uncertainty in LCA of biofuel systems.

Type LCA Phase

Goal and Scope InventoryChoice of impact

categoriesCharacterisations

Parameter

Uncertainty-

Inaccuratemeasurements, lack

of data-

Uncertainty in life timesof substance and relativecontribution to impact.

Lack of data

Scenario

Uncertainty

Choice offunctional unit,

systemboundaries

Choice of allocationmethod, technology

level, average data

Leaving out impactcategories (eg: land

use)

Choice of

characterisation methods


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Figure 4: LCA uncertainty in energy return ratios for algal biofuel systems (Sills et al. 2012)

Camelinas use as a biofuel is relatively recent and data to conduct a similar analysis as in

Figure 4 is not readily available. However from the few LCA studies that have been conducted

significant uncertainty has been reported. Shonnard (2009) reports an uncertainty of over 10 g CO2

per MJ fuel for life cycle GHG emissions from Camelina fuel. Table 6 presents the uncertainty

types that were studied by past biofuel LCA uncertainty papers (Malca 2010, Sills et al. 2012).

These are used as a guide to identify parameters to study in this thesis.

Table 6: Uncertainty types that were studied by past biofuel LCA uncertainty papers

LCA Study Scenario Uncertainty Parameter Uncertainty

Rapeseed Oil Co-product Allocation

No Allocation Mass Energy Economic System Expansion

Fertilizer application ratePesticide application rateFuel consumption for agricultural machinery

Agricultural YieldSoil Carbon Stock Exchange

Oil extraction rateIndustrial process energy use

Algal Biofuel Productivity (Yield)Cultivation energy consumptionLipid Content

Nitrogen Loading

Fertilizer absorption efficiencyProcess Energy Use


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Chapter 5: Materials and Methods

This chapter outlines the methods used by the thesis to obtain the results. The section details

the LCA and simulation tools used to formulate the results. The scope of the thesis, LCA

uncertainties and data collection methods are also defined.

5.1 Methodology

Figure 5 summarizes the methodology adopted in this thesis.

Figure 5: Methodology adopted by the thesis

Firstly, parameters essential to the production of Camelina jet fuel were identified from review

of existing biofuel LCA literature studies (Refer Chapter 4) and from those available in the GREET

Model version 2012. Uncertainty ranges for these parameters were then established from literature

values and in some cases appropriate assumptions were made. To study uncertainty, Monte Carlo

simulations were carried out on the GREET model based on the parameter ranges established.

Uncertainty analysis was conducted by studying the combined effect of all parameters on

uncertainty. To study effect of parameters, each parameter was simulated individually, keeping

other parameters at their baseline values. Finally, the results obtained were discussed and their

possible implications were explored.

Interpretation of Results

Uncertainty Analysis

Monte Carlo simulation on GREET Model

Selection of Essential Parameters


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5.2 GREET Model

Developed by the Argonne National Laboratory, the GREET (Greenhouse Gases, Regulated

Emissions, and Energy Use in Transportation) model is a publicly available LCA tool designed to

investigate numerous fuel cycles (Wang et al. 2011). The fuel-cycle model for transportation fuels

considers the operations involved in producing and using fuels, while the vehicle-cycle model

considers operations involved in manufacturing and decommissioning vehicles. The latter model is

not used in the analysis since this thesis is limited to WTP emissions.

Figure 6 presents the many biofuel systems available for analysis. GREET can be used to

compute fossil, petroleum, and total energy use and emissions of greenhouse gases (CO2, CH4, and

N2O). Moreover, it can also be used to compute emissions of five criteria pollutants: carbon

monoxide (CO), volatile organic compounds (VOCs), mono-nitrogen oxides (NOx), sulfur oxides

(SOx) and particulate matter.

Figure 6: Biofuel systems available for analysis in the GREET Model

5.3 Scope of Thesis

This section defines the scope of the thesis. It is split into two parts. Firstly the overall LCA

system is described along with the system boundary. The second part details the parameters that the

thesis examines to study uncertainty in LCA GHG emissions.

Corn Sugarcane

Cellulosic BiomassSwitchgrass

Fast Growing TreesCrop Residue

Forest Residue

Renewable Oil

Soybean

Palm OilJatropha

RapeseedCamelina

Ethanol

ButanolEthanol

Ethanol

HydrogenMethanolFT Diesel

FT Jet Fuel

Biodiesel

Renewable DieselRenewable Gasoline

HRJ Fuel


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5.3.1 System Boundary and System Description

The main goal of this thesis is to quantify the effect of parameter and scenario uncertainty on

life cycle GHG emissions of Camelina HRJ fuel. GHG emissions are studied in units of grams per

mega joule of fuel (g per MJ fuel). Figure 7 depicts the system boundary for the LCA analysis in

this thesis. The boundary defines a well-to-pump fuel cycle analysis. Any analysis beyond the

pump stage such as emissions from usage of fuel is not included. Construction, replacement, and

decommissioning of equipment and facilities for the fuel cycle are also excluded from the analysis.

Figure 7: System boundary for the Camelina jet fuel LCA analysis

The 2012 GREET version is used to conduct the LCA analysis. The focus is on analyzing

Camelina jet fuel in a Canadian context. GREET has inbuilt default values for all model parameters.

It also allows the user to manually input values for these parameters. The following is a listing of

some of the key parameters used in the GREET model (Refer Appendix B for a breakdown of the

process parameters used in the GREET model). Since interest in Camelina as a potential biofuel is

relatively recent, data on cultivation, fuel processing and co-products is scarce. Hence, most default

values in the GREET model have not been changed. GREET bases these values on data from

literature while also in some cases correlating parameters with other biofuels. Certain default

parameters have been changed given the Canadian context of this thesis and the availability of new

data in recent literature.


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Camelina Cultivation and Fuel Processing: Cultivation and fuel processing stages entail a

wide array of parameters. Table 7 lists some of the key parameters along with the values used in the

analysis. GREET model provides an option of inputting appropriate values for these parameters and

calculates emissions based on the input values. Cultivation data was inputted based on a recent

Camelina cultivation study done by Agriculture Canada (2012). For fuel processing parameters

default values in the GREET model were used which are based on research done by Shonnard

(2009).

Table 7: Key Camelina cultivation, processing and fuel production parameters

Camelina Cultivationand Processing

Farming energy use 965 Btu/kg

Bio Oil extraction energy 842 btu/lb of bio oil

Biomass use for oil extraction 2.9 lb/lb bio oil

Camelina biofuel yield (system level) 1.2 lb/kg camelina

HRJ Fuel Production

Oil use 1.27 lb oil/lb jet fuel

Fuel Production energy use 3372 btu/lb jet fuel

HRJ Fuel Yield (system level) 1.1 lb/kg camelina

System Co-products: While obtaining a Camelina based HRJ fuel, the production processes

produce other products in addition to the fuel. Table 8 presents the mixture percentages of the

different co-products produced during the oil extraction and HRJ fuel production stages (Argonne

National Laboratory 2011).

Table 8: Final co-product percentages formed during Camelina jet fuel production

Oil Extraction

(Input: Camelina Grain)

Camelina Oil 35%

Camelina Meal 60%

Glycerine 3%

Others 2%

Fuel Production(Input: Camelina Oil)

HRJ Fuel 76 %

Naphtha 14 %

Propane mix 10 %


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Electricity generation Mix: The GREET model gives an option of choosing between a United

States electricity mix and a Canadian electricity mix. Since this thesis is oriented towards Canadian

prospects, the Canadian energy mix is selected. Emissions due to electricity usage are calculated

based on this mix.

Table 9: Distribution of the Canadian Electricity mix used by the GREET model

Canadian Electricity Mix

Natural Gas 30.2%

Coal 11.3%

Nuclear Power 21.2%

Biomass 0.8%

Others 36.4%

Hydroprocessing: The hydrogen required for the Hydroprocessing stage of an HRJ fuel can be

obtained from multiple avenues (coal, natural gas, thermal chemical water cracking, electrolysis). In

Canada, the cheapest option is via natural gas (Nangia 2006). Hence, natural gas is chosen as the

source of hydrogen for the hydroprocessing stage.

Land use change: Land use change is an emergent and important topic the life cycle study of

biofuels. The GREET model does provide the option of accounting for land use change impacts.

However there is limited information and high degree of uncertainty in the data (Fargione et al.

2008, Halleux et al. 2008). Therefore, land use change effects are not considered in the analysis.

Carbon Capture and Storage (CCS): CCS is a process of capturing carbon dioxide from

large point sources and storing it where it will not enter the atmosphere.However, deployment of

CCS is unproven. Moreover, some researchers have pointed out feasibility of implementing CCS at

a large (IPCC 2009). Given the uncertainty surrounding CCS, it is assumed emission capture does

not take place in the system.


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5.3.2 LCA Uncertainties for Camelina HRJ Fuel

This section details the parameters that the thesis examines to study uncertainty in life cycle

GHG emissions. Inherently there is uncertainty associated with these parameters which add on to

the overall uncertainty of LCA results. These parameters were identified as critical based on

literature review of biofuel system uncertainties. Moreover choice of these parameters was also

based on the option in GREET to change values for these parameters.

Camelina Crop Characteristics: The two parameters examined under this section are lipid

content and moisture content. These parameters play an important role in determining the oil that

can be extracted form the Camelina seed. It is desirable to have higher lipid content in seeds since

higher lipid content results in higher oil yield. Moisture content affects a number of aspects such as

seed spoilage and fuel yield. 10 percent moisture content has been found to give ideal performance.

Table 10 presents the uncertainty ranges found in literature for the (Agriculture Canada 2012)

Table 10: Uncertainty ranges for the lipid content and moisture content parameters.

Parameter Lower limit Baseline Upper limit

Lipid Content 38% 40.5% 43%

Moisture Content 8% 9.5% 11%

Co-Product Allocation: Camelina HRJ fuel production is a multi-output product system

which results in formation of co-products in addition to the final fuel. Allocation is a method which

distributes the input energy and material flows and output emissions amongst the product and co-

products (ISO 14044:2006). The choice of the allocation method has considerable impact on the

final LCA results, and is also an area where great uncertainties have been observed amongst the

reviewed studies (Curran 2007, PATNER 2009). This thesis studies the effect of three types of

allocation methodologies; energy based allocation, mass based allocation and economic allocation.


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The mass and energy allocation approaches distribute the life cycle GHG emissions based on

either the mass or energy content, respectively, of the fuel and co-products. The economic allocation

approach distributes emissions based on the market price of co-products and fuel product.

Uncertainty in emissions from all three allocation methodologies is studied.

It has to be noted that for allocation methodologies, ISO recommends use of a system

expansion approach. This approach requires an alternative way of generating the exported functions

by expanding the system limits to include the additional functions related to the co-products and

data can be obtained for this alternative production (Huijbregts 2003). GREET does provide the

option to adopt a system expansion approach. However, when system expansion was selected for

Camelina oil, the GREET model presented an error in results. There could be many reasons for the

error. It is postulated that the error might be due to the complex nature of the approach. Ekvall and

Finnveden (2000) suggest that in application of system expansion accurate results can be acquired

only when accurate data on the effects on the production of exported functions and on the indirect

effects of changes in the exported functions are used. Given the relative recent interest in Camelina

as a jet fuel, this data might not be part of GREETs in built parameters. Hence because of

modelling error, analysis on system expansion could not be carried out.

Process Energy and Efficiency: Since interest in Camelina jet fuel is relatively recent, data on

fuel production is scarce. Hence establishing uncertainties for production parameters becomes

challenging. However, uncertainty in parameters such as energy use shows surprising consistency

for different biofuel systems. For example, rapeseed and soybean both show uncertainties of 8 to 15

percent for farming energy use (Mortimer 2003, PARTNER 2009). Based on this, a 10 percent

uncertainty was set for Camelina farming energy. The default GREET farming energy value was

used for the baseline case. Similarly, a 10 percent uncertainty was also set for Oil Use during the

Hydroprocessing stage.


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Table 11: Uncertainty ranges for the farming energy and oil use parameters


Farming Energy 865 Btu/kg 965 Btu/kg 1065 Btu/kg

Oil Use

(Hydroprocesing Stage) 1.3 lb-oil/lb-fuel 1.4 lb-oil/lb-fuel 1.5 lb-oil/lb-fuel

Co-product Formation: Another area of uncertainty is in the formation of co-products during

fuel processing. Extent and individual distribution of Co-products formed plays an important role in

estimating the lifecycle GHG emissions. For example, propane formed as a co-product has a much

larger environmental footprint when compared to glycerine. This thesis studies the effect on GHG

emissions for a variety of co-product ranges. These ranges are consistent with those found in

literature (PATNER 2009, Shonnard 2009)

Table 12: Uncertainty ranges for the co-product formation parameters


Naphtha-propane co-product mix

(Fuel constant at 76%)17%-7% 14.5%-9.5% 12%-12%

Fuel- Naphtha-propane mix 82%-6%-4% 68%-14%-10% 54%-22%-16%

5.4 Monte Carlo Method

As suggested by Huijbregts (1998), parameter and scenario uncertainties were incorporated

using Monte Carlo simulation. Monte Carlo method replaces point estimates with random variables

drawn from probability density functions (Sonnemann 2003). This thesis uses a simple normal

distribution function for all analyses. The software ModelRisk provides the required utilities to carry

out Monte Carlo simulations.

Once a probability distribution is incorporated into a spreadsheet cell, each time the

spreadsheet recalculates a new value of the variable from the distribution and is used for further


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calculations. To conduct a credible analysis, the entire simulation has to be run at a sufficiently

high number of trials (around 10,000 times). The software allows for studying individual output

cells which in this thesis is the resultant life cycle GHG emissions. The results form the output GHG

emissions are summarised in a single histogram of values. Figure 8 shows a snap shot of such a

histogram.

Figure 8: Plot showing a single histogram of values obtained form the Monte Carlo analysis .


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Chapter 6: Results and Discussion

The main results are presented and discussed in this section. The section starts off by analyzing

results from scenario and parameter uncertainty concerning WTP GHG emissions from Camelina

HRJ fuel. The implications of the presence of uncertainty in emissions are then studied. Lastly the

limitations of the study are presented along with areas for future work.

6.1 Scenario Uncertainty

For scenario uncertainty, WTP GHG emissions were calculated using different co-product

credit approaches-mass, energy and economic based (market value) allocations-to understand the

implications of these methods in the GHG emissions of Camelina HRJ fuel. It is explained in the

methodology section as to why the system expansion approach was not chosen for the study. From

the results it can be seen that GHG emissions range from -45.3 g per MJ fuel (95th

percentile mass

allocation) to -29 g per MJ fuel (5th percentile economic allocation), which means that regardless of

the scenario (i.e allocation methodology), the results are GHG emission negative. The uncertainty in

the scenario emissions comes about from the procedure used to account for co-product credits.

Figure 9: Chart showing the combined uncertainty in WTP GHG emissions for individual allocation

methods. For each bar, the top extreme represents the 5th percentile and the bottom extreme represents the

95th percentile. Red line illustrates mean value of emissions.

-47

-45

-43

-41

-39

-37

-35

-33

-31

-29

-27

-25

Energy BasedAllocation

Mass BasedAllocation

EconomicAllocation

GHG(gperMJfuel)

Allocation Type

AllocationType

Mean WTP GHGEmission

( g per MJ fuel)

Energy Based - 33.7

Mass Based - 43

Economic - 31


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6.1.1 Sources of Difference in Results

Focusing on scenario uncertainty, Figure 9 shows that mean GHG emissions range from -43 g

per MJ fuel (mass allocation) to -31 g per MJ fuel (economic allocation). The differences in the

individual allocation methods are due to the nature of allocation unique to each method. Table 13

presents the main co-products form Camelina HRJ production (from the GREET model) along with

their mass, energy content and market values. Note the units for these parameters; they are defined

based on the original Camelina seed mass. Take the example of Camelina meal, one would interpret

the table as 0.33 lb of Camelina meal was obtained from 1 lb of Camelina seeds. The 0.33 lb of

Camelina meal had a combined energy of 2,300 Btu and a combined market value of 0.04 dollars.

Table 13: Main coproducts form the Camelina fuel production process along with mass, energy and market

values (GREET 2012)

Products Mass content(lb/ Camelina seed lb)

Energy Content(Btu/ Camelina seed lb)

Market Value(USD/Camelina seed lb)

Camelina Meal 0.33 2,300 0.04

Propane Mix 0.06 1,530 0.018

Naphtha 0.08 1,600 0.044

Total Coproduct 0.47 5,430 0.102

Camelina HRJ Fuel 0.42 8,000 0.231

From Table 13, it is clearly evident that when compared to the process co-products, Camelina

HRJ fuel has a much higher energy content. However, when mass is compared, co-products such as

meal have significant mass (33% of overall mix). This would explain the difference in results from

adopting the mass and energy based allocation methodologies. Since co-products have significant

mass, the mass based approach allocates considerable GHG emissions to the co-products. Whereas

in the case of the energy based approach, the low energy value of the co-products results in lower

lifecycle emissions being allocated to them. A similar explanation can be given for the high GHG

emissions from the economic based allocation approach. Table 13 lists the market values used by

GREET to allocate the emissions. As can be seen, the co-products are much less valuable than the

HRJ fuel. In fact the disparity in market value is larger than the disparity in mass and energy content


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values. Hence in the market value based approach, a large portion of the emissions are allocated to

the fuel, larger than the mass and energy based approaches.

To summarize, the difference in results from the mass, energy and economic allocation

approaches can be attributed to their different approaches to allocation, based on mass, energy and

market value respectively. Camelina fuel co-products have high mass, low energy density and low

market value (high and low are based on relative terms on comparison with the fuel). The

difference in WTP GHG emissions is a reflection of these co-product characteristics.

6.1.2 Appropriateness of Allocation Methods

It was confirmed in the earlier section that the choice of allocation method has a significant

influence on the final LCA emission results. In literature, different studies have adopted different

allocation approaches and some even adopting a combination of approaches (PATNER 2009, Malca

2010). This implies that there is no perfect fit for all allocation method. There are advantages and

disadvantages in each allocation methodology.

Mass and energy allocation methods which are based on physical properties are easily

applicable (Kim and Dale 2005). Data on the properties are generally available and easily

interpreted. However, as reported by the LowCVP working group (2004), different dispositions of

co-products can produce different environmental impacts, which would not be reflected in the

calculation. The mass and energy might not have a direct correlation with the GHG emissions. For

mass based allocation, it seems strange that the more co-product one produces in the production of

Camelina HRJ fuel, the better the GHG emission performance of the Camelina fuel will be. Energy

based allocation also has similar problems (LowCVP 2004). However as Malca (2004) argues, use

of an energy allocation method would be particularly appropriate in cases where along with the

main product, majority of the co-products were also burned as fuels. In this thesis, only a small


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fraction of the Camelina coproducts can be used as fuel. Hence, an energy based allocation method

would seem less appropriate given the criteria laid out by Malca.

The main advantage of Market based allocation is that it is universally applicable and reflects

the underlying economic reasons for production. Economic factors are important in determining

how co-products are used. These could change over time to reflect changing economic markets

(LowCVP, 2004). There have been concerns in literature (Jonassonm 2004, Malca 2006) about the

effect of short term price changes and their effect on emission calculations. Price changes change

LCA results, whereas in reality the use and environmental impact of the products may not really

change.

To summarize, there are advantages and disadvantages in each allocation methodology. It is

important to recognize that there is no single allocation procedure which is appropriate for all

Camelina fuel production process. In this thesis, both mass based and market value based

approaches seem appropriate. Hence, future Camelina LCA studies should consider conducting a

sensitivity analysis incorporating the two approaches.

6.2 Parameter Uncertainty

With respect to parameter uncertainty, WTP GHG emissions probability distributions,

expressed in the 95th

and 5th

percentiles, are presented in Figure 10.


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Figure 10: WTP GHG Emissions results showing individual parameter uncertainties for the Mass Based,

Energy Based and Economic allocation methods. For each bar, the right extreme represents the 95 th

percentile and the left extreme represents the 5th

percentile. Red lines illustrate mean value of emissions.

Comparing the three allocation methods, the figure shows that uncertainty ranges across the

three methods are mostly consistent. Both energy and economic methods have combined

uncertainty ranges of around 4 g per MJ fuel while the mass method has a range of around 6 g per

MJ fuel. The higher uncertainty range for mass based allocation can be attributed to the

corresponding higher uncertainty range in the Fuel-Coproduct mix parameter; varying the fuel-

coproduct mix results in significant mass uncertainty for the final HRJ fuel. Quantitatively speaking,

uncertainty in most model parameters is consistent across the three allocation methods. Effect of

some parameters on the WTP GHG emissions is dampened in the mass allocation method.

However, the dampening effect is very minor (less than 0.5 g per MJ fuel).

-48 -46 -44 -42 -40 -38 -36

Lipid Content

Moisture Content

Farming Energy

Hydroprocessing Stage- Oil Use

Co Product Naphtha - Propane Mix

Fuel- Co product Mix

Combined Uncertainty

GHG (g per MJ Fuel)

ParameterUncertainty

Mass Based Allocation

-38 -36 -34 -32 -30 -28

Lipid Content

Moisture Content

Farming Energy



Fuel- Co product Mix


GHG (g per MJ Fuel)

Para

meterUncertainty

Energy Based Allocation

-34 -32 -30 -28 -26

Lipid Content

Moisture Content

Farming Energy



Fuel - Co product Mix


GHG (g per MJ Fuel)

Para

meterUncertainty

Economic Allocation


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6.2.1 Parameters contributing to combined uncertainty

From Figure 10, it can be derived that a large portion of the uncertainty in emissions occurs

primarily due to two parameters; fuel-coproduct mix (50-60% share of overall uncertainty) and

lipid content (30-40% share of overall uncertainty). Fuel-coproduct mix is the parameter that

varies the HRJ fuel output from the production process. Two main reasons are behind its significant

effect on the overall uncertainty. Firstly, the data for final process products itself was very broad,

ranging from 54% to 84% output HRJ fuel. This uncertainty in the input parameter value itself has

an effect in the overall output emission value. A secondary reason is the inherent nature of the

parameter that leads to large degree of uncertainty; final percentage of HRJ fuel greatly affects the

overall allocation of emissions. It is interesting to note that although the percentage of HRJ fuel has

a significant impact, the type of coproduct produced has very little impact. As shown by the

Coproduct Naphtha-Propane mix parameter, when the HRJ fuel was fixed and the naptha-propane

mix was varied, it was hardly found to have an effect on uncertainty. This implies that coproducts,

Naphtha and Propane mix have similar impact with regards to GHG emissions.

Pre processing parameters, moisture content and farming energy play a very minor role in the

overall uncertainty. However, as seen by Figure 10, lipid content plays an important role,

responsible for up to 40% share of the overall uncertainty. This is despite the fact that the data input

range for this parameter was relatively narrow (38% to 43%). The parameters significant effect on

uncertainty can be attributed to the critical inherent nature of the parameter. Amount of lipid content

in a Camelina seed plays a crucial role in determining the final oil that can be derived (Zubr 1997).

Hence, even a small increment in the lipid content can have a large effect on the final emissions

attributed to the fuel. More fuel produced would correspond to less emission attribution per MJ of

the fuel.


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6.3 Comparison with Conventional Jet Fuel Emissions

Figure 11 compares the WTP GHG emissions from conventional jet fuel (jet fuel A) with those

from Camelina based HRJ Fuel. For the Camelina fuel, the emissions results are based on default

values in GREET (energy allocation). For the boundaries set in this thesis, Figure 11 shows that

Camelina fuel would clearly bring about significant emissions savings. This is true even when

considering the uncertainty quantified in earlier sections (-46 to -29 g per MJ fuel). The results

obtained for Camelina fuel from the GREET model are robust and regardless of uncertainty have a

better GHG emission performance than conventional jet fuel confirming the results obtained by

other studies (Shonnard 2009).

Figure 11: Chart comparing WTP GHG emissions from conventional jet fuel with WTP emissions fromCamelina based HRJ Fuel

6.4 Implications of Uncertainty

The following sections present implications of the results obtained in this thesis. It should be

kept in mind that the uncertainty results strongly depend on the thesis boundaries. If these change

(i.e., type of coproducts, energy or electricity mix), the resultant outcomes and implications,

including the importance of the different types of uncertainty, might change.

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-30

-20

-10

0

10

Camelina HRJ Fuel Conventional Jet Fuel

WTP GHG Emissions (g per MJ fuel)


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6.4.1 Quantitative Understanding of Uncertainty

When we assess the WTP GHG uncertainty results presented in Figure 12, we can conclude

that uncertainty due to scenario choices is more significant than parameter uncertainty. The

maximum scenario uncertainty amounts up to 12 g per MJ fuel and the maximum parameter

uncertainty to 6 g per MJ fuel.

Figure 12: Uncertainty analysis results obtained in this thesis

To put the quantitative GHG uncertainty results in perspective, a comparison with the US state

of California is done. California has an annual GHG emission of around 250 million tons from

transportation fuels (California Energy Commission 2011). In 2007, efforts to reduce fuel emissions

led to the enactment of the California Low Carbon Fuel Standard focused towards a 10%

reduction in carbon intensity by 2020. This would amount to a 25 million tonnes reduction in GHG

emissions. An effective comparison would be to compare the uncertainty obtained from the results

with the target 25 million tons emission reduction. If the 6 g per MJ fuel parameter uncertainty were

to be scaled up to an annual scale in tones, it would amount to about 10 million tonnes GHG

emissions (California Energy Commission 2011). In terms of the California reduction target, this is

a 4% reduction in emissions. Similarly, a scenario uncertainty of 12 g per MJ fuel would amount to

an 8% reduction in emissions.

At first sight, the value of the uncertainties from this thesis might seem small, especially on

comparison with uncertainty results obtained from other researchers (Malca 2011, Sills et al. 2012).

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-45

-43

-41

-39

-37

-35

-33

-31

-29

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-25

Energy Based

Allocation

Mass Based

Allocation

Economic

Allocation

GHG(gperMJfuel)

Allocation Type


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However, it is clear from the above example they can have a significant effect in overall GHG

emissions. In Californias case, they could add up to almost 80% of the target reduction. Hence it

becomes critical that efforts be spent in reducing uncertainty in life cycle GHG emissions.

6.4.2 Reducing Uncertainty

Figure 9 and Figure 10 show that co-product allocation, lipid content and the amount of fuel

formed (fuel-coproduct mix) are the dominant contributors to uncertainty. The remaining

parameters do not have as significant an impact on the uncertainty in WTP GHG emissions of

Camelina jet fuel. Identifying critical parameters is important for decision makers because it

indicates which variables to act on and, moreover, the parameters that could be neglected, especially

if it is hard to get detailed information about them. It has to be however remembered that this is

exclusively for GHG emissions. Before concrete decisions are made, effect of other environmental

and economic factors would also have to be taken into account.

Parameter uncertainties such as final fuel percentage and lipid content can be reduced by

effective data gathering. Particularly in this study, a large source of uncertainty can be attributed to

lack of data since there was found to be scarcity in data for Camelina HRJ fuel production and

processing. Hence, for reducing parameter uncertainty, efforts can be focused on large data-

gathering for the production and fuel processing phase. The other major source of uncertainty seen

in this study is from coproduct allocation. Reducing this uncertainty is challenging and would have

to be taken on a case by case basis. As discussed earlier, in some cases it might make sense to use a

combination of approaches to measure the sensitivity of results to different allocation methods.

An interesting question would be the degree to which uncertainly in Camelina fuel WTP GHG

emissions can be reduced. In the end it should be kept in mind that biofuel production process are

complex systems and uncertainty in results reflects our limited ability to accurately predict the

behavior of such systems.


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6.4.3 Decision Making

It is clear from the results presented in this thesis that uncertainty in GHG emissions of

Camelina jet fuel can be considerable and could have significant impacts on the results of LCA

studies. An LCA outcome ignoring uncertainty could give rise to decision making devoid of the

holistic view point. For example, a government could draft an environmental policy based on an

LCA result without being aware of the risk involved. Hence, it is important that LCA results,

especially those associated with new and developing technologies such as Camelina biofuel

systems, be reported as ranges of expected values rather than single values to provide decision

makers with reliable results.

Policymakers have traditionally preferred discrete answers rather than characterizing

uncertainty and in some cases decision makers would prefer a single value factor as opposed to

dealing with complicated ranges. In such cases, use of mean emission values is understandable.

However, conducting uncertainty analysis is still important since it relates the probabilities involved

and generates awareness amongst decision makers about the risk involved in their decisions.

6.5 Limitations of Study and Future Work

The uncertainty analysis conducted in this thesis provides useful information to assess the

reliability of Camelina LCA-based decisions and to guide future research toward reducing

uncertainty. However, given that evaluation of uncertainty is relatively new in LCA, efforts need to

be focused on understanding and countering the limitations.

One of the primary limitations of this study is that coproduct allocation using system expansion

could not be carried out. System expansion methodology has gained widespread acceptance in the

LCA field and ISO recommends its usage in LCA studies. However, in this thesis due to GREET

software errors, system expansion methodology could not be used. Future work should be directed


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towards studying the effect of system expansion and comparing the results with other allocation

methodologies.

Another limitation of this thesis is that results are based on limited data. Estimated

uncertainties are a function of data availability and the analysis conducted in this thesis likely

overestimates the total uncertainty. Future work should focus towards increasing volume of

available data during the cultivation and processing stages of the Camelina HRJ fuel. In particular

efforts should be focused towards gathering data on Came

uncertainty in lifecycle ghg emissions from camelina jet fuel

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