chemical kinetic modeling of biofuel combustion · chemical kinetic modeling of biofuel combustion...

Chemical Kinetic Modeling of Biofuel Combustion

by

Subram Maniam Sarathy

A thesis submitted in conformity with the requirementsfor the degree of Doctor of Philosophy

Graduate Department of Chemical Engineering and Applied ChemistryUniversity of Toronto

Copyright c⃝ 2010 by Subram Maniam Sarathy

Abstract

Chemical Kinetic Modeling of Biofuel Combustion

Subram Maniam Sarathy

Doctor of Philosophy

Graduate Department of Chemical Engineering and Applied Chemistry

University of Toronto

2010

Bioalcohols, such as bioethanol and biobutanol, are suitable replacements for gaso-

line, while biodiesel can replace petroleum diesel. Improving biofuel engine performance

requires understanding its fundamental combustion properties and the pathways of com-

bustion. This study’s contribution is experimentally validated chemical kinetic com-

bustion mechanisms for biobutanol and biodiesel. Fundamental combustion data and

chemical kinetic mechanisms are presented and discussed to improve our understanding

of biofuel combustion.

The net environmental impact of biobutanol (i.e., n-butanol) has not been studied

extensively, so this study first assesses the sustainability of n-butanol derived from corn.

The results indicate that technical advances in fuel production are required before com-

mercializing biobutanol. The primary contribution of this research is new experimental

data and a novel chemical kinetic mechanism for n-butanol combustion. The results

indicate that under the given experimental conditions, n-butanol is consumed primarily

via abstraction of hydrogen atoms to produce fuel radical molecules, which subsequently

decompose to smaller hydrocarbon and oxygenated species. The hydroxyl moiety in

n-butanol results in the direct production of the oxygenated species such as butanal,

acetaldehyde, and formaldehyde. The formation of these compounds sequesters carbon

from forming soot precursors, but they may introduce other adverse environmental and

health effects.

Biodiesel is a mixture of long chain fatty acid methyl esters derived from fats and

ii

oils. This research study presents high quality experimental data for one large fatty acid

methyl ester, methyl decanoate, and models its combustion using an improved skeletal

mechanism. The results indicate that methyl decanoate is consumed via abstraction

of hydrogen atoms to produce fuel radicals, which ultimately lead to the production of

alkenes. The ester moiety in methyl decanoate leads to the formation of low molecular

weight oxygenated compounds such as carbon monoxide, formaldehyde, and ketene.

The study concludes that the oxygenated molecules in biofuels follow similar combus-

tion pathways to the hydrocarbons in petroleum fuels. The oxygenated moiety’s ability to

sequester carbon from forming soot precursors is highlighted. However, the direct forma-

tion of oxygenated hydrocarbons warrants further investigation into the environmental

and health impacts of practical biofuel combustion systems.

iii

Dedication

Sabbo pajjalito loko, sabbo loko pakampito

The entire universe is nothing but combustion and vibration

To my forefathers and their wives

Sivarajan the Medical Doctor

Viswanathan the Industrialist

Subramaniam the Entrepreneur

Radhakrishnan the Doctor of Engineering

Gurumoorthy the Renunciate

To all beings

May you be happy and peaceful.

May you enjoy good health and harmony.

iv

Acknowledgements

I offer greatest thanks to my wife, Nimisha Rajawat. I would have never been able to

complete this work without her contributions of patience, compassion, encouragement,

love, and delicious food. Thanks for being their and bringing Panya into my life.

Endless gratitude to Professor Murray Thomson for his guidance and support in my

academic and professional endeavours. Thanks for being a wise guide, a passionate

leader, and a good friend.

Much appreciation to Professor Kirk and Professor Wallace for being members on my

PhD supervisory committee. I also thank Professor Mims and Professor Tran for serving

on my examination committee. It was an honour to have Professor Seshadri from UC

San Diego serving as my external examiner. His insights into my work were invaluable.

Thanks to Professor Phillipe Dagaut, Professor Heather MacLean, Dr. Yimin Zhang,

Dr. William Pitz, Professor Tianfeng Lu, and my other coauthors for their efforts.

Gratefulness to all my colleagues in the Combustion Research Group, specifically Dr.

Qingan Zhang, Dr. Seth Dworkin, Dr. Salvador Rego, Professor Zhenyu Wen, Dr.

Jerome Thiebaud, Richard Mills, Phil Geddis, Parham Zabeti, Meghdad Saffaripour,

Tim Chan, and Coleman Yeung. My contemporary, Tom Tzanetakis, deserves a special

recognition for sharing his knowledge of both practical engineering systems and funda-

mental scientific theory. He is the smartest and most humble engineer that I know.

Acknowledgement to NSERC and AUTO21 for funding my research and studies.

My father, Roger Sarathy, deserves special a acknowledgement for proofreading this

dissertation. Also, thanks for always encouraging me to ask questions and to search for

answers.

Besides my wife, two other women deserve special acknowledegment: my mother, Saraswathi

Sarathy, and her sister (i.e., my aunt), Durga Krishnan. Their regular phone calls and

barrage of internet messages always kept me on my toes. Thanks for being there and

v

making sure I never slacked off.

Much respect to all my family and friends. Words cannot express my gratitude to-

wards each of you, so here is a loving smile :)

Thanks to my Dhamma family at the Ontario Vipassana Centre for supporting my

journey towards fulfilling the ten paramis (i.e., perfect qualities): generosity, moral-

ity, renunciation, wisdom, energy, patience, truthfulness, determination, loving kindness,

and equanimity. The development of these paramis has provided immeasurable benefits

in my research.

vi

Contents

Abstract . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ii

Dedication . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . iv

Acknowledgements . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . v

Table of Contents . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xi

List of Figures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xii

List of Tables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xvi

Acronyms . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xvii

Statement of Co-Authorship and Copyright . . . . . . . . . . . . . . . . . . . xx

I Background and Methods 1

1 Introduction 2

1.1 Research Motivation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3

1.2 Dissertation Objectives and Layout . . . . . . . . . . . . . . . . . . . . . 5

Literature Cited . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7

2 Background 8

2.1 Reciprocating ICEs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9

2.1.1 SI Engines . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10

2.1.2 CI Engines . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10

2.2 Fuel Properties . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10

2.3 Combustion Emissions . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12


3 Modeling Combustion Chemistry 15

3.1 Chemical Kinetics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16

3.2 Computer Simulations for Mechanism Validation . . . . . . . . . . . . . . 16

vii

3.2.1 Governing Equations for Chemically Reacting Flows . . . . . . . . 17

Conservation of Mass . . . . . . . . . . . . . . . . . . . . . . . . . 18

Conservation of Momentum . . . . . . . . . . . . . . . . . . . . . 18

Conservation of Species . . . . . . . . . . . . . . . . . . . . . . . . 19

Conservation of Energy . . . . . . . . . . . . . . . . . . . . . . . . 20

3.3 Solving the Governing Equations . . . . . . . . . . . . . . . . . . . . . . 21

3.3.1 Chemical Kinetic Database . . . . . . . . . . . . . . . . . . . . . 22

3.3.2 Thermochemical Database . . . . . . . . . . . . . . . . . . . . . . 24

3.3.3 Transport Database . . . . . . . . . . . . . . . . . . . . . . . . . . 24

3.4 Developing Chemical Kinetic Mechanisms . . . . . . . . . . . . . . . . . 28

3.4.1 Mechanisms for Hydrocarbon Fuels . . . . . . . . . . . . . . . . . 30

Combustion of Alkanes . . . . . . . . . . . . . . . . . . . . . . . . 30

Combustion of Alkenes . . . . . . . . . . . . . . . . . . . . . . . . 33

Mechanism of Soot Formation . . . . . . . . . . . . . . . . . . . . 34

3.4.2 Determining Rate Coefficients . . . . . . . . . . . . . . . . . . . . 36


4 Experimental Apparatus and Analytical Methodology 43

4.1 Opposed-flow Diffusion Burner Setup . . . . . . . . . . . . . . . . . . . . 44

4.2 Fuel Preparation and Vaporization . . . . . . . . . . . . . . . . . . . . . 45

4.3 Supply of Fuel and Oxidizer Streams . . . . . . . . . . . . . . . . . . . . 50

4.4 Reynold’s Number and Strain Rate Calculations . . . . . . . . . . . . . . 52

4.5 Gas Sampling System . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 53

4.5.1 Sampling Apparatus . . . . . . . . . . . . . . . . . . . . . . . . . 53

4.5.2 Sampling Procedure . . . . . . . . . . . . . . . . . . . . . . . . . 56

4.6 Analytical Techniques . . . . . . . . . . . . . . . . . . . . . . . . . . . . 58

4.6.1 Non-Dispersive Infrared Analysis . . . . . . . . . . . . . . . . . . 58

CO and CO2 Measurements . . . . . . . . . . . . . . . . . . . . . 58

4.6.2 Gas Chromatography . . . . . . . . . . . . . . . . . . . . . . . . . 59

GC Carrier Gas . . . . . . . . . . . . . . . . . . . . . . . . . . . . 60

Injection System . . . . . . . . . . . . . . . . . . . . . . . . . . . 60

GC Measurement Procedures . . . . . . . . . . . . . . . . . . . . 61

GC Calibration Procedure . . . . . . . . . . . . . . . . . . . . . . 63

4.6.3 Temperature Measurement . . . . . . . . . . . . . . . . . . . . . . 66

viii

Correction for Radiation Losses . . . . . . . . . . . . . . . . . . . 68


II Biobutanol 71

5 Background 72

5.1 Biobutanol History . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 73

5.2 Biobutanol Production . . . . . . . . . . . . . . . . . . . . . . . . . . . . 74

5.3 Biobutanol Fuel Properties . . . . . . . . . . . . . . . . . . . . . . . . . . 76


6 LCA of Biobutanol for use in Transportation 80

6.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 80

6.1.1 Bioethanol LCA . . . . . . . . . . . . . . . . . . . . . . . . . . . . 80

Land Use Change and Food Security Issues . . . . . . . . . . . . . 82

6.1.2 Biobutanol LCA . . . . . . . . . . . . . . . . . . . . . . . . . . . 83

6.2 Methodology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 83

6.2.1 Data sources and uncertainties . . . . . . . . . . . . . . . . . . . . 84

6.2.2 Corn ethanol and butanol production . . . . . . . . . . . . . . . . 85

6.2.3 Post Production Life Cycle Activities . . . . . . . . . . . . . . . . 87

6.3 Results and Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . 89

6.3.1 WTP fossil energy use . . . . . . . . . . . . . . . . . . . . . . . . 89

6.3.2 WTP petroleum use . . . . . . . . . . . . . . . . . . . . . . . . . 89

6.3.3 WTP GHG emissions . . . . . . . . . . . . . . . . . . . . . . . . . 90

6.3.4 WTW Fossil Energy Use and GHG Emissions . . . . . . . . . . . 91

6.4 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 92

6.4.1 Recommendations . . . . . . . . . . . . . . . . . . . . . . . . . . . 93


7 Chemical Kinetic Modeling of Butanol Combustion 100

7.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 100

7.1.1 Engine Studies . . . . . . . . . . . . . . . . . . . . . . . . . . . . 100

7.1.2 Combustion Chemistry Studies . . . . . . . . . . . . . . . . . . . 101

7.2 Experimental Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . 102

ix

7.2.1 Opposed-flow Diffusion Flame . . . . . . . . . . . . . . . . . . . . 102

7.2.2 Jet Stirred Reactor . . . . . . . . . . . . . . . . . . . . . . . . . . 103

7.2.3 Laminar Flame Speed Setup . . . . . . . . . . . . . . . . . . . . . 104

7.3 Computational Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . 105

7.3.1 Chemical Kinetic Mechanism . . . . . . . . . . . . . . . . . . . . 106

7.3.2 Thermochemical Data . . . . . . . . . . . . . . . . . . . . . . . . 109

7.3.3 Transport Properties . . . . . . . . . . . . . . . . . . . . . . . . . 109




7.4.3 Laminar Flame Speed . . . . . . . . . . . . . . . . . . . . . . . . 126

7.5 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 127

7.6 Recommendations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 128

Supplemental Material . . . . . . . . . . . . . . . . . . . . . . . . 130


III Biodiesel 136

8 Background 137

Biodiesel Sustainability . . . . . . . . . . . . . . . . . . . . . . . . 138

8.1 Biodiesel Fuel Chemistry . . . . . . . . . . . . . . . . . . . . . . . . . . . 139

8.1.1 Biodiesel Production . . . . . . . . . . . . . . . . . . . . . . . . . 140

8.2 Biodiesel Fuel Properties . . . . . . . . . . . . . . . . . . . . . . . . . . . 142

8.3 Biodiesel Exhaust Emissions . . . . . . . . . . . . . . . . . . . . . . . . . 143

8.3.1 CO, THC, and Oxygenate Emissions . . . . . . . . . . . . . . . . 144

8.3.2 PM and NOx emissions . . . . . . . . . . . . . . . . . . . . . . . . 145


9 Chemical Kinetic Modeling of Biodiesel Combustion 152

9.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 152

9.2 Mechanisms for Short Chain Methyl Esters . . . . . . . . . . . . . . . . . 153

9.3 Mechanisms for Long Chain Methyl Esters . . . . . . . . . . . . . . . . . 156

9.3.1 Mechanisms for Methyl Decanoate . . . . . . . . . . . . . . . . . 158

9.4 Background Summary and Research Motivation . . . . . . . . . . . . . . 159

x

9.5 Experimental Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . 160

9.6 Computational Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . 161

9.6.1 Chemical Kinetic Mechanism . . . . . . . . . . . . . . . . . . . . 161

Modified Detailed Chemical Kinetic Mechanism . . . . . . . . . . 162

Skeletal Chemical Kinetic Mechanism . . . . . . . . . . . . . . . . 167

9.6.2 Thermochemical Data . . . . . . . . . . . . . . . . . . . . . . . . 171

9.6.3 Transport Properties . . . . . . . . . . . . . . . . . . . . . . . . . 171



9.7.2 Temperature, Fuel, and Hydrocarbon Species . . . . . . . . . . . 174

9.7.3 Oxygenated Species . . . . . . . . . . . . . . . . . . . . . . . . . . 178

The Fate of the Ester Moiety . . . . . . . . . . . . . . . . . . . . 182


9.8 Conclusions and Recommendations . . . . . . . . . . . . . . . . . . . . . 185


IV Closing 195

10 Scientific Contribution 196

Appendix A 198

Appendix B 199

Appendix C 239

xi

List of Tables

2.1 Selected characteristics of SI and CI engines . . . . . . . . . . . . . . . . 9

3.1 Relative magnitudes of rate constants for H abstraction from different CH

bonds . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33

4.1 Physical Chemical Properties of the Fuels Used . . . . . . . . . . . . . . 46

4.2 Experimental conditions . . . . . . . . . . . . . . . . . . . . . . . . . . . 51

4.3 Dual Column GC Method Parameters . . . . . . . . . . . . . . . . . . . 63

4.4 GC Method Parameters for Fatty Acid Methyl Esters . . . . . . . . . . . 64

4.5 Measured FID relative molar response factors for organic molecules . . . 65

5.1 Selected fuel properties of butanol, ethanol, and gasoline . . . . . . . . . 77

6.1 Data for estimating energy use and GHG emissions for corn butanol pro-

duction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 88

7.1 Chemical structures of species during the oxidation of n-butanol . . . . . 107

7.2 Comparison of maximum measured and predicted product concentrations

for n-butanol in the JSR . . . . . . . . . . . . . . . . . . . . . . . . . . . 117

7.3 Comparison of maximum measured species in n-butanol (C4H9OH) and

n-butane (C4H10) opposed-flow diffusion flames. . . . . . . . . . . . . . . 121

8.1 Typical fatty acid composition (wt%) of various biodiesel feedstock [17] . 141

8.2 Properties of common biodiesel fuels and pure FAME . . . . . . . . . . . 144

8.3 Percent of publications that report changes in emissions for biodiesel [23]. 145

9.1 Experimentally and Empirically Determined Polarizabilities (A3) for FAME173

9.2 maximum Measured and Predicted Concentration of Oxygenated Species 180

xii

List of Figures

3.1 A typical phase diagram showing critical point . . . . . . . . . . . . . . . 26

3.2 Flowchart for developing and validating chemical kinetic mechanisms . . 29

3.3 Decay of isopropyl radical . . . . . . . . . . . . . . . . . . . . . . . . . . 33

3.4 Addition of O radical to ethene . . . . . . . . . . . . . . . . . . . . . . . 34

3.5 Chemical structures and bond dissociation energies for alcohols and alka-

nes [24] . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 37

3.6 General mechanism for soot formation from Glassman [27] . . . . . . . . 38

4.1 Diagram of burner port . . . . . . . . . . . . . . . . . . . . . . . . . . . . 45

4.2 Photograph of burner setup . . . . . . . . . . . . . . . . . . . . . . . . . 46

4.3 Schematic of the mixing chamber . . . . . . . . . . . . . . . . . . . . . . 49

4.4 Schematic of the machined flange plate. . . . . . . . . . . . . . . . . . . . 49

4.5 Vapor pressure curves for fatty acid methyl esters . . . . . . . . . . . . . 51

4.6 Schematic of microprobe . . . . . . . . . . . . . . . . . . . . . . . . . . . 56

4.7 Schematic of microprobe and burner setup . . . . . . . . . . . . . . . . . 57

4.8 Schematic of dual column GC Setup . . . . . . . . . . . . . . . . . . . . 62

4.9 Schematic of the permeation tube setup . . . . . . . . . . . . . . . . . . 66

4.10 Thermocouple Schematic . . . . . . . . . . . . . . . . . . . . . . . . . . . 67

5.1 Timeline of biobutanol history . . . . . . . . . . . . . . . . . . . . . . . . 73

6.1 A simplified life cycle flowchart for corn-derived butanol and ethanol . . . 85

6.2 WTP fossil energy use . . . . . . . . . . . . . . . . . . . . . . . . . . . . 90

6.3 WTP petroleum energy use . . . . . . . . . . . . . . . . . . . . . . . . . 91

6.4 WTP GHG emissions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 92

6.5 WTW fossil energy use . . . . . . . . . . . . . . . . . . . . . . . . . . . . 93

6.6 WTW GHG emissions . . . . . . . . . . . . . . . . . . . . . . . . . . . . 94

xiii

7.1 Schematic of the laminar flame speed measurement setup . . . . . . . . . 105

7.2 Comparison of the experimental and predicted concentration profiles ob-

tained from the oxidation of n-butanol in a JSR at �=1, P=1013 kPa,

�=0.7 s. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 112


tained from the oxidation of n-butanol in a JSR at �=1, P=101.3 kPa,

�=0.07 s. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 113


tained from the oxidation of n-butanol in a JSR at �=2, P=101.3 kPa,

�=0.07 s. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 114


tained from the oxidation of n-butanol in a JSR at �=0.5, P=101.3 kPa,

�=0.07 s. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 115


tained from the oxidation of n-butanol in a JSR at �=0.25, P=101.3 kPa,

�=0.07 s. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 116

7.7 Reaction pathway diagram for n-butanol oxidation in the JSR at �=0.5,

�=1, �=2, P=101.3 kPa, �=0.07s, and T=1160 K. . . . . . . . . . . . . 119

7.8 Sensitivity of n-butanol concentraion to select reactions in the JSR at

�=1.0, P=101.3 kPa, �=0.07 s, and T=1160 K. . . . . . . . . . . . . . . 120

7.9 Experimental and computed profiles obtained from the oxidation of n-

butanol in an atmospheric opposed-flow flame (5.89% C4H9OH, 42% O2). 122

7.10 Reaction pathway diagram for n-butanol oxidation in the opposed-flow

diffusion flame at T=858 K, T=1170 K, and T=1520 K . . . . . . . . . . 125

7.11 Sensitivity of n-butanol concentration to select reactions in the atmo-

spheric opposed-flow diffusion flame (6% C4H9OH, 42% O2). . . . . . . . 126

7.12 Laminar burning velocities of n-butanol/air mixtures, T=350 K, P=90 kPa.127

8.1 Transesterification Reaction . . . . . . . . . . . . . . . . . . . . . . . . . 142

9.1 Biodiesel FAME and their surrogates . . . . . . . . . . . . . . . . . . . . 153

9.2 Computed profiles obtained from the oxidation of methyl decanoate and

methyl butanoate in a JSR at �=1.0, P=1013.25 kPa, �=1 s, 0.1% fuel

mole fraction. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 155

xiv

9.3 One combustion pathway of methyl butanoate that depicts the fate of the

ester moiety. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 156

9.4 A comparison of the combustion pathways for methyl trans-2-butenoate

(above) and methyl butanoate (below) which lead to unsaturated hydro-

carbons. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 157

9.5 Decomposition of the MD4J radical to 1-octene (C8H16) and the ME2J

radical . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 164

9.6 Decomposition of the MD2J to methyl 2-propenoate (MP2D) and the

C7H15 radical . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 165

9.7 Abstraction of H atoms from the alpha carbon atom by a reactive radical

species (R) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 165

9.8 Abstraction of H atoms from the methoxy carbon atom by a reactive

radical species (R) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 166

9.9 Unimolecular decomposition of MD via scission of C-C bonds in and

around the carbonyl group . . . . . . . . . . . . . . . . . . . . . . . . . . 167

9.10 Experimental (symbols) and computed (lines with symbols) profiles ob-

tained from the oxidation of methyl decanoate, n-decane, and methyl bu-

tanoate in a JSR at �=1.0, P=1013.25 kPa, �=1 s, 0.1% fuel mole fraction.169

9.11 Comparison of MD mechanisms (lines with symbols) and experimental

data (symbols) for RME in a JSR at �=1.0, P=101.325 kPa, �=1.0 s [26]. 170

9.12 Critical pressure (Pc) and critical temperature (Tc) for C2-C10 methyl esters.172

9.13 Experimental and computed profiles obtained from the oxidation of MD

in an atmospheric opposed-flow flame (1.8% MD, 42% O2). . . . . . . . . 176

9.14 Reaction pathway diagram for consumption of the MDMJ radical in the

opposed-flow diffusion flame at T=1040 K. . . . . . . . . . . . . . . . . . 177

9.15 Reaction pathway diagram for consumption of the MD2J radical in the


9.16 Reaction pathway diagram for consumption of the MD4J radical in the


9.17 Reaction pathways for the formation and consumption of methyl 2-propenoate

in the opposed-flow diffusion flame at T=1040 K. . . . . . . . . . . . . . 182

9.18 Reaction pathways leading to the formation of the methoxycarbonyl radi-

cal in the opposed-flow diffusion flame at T=1030 K given the experimental

and modeling conditions of Gail et al. [2]. . . . . . . . . . . . . . . . . . 183

xv

9.19 Comparison of proposed MD skeletal mechanism and experimental data

for RME in a JSR at �=1.0, P=101.325 kPa, �=1.0 s [26]. . . . . . . . . 185

9.20 Comparison of proposed MD skeletal mechanism (lines with symbols) and

experimental data (symbols) for RME in a JSR at �=1.0, P=101.325 kPa,

�=1.0 s [26]. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 186

xvi

Acronyms

ABE acetone-butanol-ethanol

ANL Argonne National Laboratory

BDE bond dissociation energy

BP British Petroleum

Bu85 85% butanol-15% gasoline

CG conventional gasoline

CI compression-ignition

DDGS dried grains with solubles

DFT density function theory

DRG directed relation graph

E85 85% ethanol-15% gasoline

EEI Environmental Energy Inc.

FAME fatty acid methyl esters

FFV flexible fuel vehicle

FID flame ionization detector

FTIR fourier transform infrared spectroscopy

FU functional unit

GC/FID gas chromatograph flame ionization detector

GC Gas chromatography

GDP gross domestic product

GHG greenhouse gases

GREET Greenhouse Gases, Regulated Emissions, and Energy Use in Transportation

xvii

GSV gas sampling valve

GUI graphical user interface

HACA hydrogen abstraction carbon addition

ICEs internal combustion engines

ID inner diameter

JSR jet stirred reactor

LCA life cycle assessment

LDV light duty vehicle

LHV lower heating value

LOD limit of detection

MB methyl butanoate

MC methyl trans-2-butenoate

MD methyl decanoate

NDIR non-dispersive infrared

NOx oxides of nitrogen

NTC negative temperature coefficient

OD outer diameter

Pc critical pressure

PM particulate matter

PTFE polytetrafluoroethylene

PTW pump-to-wheel

RME rape seed oil methyl ester

RVP reid vapour pressure equivalent

xviii

SI spark-ignition

Tb boiling point

Tc critical temperature

TCD thermal conductivity detector

THC total unburnt hydrocarbons

VOC volatile organic compounds

WTP well-to-pump

WTW well-to-wheel

xix

Statement of Co-Authorship and Copyright

The material presented in this thesis benefits from collaborations with a number of re-

searchers. In addition, much of the material has been published in academic journals.

This section details the author’s contribution to each section and the right to reproduce

published material without infringing on any copyrights.

All journal articles published from material in this thesis fall under Elsevier’s copyright

policy1. The policy states that the author has the right to include the article in full or

in part in a thesis without obtaining specific permission from Elsevier.

Butanol LCA Studies

The biobutanol life cycle assessment research presented in Chapter 6 was performed in

collaboration with Professor Murray Thomson, Professor Heather MacLean, Professor

Mike Griffin, Yimin Zhang, and Sylvia Sleep. S.M. Sarathy conceptualized the research

and was the primary contributer towards literature survey, developing modeling assump-

tions, life cycle assessment modeling, and writing the manuscripts. Yimin Zhang also

contributed significantly towards literature survey, developing modeling assumptions, life

cycle assessment modeling, and manuscript revisions. Professor Heather MacLean and

Professor Mike Griffin contributed towards developing modeling assumptions, research

supervision, and manuscript revisions. Sylvia Sleep contributed towards literature sur-

vey. Professor Murray Thomson provided financial support to S.M. Sarathy. The above

co-authors have approved the material published in this dissertation.

This study was presented at the following peer reviewed conference proceedings:

1. S.M. Sarathy, Y. Zhang, W.M. Griffin, M.J. Thomson, and H. Maclean, Life Cy-

cle Assessment of Biobutanol for use in Transportation Applications. 8th World

Congress of Chemical Engineering Conference, Montreal, Canada, 2009.

Butanol Combustion Studies

The butanol combustion research presented in Chapter 7 was performed in collaboration

with Professor Murray Thomson, Professor Philippe Dagaut, Dr. C. Togbe, Professor

Christine Rouselle, and Professor Fabien Halter. S.M. Sarathy’s contribution included

1Available online at http://www.elsevier.com/wps/find/authorsview.authors/copyright

xx

literature survey, developing chemical kinetic mechanisms, simulating the jet stirred re-

actor and opposed-flow diffusion flame, acquiring experimental data in the opposed-flow

diffusion flame, and preparing the manuscripts. Professor Murray Thomson contributed

towards financial support to S.M. Sarathy, research supervision, and manuscript revi-

sions. Professor Philippe Dagaut’s contribution was research supervision, developing

chemical kinetic mechanisms, manuscript revisions, and financial support to C. Togbe

who obtained the jet stirred reactor experimental data. Professor Christine Rouselle and

Professor Fabien Halter contributed the laminar flame speed experimental data and sim-

ulations. The above co-authors have approved the material published in this dissertation.

This study was published in the following journals:

1. S.M. Sarathy, M.J. Thomson, C. Togbe, P. Dagaut, F. Halter, C. Mounaim-

Rousselle. An experimental and kinetic modeling study of n-butanol combustion.

Combustion and Flame, 2009, Vol. 156, 852-864.

2. P. Dagaut, S.M. Sarathy, M.J. Thomson. A Chemical Kinetic Study of n-Butanol

Oxidation at Elevated Pressure in a Jet Stirred Reactor. Proceedings of the Com-

bustion Institute, 2009, Vol. 32, 229-237.

Biodiesel Combustion Studies

The biodiesel combustion research presented in Chapter 9 was performed in collabora-

tion with Professor Murray Thomson, Professor Tianfeng Lu, and Doctor William Pitz.

S.M. Sarathy’s contribution included literature survey, developing the modified detailed

chemical kinetic mechanism, performing computer simulations, acquiring experimental

data in the opposed-flow diffusion flame, and preparing the manuscripts. Professor Mur-

ray Thomson contributed towards financial support to S.M. Sarathy, research supervi-

sion, and manuscript revisions. Dr. William Pitz’s contribution was the original methyl

decanoate chemical kinetic mechanism and additional research supervision. Professor

Tianfeng Lu contributed the algorithm used to reduce the modified detailed chemical

kinetic mechanism developed by S.M. Sarathy. The above co-authors have approved the

material published in this dissertation.

This study has been submitted for publication in the following peer-reviewed confer-

ence proceedings:

xxi

1. S.M. Sarathy, M.J. Thomson, T. Lu, W.J. Pitz. An experimental and kinetic

modeling study of methyl decanoate combustion. Proceedings of the Thirty Third

International Combustion Symposium, 2010, Beijing, China.

2. S.M. Sarathy, M.J. Thomson. Chemical Kinetic Modeling of Biodiesel Combustion.

8th World Congress of Chemical Engineering Conference, Montreal, Canada, 2009.

xxii

Part I

Background and Methods

1

Chapter 1

Introduction

The National Resources Canada 2005 report on energy efficiency trends in Canada [1]

indicates that the transportation sector accounts for 30% of total energy use, second only

to the industrial sector. However, transportation produces the largest share of greenhouse

gases (GHG) because the fuels used in transportation are the most GHG intensive. In

addition, Canada spent nearly $61 billion on transportation fuels, the most of any sector.

The transportation sector includes road, air, rail, and marine vehicles; however, the main

source of energy use and GHG was road vehicles used for moving passengers and freight.

From 1990-2005, transportation energy use and GHG increased by more than 30 % due

to an increase in passenger kilometers driven, the consumer shift from cars to minivans

and light trucks, and the increased use of energy intensive modes for transportation.

Although these statistics are for the Canadian economy, the trends are consistent with

other high gross domestic product (GDP) economies.

Road vehicles are powered by internal combustion engines (ICEs) fueled by either

gasoline or diesel. These petroleum derivatives are inherently expensive, and upon

combustion they release large amounts of GHG and other pollutants. Alternatives to

petroleum derived transportation fuels are attractive due to the increasing demand and

limited supply of conventional fossil fuels. Liquid fuels derived from biomass feedstock

(i.e., biofuels) are attracting interest as transportation fuels because they are renewable,

can be locally produced, are more biodegradable, and may reduce net GHG [2]. The

primary driver for using biofuels in the transportation sector is to displace fossil fuel

use. Reducing harmful emissions to the atmosphere is also imperative for mitigating

global warming and sustaining healthy metropolitan areas for human inhabitation. In

addition, the ability for citizens to locally grow and produce their own fuel minimizes the

2

Chapter 1. Introduction 3

dependence on nonrenewable and foreign energy sources.

Currently, gasoline fuel is displaced with bioethanol, while diesel fuel is displaced

with biodiesel. Bioethanol is an alcohol conventionally produced via fermentation of

agriculturally derived starches and sugars. It is blended with gasoline for use in spark-

ignition (SI) engines with only minor modifications required to the engine and fueling

systems. Biodiesel is defined as a mixture of mono-alkyl esters of long chain fatty acids

derived from vegetable oils or animal fats [3]. Biodiesel can be used in its pure form

or it can be blended with petroleum diesel without major modifications to the existing

compression-ignition (CI) engine and fuel distribution infrastructure. In 2007, biofuels

accounted for over 1.5% of global transport fuels [4] with bioethanol and biodiesel con-

tributing an estimated 47 billion liters and 8 billion liters, respectively [5]. The global

demand for biofuels has tripled since 2000, and strong growth is expected in the near

future due to favourable policies from North American and European governments.

A recent review on biofuels provides a unique perspective on the environmental and

societal impacts of biofuels [6]. The rapid policy-driven growth of biofuel use has led to

serious environmental and food security concerns. Current biofuel technologies compete

with the food industry for feedstock, and the diversion of corn, rice, and oilseeds to biofuel

production is cited as the cause of rising food prices and global food shortages. In addi-

tion, large amounts of forest land are being destroyed for biofuel feedstock production,

leading to a loss in biodiversity and carbon-rich sinks. The competition for fresh water

resources presents an additional barrier towards widespread biofuel use. Despite these

challenges, advances in biofuel feedstock and production technologies can ameliorate the

negative impacts of biofuels. Along with environmental stewardship, energy conserva-

tion, efficiency improvements, and other renewable energy technologies (e.g., solar, wind,

geothermal, etc.), biofuels can safely be part of a diverse energy portfolio that reduces

fossil fuel consumption.

1.1 Research Motivation

The rapid increase in biofuel use has sparked an equally rapid growth in research on

biofuel sustainability assessment and combustion properties. Biofuels can either benefit

or harm the environment, so sustainability assessment research attempts to determine the

net environmental impacts associated with biofuel production and use. The most widely

used tool for sustainability assessment is the life cycle assessment (LCA) methodology,


which determines the biofuel’s environmental performance based on a set of user-defined

metrics, such as fossil energy input and GHG output. Biofuel combustion research studies

the fundamental combustion properties in order to improve vehicle performance and

minimize harmful emissions. One important combustion tool is the chemical kinetic

mechanism, which describes the molecular level transformation of reactants (i.e., fuel

and air) into products via a series of elementary steps. These mechanisms can be used to

predict ignition properties, heat release rates, amounts of emissions, and the types and

levels of intermediate species [7] in any combustion system.

The broad research questions that this dissertation addresses are:

∙ Do biofuels offer better environmental performance than fossil fuels?

∙ How does biofuel combustion differ from fossil fuel combustion?

LCAs and chemical kinetic mechanisms of bioethanol fuel are already under intense

research, so such work is beyond the scope of this dissertation. Recently, biobutanol has

attracted attention with British Petroleum and DuPont announcing they would begin

selling sugar beet derived butanol as a gasoline blending component in the United King-

dom [8]. This announcement, in combination with reported research and development

advances in biobutanol production, and cited fuel property advantages of biobutanol

compared to bioethanol, suggest that sustainability assessment and combustion research

on biobutanol is warranted.

Biodiesel sustainability assessment research is already well established, but funda-

mental combustion research is limited due to biodiesel’s complex composition. Biodiesel

is typically comprised of a mixture of saturated and unsaturated fatty acid alkyl esters

(i.e., fatty acid methyl esters (FAME)1) with chain lengths ranging from 12 to 18 carbon

atoms. Developing chemical kinetic models for biodiesel has been challenging due to the

large size of the fatty acid alkyl esters found in practical fuels. The added complexity

of varying chain length and degrees of unsaturation has led to the use of surrogate fuels

of well characterized composition for chemical kinetic modeling. Well validated chemical

kinetic mechanisms exist for short chain FAME surrogates; however, there are few vali-

dated chemical kinetic mechanisms for long chain FAME surrogates, so this dissertation

fills the void.

1Biodiesel can also be comprised of ethyl esters, but this study is only concerned with methyl esterssince these constitute the majority of biodiesel in production today.


1.2 Dissertation Objectives and Layout

The primary focus of this dissertation is to research the combustion kinetics of biobutanol

and FAME. The study proceeds by performing fundamental combustion experiments and

then using the experimental data to validate chemical kinetic mechanisms for the biofuels.

In addition, since biobutanol is a new biofuel that has not been critically assessed for

sustainability, this dissertation also performs an LCA of biobutanol.

This dissertation is divided in three parts. Part I includes this introduction, as well

as background material relevant to both biobutanol and biodiesel combustion chemistry.

Part II contains all research material related to biobutanol, which includes an LCA of

biobutanol and an experimentally validated chemical kinetic mechanism. Part III is

dedicated to research on biodiesel, which focuses on creating an experimentally validated

chemical kinetic mechanism for long chain FAME. Below is a specific list of objectives

for each part of this dissertation.

Part I Background and Methods

Chapter 1 Present the research motivation and objectives of this dissertation

Chapter 2 Provide a background on combustion chemistry in practical applications

Chapter 3 Discuss the modeling of combustion chemistry

Chapter 4 Describe the experimental methods used for validating chemical kinetic

mechanisms

Part II Biobutanol

Chapter 5 Provide background information related to biobutanol

Chapter 6 Present the LCA of biobutanol

Chapter 7 Present the validated chemical kinetic mechanism for biobutanol combustion

Part III Biodiesel

Chapter 8 Provide background information related biodiesel

Chapter 9 Present the validated chemical kinetic mechanism for biodiesel combustion


Part IV Closing

Chapter 10 Summarizes the contributions of this dissertation

Literature Cited

[1] NRCAN, “Energy efficiency trends in canada, 1990 to 2005,” Natural Resources

Canada Office of Energy Efficiency, Tech. Rep., 2009.

[2] A. Demirbas, “Importance of biodiesel as transportation fuel,” Energy Policy, vol. 35,

no. 9, pp. 4661–4670, September 2007.

[3] ASTM, “ASTM D 6751 — specification for biodiesel fuel blend stock (B100) for

middle distillate fuels,” in ASTM Book of Standards. ASTM, 2003.

[4] R. Sims, M. Taylor, J. Saddler, and W. Mabee, “From 1st to 2nd generation biofuel

technologies - an overview of current industry and RD&D activities,” International

Energy Agency and IEA Bioenergy, Tech. Rep., 2008.

[5] REN21, “Renewables 2007 global status report,” Renewable Energy Policy Network

for the 21st Century, Tech. Rep., 2008.

[6] L. P. Koh and J. Ghazoul, “Biofuels, biodiversity, and people: Understanding the

conflicts and finding opportunities,” Biological Conservation, vol. 141, no. 10, pp.

2450–2460, 2008.

[7] C. K. Westbrook, W. J. Pitz, P. R. Westmoreland, F. L. Dryer, M. Chaos, P. Osswald,

K. Kohse-Hoeinghaus, T. A. Cool, J. Wang, B. Yang, N. Hansen, and T. Kasper, “A

detailed chemical kinetic reaction mechanism for oxidation of four small alkyl esters

in laminar premixed flames,” Proceedings of the Combustion Institute, vol. 32, no.

Part 1, pp. 221–228, 2009.

[8] G. Hess, “BP and DuPont to make biobutanol,” Chemical & Engineering News,

vol. 84, pp. 9–10, 2006.

7

Chapter 2

Background

Since the late 1800s when Otto invented the SI engine and Diesel invented the CI engine,

ICEs have become a leading source of stationary and mobile power. ICEs convert the

fuel’s chemical energy into mechanical energy via oxidation (i.e., combustion) within the

engine. Therefore, the reactant fuel-air mixture and the combustion products are the

working fluids of the engine. The difference between the SI and CI engine lies in the

method by which the fuel-air mixture is introduced to the combustion chamber and ig-

nited. The method of ignition determines the key engine characteristics, including the fuel

requirements, operating temperatures and pressures, emission formation mechanisms,

and performance and efficiency [1]. Table 2.1 summarizes several unique characteristics

of SI and CI engines.

The vast majority of ICEs in mobile power applications burn petroleum derived liquid

hydrocarbon fuels. However, early pioneers in the auto industry envisioned the use of

liquid fuels derived from biomass; Henry Ford’s Model T was designed to run on ethanol

while Rudolf Diesel operated his CI engine on peanut oil. This chapter begins with a

brief background of SI and CI engines, fuel properties, and combustion emissions, so that

the reader understands the importance of studying the combustion chemistry of biofuels1.

The following chapters then focuses on combustion kinetics, including its importance, the

development of kinetic mechanisms, and the use of computer simulations as a modeling

tool.

1A thorough description of engine fundamentals is available in Heywood’s text “Internal CombustionEngine Fundamentals” [1].

8

Chapter 2. Background 9

Table 2.1: Selected characteristics of SI and CI engines

SI CI

Ignition Mode spark ignition compression ignition

Combustion Mode premixed nonpremixed

turbulent flame turbulent diffusion flame

Petroleum Fuel gasoline diesel

Biofuel alcohols fatty acid alkyl esters

Compression Ratio 8 to 12 12 to 24

2.1 Reciprocating ICEs

In reciprocating ICEs, the piston moves cyclically up and down in the cylinder chamber

to produce work. The ratio of the maximum cylinder volume (i.e., when the piston is

at the bottom of its stroke) to the minimum cylinder volume is called the compression

ratio. Typically, to generate one power stroke, the piston goes through a four-stroke cycle

which consists of the following [1]:

1. The intake stroke draws fresh mixture into the cylinder chamber by opening the

intake valve and moving the piston from the top of the cylinder to the bottom.

2. During the compression stroke the intake valve closes and the piston moves back

towards the top of the cylinder, compressing the cylinder mixture. Combustion

begins near the end of the compression stroke causing a rapid pressure rise.

3. The expansion stroke occurs as the rapid pressure rise in the cylinder forces the

piston downwards to the bottom of the cylinder chamber. The work generated dur-

ing expansion, which is five times greater than the work used during compression,

turns a crank shaft that delivers power to the vehicle’s wheels.

4. Finally, the exhaust stroke pushes the burned gases out of the cylinder by moving

the piston upwards and opening the exhaust valve. When the piston reaches the

top of the cylinder chamber, the exhaust valve closes and the intake valve opens,

and the four-stroke cycle is reciprocated.


2.1.1 SI Engines

SI engines are characterized by air and fuel being premixed prior to entering the cylinder

chamber through the intake valve. Air-to-fuel ratios in SI engines are typically near

stoichiometric. As the piston moves upwards and the cylinder volume decreases, the

premixed cylinder gas is compressed to 0.8-1.4 MPa (8-14 atm). A typical compression

ratio in SI engines is 8 to 12, which results in less work per stroke when compared to CI

engines [1]. Lower compression ratios are required in SI engines to minimize auto-ignition

(i.e., knocking) of the air-fuel mixture during the compression stroke. Near the top of the

compression stroke, a spark plug ignites the cylinder gases and propagates a turbulent

flame through the cylinder chamber. The rapid pressure and temperature rise forces the

piston downwards through the expansion stroke. Finally, the exhaust stroke forces the

burned gases out of the cylinder and the process is repeated.

2.1.2 CI Engines

In CI engines, only air enters the cylinder during the intake stroke. The cylinder air is

then compressed to approximately 4 MPa (40 atm) and 800 K. A typical compression

ratio in CI engines is 12 to 24, allowing for a greater amount of work to be done during

each cycle [1]. Near the end of the compression stroke, fuel is injected directly into the

cylinder chamber. The liquid fuel jet impinges upon the hot cylinder air and begins to

vaporize. Small pockets of premixed fuel and air then auto-ignite creating additional

heat and radicals sufficient to generate a diffusion flame which consumes the remaining

liquid fuel jet. The exhaust valve opens near the end of the expansion stroke and the

burned gases are exhausted as the piston moves back upwards to its starting position.

2.2 Fuel Properties

The nature of the ignition process in SI and CI engines determines each engine’s fuel

requirements. In SI engines, ignition is initiated by a spark and it is important for the

fuel-air mixture to avoid autoignition. However in CI engines, fuel autoignition is desired

in order to initiate the combustion process during the expansion stroke. ASTM standards

for petroleum derived gasoline [2] and diesel [3] provide specifications for fuel properties

such as density, viscosity, volatility, autoignition characteristics, composition, stability,

and seasonal performance. It is important for biofuels to have properties similar to their


hydrocarbon counterparts, so that major changes are not required to the fueling system

and engine.

A fuel’s ability to resist autoignition in SI engines is known as its antiknocking ten-

dency, and it is quantified by its octane number. The octane number is based on the

fuel’s knocking tendency relative to n-heptane (octane number 0) and iso-octane (2,2,4-

trimethylpentane, octane number 100) [1]. Higher octane numbers indicate a greater

resistance to knock, and are therefore desirable. The octane number of a hydrocarbon

fuel decreases with increasing chain length. Branched hydrocarbons have higher octane

numbers than straight hydrocarbons of the same carbon number because the length of

the basic chain is reduced. SI engine fuels (i.e., gasoline) have octane ratings ranging

from 87 to 105, and mainly consist of straight and branched hydrocarbons of less than

12 carbon atoms with a smaller amount of aromatic species. The addition of oxygenated

compounds, such as ethanol, to hydrocarbon fuel increases the octane number because

oxygenates have greater antiknocking tendencies.

A fuel’s ability to autoignite in CI engines is measured by its cetane number, and it

is inversely related to octane number. The cetane number is based on the fuel’s ability

to autoignite relative to n-hexadecane (cetane number 100) and 1-methylnaphthalene

(cetane number 0). For hydrocarbon fuels, the cetane number increases with increasing

chain length and decreases with branching and cyclication. CI engine fuels (i.e., diesel)

have cetane ratings ranging from 40 to 55 [1], and consist of straight chain hydrocarbons

between 10 and 20 carbon atoms in length with lesser amounts of branched and aromatic

hydrocarbons. Alcohols have low cetane numbers, thereby making them difficult to use

in CI engines. Fatty acid methyl ester species like those found in biodiesel have high

cetane numbers, making them suitable for CI engines.

Besides octane/cetane rating, another important fuel property in SI and CI engines

is the amount of energy per unit of volume. Fuels are sold on a volumetric basis, but

it is energy that powers a vehicle. The energy and volume of a fuel are related through

the density (i.e., kg/L) and the lower heating value (LHV) (i.e., MJ/kg). The LHV is

defined as the amount of energy (i.e., MJ) released during the combustion of a specified

mass of fuel (i.e., kg) at 25 ∘C and returning the combustion products to 25∘C, and then

subtracting the latent heat of vaporization of the water vapor formed during combustion.

Thus, the LHV assumes that water in the combustion products is in the vapor state,

and energy is not recovered by condensing it out of the combustion gas. The volumetric

heating value (i.e., MJ/L) is obtained by multiplying the fuel’s LHV by the fuel’s density,


and it is this value that is important for consumers. Fuels with a larger volumetric heating

value increase fuel economy (i.e., L/100 km) because the amount of energy per unit of

fuel volume is greater. Biofuels tend to have smaller volumetric heating values than their

hydrocarbon counterparts; therefore, it is expected that the fuel economy of a vehicle

powered by biofuel is lower than when powered by petroleum. It should be noted that

some studies have reported an improvement in engine efficiency when using biofuels,

which offsets the lower volumetric heating value, and results in no net change in fuel

economy [4].

2.3 Combustion Emissions

ICEs are a major source of air pollutants and GHG emissions. These emissions have

short-term and long-term health effects on humans, which include irritation of the eyes

and respiratory tract, severe respiratory illnesses, heart disorders, and cancer. Environ-

mental effects include global warming caused by GHG emissions, ozone layer depletion,

acidification, and urban smog formation [5].

Both SI and CI engines emit oxides of nitrogen (NOx), carbon monoxide (CO), par-

ticulate matter (PM), and total unburnt hydrocarbons (THC). These emissions are

regulated by government agencies, so vehicles must meet stringent emission standards.

Combustion in SI engines creates low levels of PM, high levels of CO, THC, and NOx,

but the use of three-way catalytic converters greatly reduces the tail-pipe emissions of

these compounds. CI engine combustion generally produces lower levels of THC and

CO, comparable levels of NOx, and higher levels of PM. Until recently, tail-pipe emis-

sions of CO and THC were reduced using two-way catalytic converters, and little exhaust

treatment was conducted to reduce NOx and PM emissions. However, new environmen-

tal regulations are introducing the use of NOx traps and diesel particulate filters for CI

engines.

Typically, biofuels produce similar amounts of regulated emissions as hydrocarbon

fuels, but there is one notable difference. The use of oxygenated fuels (e.g., biofuels)

has been shown to be an effective way of reducing soot emissions in diesel engines [5, 6].

Oxygenated fuels reduce soot formation by i. sequestering carbon atoms from forming

soot by creating carbon-oxygen bonds, and ii. reducing the aromatic content compared


to petroleum fuels2 . A recent study by Pepiots-Desjardins et al. [7] studied the sooting

tendency of various oxygenated (e.g., alcohols, esters, aldehydes, etc.) and hydrocarbon

compounds, and concluded that oxygenated fuels have soot reducing efficiencies that are

directly related to nature of the functional group.

In addition to the regulated emissions mentioned above, there are several unregulated

emissions that may be of concern. Carbon dioxide is a major cause of global warming

and its emission is currently not regulated by government agencies. Oxygenate emissions,

such as aldehydes and ketones, may become more important when using oxygenated fuels

because the fuel bound oxygen could lead to direct formation of oxygenated compounds.

A study by Jacobson [8] concluded that widespread ethanol use may increase the risk of

cancer and ozone-related illness due to higher aldehyde emissions and increased unburnt

ethanol emissions, which break down to acetaldehyde in the atmosphere. It is uncertain

whether or not biodiesel leads to higher oxygenate emissions since engine studies have

shown both increases and decreases when compared to diesel [4]. Combustion chem-

istry studies of biofuels can help determine the importance of oxygenated emissions by

elucidating the role of fuel bound oxygen during combustion.

The composition of the pollutant gases in the cylinder chamber at the end of the ex-

pansion process varies depending on the engine operating parameters. The concentration

of the pollutants can be calculated assuming chemical equilibrium, as described in the

next section, but these values tend to differ greatly than measured values. This discrep-

ancy is because the combustion products cool rapidly during the expansion process, and

the chemical reactions controlling pollutant formation become rate limited (i.e., they

cannot achieve equilibrium). The pollutant concentrations are essentially “frozen” at

their higher temperature values. Detailed chemical mechanisms and their corresponding

kinetic parameters are required for an accurate calculation of pollutant concentrations

[1]. Therefore, combustion chemistry studies, such as those presented in this thesis, are

required to design engines that curtail pollutant emissions.

2Aromatic hydrocarbons lead directly to soot formation.

Literature Cited

[1] J. Heywood, Internal Combustion Engine Fundamentals. McGraw-Hill Book Com-

pany, New York., 1988.

[2] ASTM, “ASTM D 4814 — standard specification for automotive spark-ignition engine

fuel,” in ASTM Book of Standards. ASTM, 2003.

[3] ASTM, “ASTM D 975 standard specification for diesel fuel oils,” in ASTM Book of

Standards. ASTM, 2003.

[4] M. Lapuerta, O. Armas, and J. Rodriguez-Fernandez, “Effect of biodiesel fuels on

diesel engine emissions,” Progress in Energy and Combustion Science, vol. 34, no. 2,

pp. 198–223, April 2008.

[5] A. Agarwal, “Biofuels (alcohols and biodiesel) applications as fuels for internal com-

bustion engines,” Progress in Energy and Combustion Science, vol. 33, no. 3, pp.

233–271, June 2007.

[6] J. Song, K. Cheenkachorn, J. Wang, J. Perez, A. L. Boehman, P. J. Young, and F. J.

Waller, “Effect of oxygenated fuel on combustion and emissions in a light-duty turbo

diesel engine,” Energy & Fuels, vol. 16, no. 2, pp. 294 – 301, 2002.

[7] P. Pepiot-Desjardins, H. Pitsch, R. Malhotra, S. Kirby, and A. Boehman, “Structural

group analysis for soot reduction tendency of oxygenated fuels,” Combustion and

Flame, vol. 154, pp. 191–208, 2008.

[8] M. Z. Jacobson, “Effects of ethanol E85 versus gasoline vehicles on cancer and mor-

tality in the united states,” Environmental Science and Technology, vol. 41, no. 11,

pp. 4150–4157, June 2007.

14

Chapter 3

Modeling Combustion Chemistry

Combustion in ICEs is a complex process involving fuel atomization, vaporization, fuel-air

mixing, ignition, and combustion. For example, in a CI engine liquid fuel is injected as a

high velocity spray into the combustion chamber, where it vaporizes upon impingement

with high-temperature high-pressure cylinder gases. Low temperature reactions then

spontaneously ignite portions of premixed fuel and air causing rapid heat release. The

remaining fuel spray is then consumed in a high temperature diffusion flame, and burned

gases are produced through the entire expansion process. This unsteady, heterogeneous,

3-dimensional process is challenging to model, and it is difficult to decouple mixing

processes from chemical kinetic processes [1].

Computer simulations based on the KIVA code [2] are capable of combining fluid

dynamics, spray dynamics, chemically reacting flows, and heat and mass transfer in

an engine cylinder to predict ignition behavior, pollutant formation, energy release, and

other features of engine operation. Such codes are widely used in the automobile industry

to increase fuel economy and reduce emissions. Typically, these engine simulations are

computationally expensive, so simplifications to fluid dynamics, spray dynamics, and

elementary chemical kinetics are required. However, reducing the chemical kinetic model

(i.e., mechanism) reduces chemical fidelity and limits our ability to fully understand

combustion chemistry. This chapter describes how detailed chemistry is modeled in

idealized combustion systems, so that the effects of molecular structure on combustion

and emissions can be understood.

15

Chapter 3. Modeling Combustion Chemistry 16

3.1 Chemical Kinetics

Many processes in the engine, including reactions in the flame zone which determine heat

release, reactions controlling ignition, and air pollutant formation mechanisms, occur

at times when temperature and pressure are changing rapidly. These nonequilibrium

processes depend on the rate of each individual chemical reaction (i.e., reaction kinetics),

which are governed by the temperature and the concentration of reactants.

The rates at which reactant species are consumed and product species are produced in

a kinetically controlled process is governed by the law of mass action. For the elementary

reaction in Equation 3.1, the law of mass action states that the rate at which reactants

are consumed is proportional to the product of concentration of each reactant raised to its

stoichiometric coefficient, as shown in Equation 3.2. The forward reaction rate constant

(kf ) shown in the equation follows the Arrhenius form and is further discussed in a later

section.

aA+ bB = cC + dD (3.1)

−dAdt

= kf [A]a[B]b (3.2)

A comprehensive list of chemical reactions and their rates (i.e., a chemical kinetic

mechanism) is required to accurately predict the rate of energy release, soot and pollu-

tant formation, ignition behaviour, knocking limits, and cool flame characteristics [3, 4, 5].

Rather than attempting to validate the detailed kinetic model in an engine, a better op-

tion is to study combustion chemistry and flame structure in idealized chemically react-

ing flow systems (e.g., an opposed-flow diffusion flame) [6]. The combustion phenomenon

observed in the laboratory experiment can then be used to validate a chemical kinetic

mechanism and understand combustion performance in an engine. Furthermore, com-

prehensive chemical kinetic mechanisms validated against a wide range of experimental

data provide the foundation for the reduced mechanisms used in engine simulations [7].

3.2 Computer Simulations for Mechanism Validation

Chemical kinetic mechanisms describe the molecular level transformation of reactants

(i.e., fuel and air) into products via a series of elementary steps. The mechanism valida-

tion first requires a model describing the geometry and operating regime of the specific


combustion application. A large number of differential equations describing the mass,

momentum, energy, and species concentration are numerically integrated to generate

concentration profiles for reactants, intermediates, and products [8]. The computed pro-

files are then validated against experimental data from one or more well-characterized

combustion apparatuses.

A chemical kinetic mechanism is typically validated against an idealized chemically

reacting flow system. The experimental setups modeled in the present study included

opposed-flow diffusion flames, jet stirred reactors, and premixed laminar flames (i.e.,

laminar flame speed). This section describes the governing equations used for modeling

chemically reacting flow systems. Numerical modeling is not the focus of this dissertation

study, so a complete derivation of equations for the various combustion systems (e.g.,

opposed-flow diffusion flames, jet stirred reactors, and premixed laminar flames) and their

solution methodology is not presented herein. The reader is directed to the “CHEMKIN

Theory Manual” for further elaboration on the specific computer codes used for numerical

modeling [9].

3.2.1 Governing Equations for Chemically Reacting Flows

Chemically reacting flow problems are mathematically formulated using equations for

conservation of mass, momentum, energy, and concentration of chemical species, along

with thermodynamic relationships [5, 6, 10]. The chemical kinetic mechanism couples

chemical species concentrations with the energy equation via the enthalpy of reaction.

A set of ordinary differential equations for species and energy, with time as the indepen-

dent variable, make up the conservations equations for problems where spatial transport

is negligible (e.g., plug flow reactors, perfectly stirred reactors, etc.). When transport pro-

cesses are important (e.g., laminar flames), the conservation equations becomes a partial

differential equations, with time and space as the independent variables. The compu-

tational cost for kinetically controlled problems is small, but when transport processes

are included the computational load increases dramatically [5]. The following sections

discuss the governing equations for modeling chemically reacting flow systems comprised

of laminar gaseous flows.


Conservation of Mass

Mass is always conserved in a system, and therefore, in a steady state process the rate at

which mass enters a differential element (i.e., a point in space) is equal to the rate at which

it leaves the element. In fluid mechanics, the conservation of mass is mathematically

formulated using the continuity equation shown in Equation 3.3.

∂�

∂t+∇ ⋅ (�v) = 0 (3.3)

where

� is the fluid density

t is the time

v is the fluid velocity vector

∇() is the divergence operator

The equation indicates that the rate of change in mass with respect to time in a

differential element, ∂�∂t

, plus the net mass flow into and out of that element, ∇(�V), is

zero.

Conservation of Momentum

The conservation of momentum (i.e., Navier-Stokes equations) together with the conti-

nuity equation for conservation of mass are the fundamental formulations in fluid me-

chanics. The general differential form of the Navier-Stokes equations where gravity is the

only acting force is shown in Equation 3.4.

�∂v∂t

+�v ⋅ ∇v = fsurface + fbody = ∇¯� −∇p+ �g (3.4)

where


t is the time


p is the pressure representing a surface force, fsurface

¯� is the viscous stress tensor representing a surface force, fsurface

g is the gravitational force constant representing a body force, fbody


The equation states that the change in momentum with respect to time in a differential

element, �∂v∂t

, plus the contribution of convection on momentum, �V ⋅∇v, is equal to the

contribution of viscous stress on momentum, ∇¯� , minus the contribution of pressure on

momentum, ∇p, plus the contribution of gravitational forces on momentum, �g.

Conservation of Species

The continuity equation presented above defines mass conservation in a fluid flow, but it

does not provide any distinction on the chemical species present in the flow. However, the

mass conservation of individual species is important in chemically reacting flow systems

consisting of a multicomponent gaseous mixture. The mass fraction of an individual

species is shown in Equation 3.5.

Yk =�k�

(3.5)

where

Yk is the mass fraction of the ktℎ species, andK∑k=1

Yk = 1

� is the total fluid density

�k is mass density of the ktℎ species, andK∑k=1

�k = �

The chemical composition of a gaseous mixtures in a differential element can be

derived from species mass conservation equations. The mass conservation of a the ktℎ

species in an element is altered by homogeneous chemical reactions, molecular diffusion,

and convection, as shown in Equations 3.6

�∂Yk∂t

+ �v ⋅ ∇Yk = !kWk −∇Jk (3.6)

where


t is the time


Yk is the mass fraction of the ktℎ species

Jk is the diffusive mass flux vector of the ktℎ species

!k is the net molar production rate of the ktℎ species

Wk is the molecular weight of the ktℎ species


The equation states that the change in concentration (i.e., mass fraction) of the ktℎ

species with respect to time in a differential element, �∂Yk∂t

, plus the contribution of

convection on concentration, �v ⋅∇Yk, is equal to the contributions of chemical reactions

on concentration, !Wk, minus the contribution of molecular diffusivity on concentration,

∇Jk.The diffusivity (i.e., diffusion mass flux) of a species, jk, can be described using Fick’s

law as shown in Equation 3.7. The theory states that the diffusivity depends linearly on

the negative concentration gradient multiplied by the binary diffusion coefficient, Dk.

Jk = −� YkXk

Dk∇Xk = −�Wk

WDk∇Xk (3.7)

where

Jk is the diffusive mass flux vector of the ktℎ species



Xk is the mole fraction of the ktℎ species

Dk is the binary diffusion coefficient


W is the mean molecular weight of the mixture

Conservation of Energy

Thermal energy is conserved in chemically reacting flow systems, and the energy equation

is used as the basis for such systems. The energy equation is used to describe the

temperature profile of a chemically reacting flow, which affects processes such as chemical

reaction, convection, and molecular diffusion. The thermal energy equation, shown in

Equation 3.8, stems from the first law of thermodynamics, and assumes ideal gases, low

Mach numbers. and Fourier’s law for heat conduction.

�cp∂T

∂t+ �cpvk ⋅ ∇T = ∇ ⋅ (�∇T )− �

K∑k=1

cp,kYkvk ⋅ ∇T −K∑k=1

ℎk!kWk + qrad (3.8)

where

cp is the constant pressure heat capacity of the ktℎ species



t is time

T is the temperature

� is the thermal conductivity


vk is the fluid velocity vector of the ktℎ species

!k is the net molar production rate of the ktℎ species

ℎk is the enthalpy of formation of the ktℎ species


qrad is the radiative heat transfer

The equation states that the change in thermal energy with respect to time in a

differential element, �cp∂T∂t

, plus the thermal energy convected to the element by the

temperature gradient, �cpv ⋅ ∇T , is equal to the contribution of thermal heat conduc-

tion (i.e., Fourier’s law) on thermal energy, ∇(�∇T ), minus the contribution of thermal

diffusivity on thermal energy, �K∑k=1

cp,kYkvk ⋅ ∇T , minus the contribution of heat from

chemical reaction on thermal energy,K∑k=1

ℎk!kWk, plus the contribution of radiative heat

transfer on the the element, qrad.

3.3 Solving the Governing Equations

The governing equations can be solved using a numerical solver that evaluates the chemi-

cal kinetic, thermodynamic, and transport properties in each differential element as time

proceeds. This study models chemically reacting flow systems using the CHEMKIN

software package [11]. CHEMKIN provides modeling of a wide range of combustion ap-

paratuses, including shock tubes, premixed flames, diffusion flames, and partially and

perfectly stirred reactors. Chemical kinetic mechanisms are coupled with thermochem-

ical data for all the species in the mechanism to calculate forward and reverse reaction

rates. Transport properties for the species are also included when attempting to model a

combustion process in which transport processes are rate-controlling (e.g., opposed-flow

diffusion flames).

The combustion setups were modeled using the CHEMKIN 4.1 software package. The

first step used the CHEMKIN 4.1 graphical user interface (GUI) to set up a diagram of

the experimental apparatus, including all reactant and product streams. The next step


was to generate the linking files for the numerical code. This required using the pre-

processors to access three important information files: i.) the chemical kinetic database;

ii.) the thermodynamic database; and iii.) the transport database. More information

on these files is given below. The “CHEMKIN Gas-Phase Interpreter” reads the first

two files and generates the “CHEMKIN Linking File”. The third file is used by the

“TRANSPORT Preprocessor” to generate the “Transport Linking File” when modeling

systems where transport processes are important.

Next, the characteristics of the chemically reacting flow system and inlet flows were

input. This includes the velocity of each inlet stream, initial concentrations, pressure,

physical configuration, temperature, and a number of solution method options. The

model was run, and the numerical simulation output a text file containing the solution.

The “Solution Export Utility” was used to convert this text file into a comma separated

values file format that was readable by Microsoft Excel.

This section discuss the the three input files required by CHEMKIN for solving the

chemically reaction flow system problem. The development of these input files is the

primary focus of this dissertation, so a thorough background is presented for the reader

to appreciate this study’s contributions.

3.3.1 Chemical Kinetic Database

The chemical kinetic database identifies all the gaseous species present, and it provides

a user-defined chemical kinetic mechanism for the production and consumption of these

species. The chemical kinetic mechanism details each reaction taking place and the ap-

propriate reaction rate parameters in the modified Arrhenius form, as shown in Equation

3.12. The gas phase kinetic file conforms to the CHEMKIN input format. Additional

information of the development of chemical kinetic mechanisms is provided in Section

3.4.

The chemical source term, !k, describes the net molar production rate of the ktℎ

species, and it appears in Equations 3.6 and 3.8. The chemical kinetic mechanism con-

tains the information needed to evaluate !k. The chemical system consisting of N species

and M reversible reactions can be expressed as

N∑k=1

� ′nk[Xk] ⇀↽N∑k=1

� ′′nk[Xk] (3.9)

where


� ′nk is the stoichiometric coefficient of the ktℎ reactant species in the ntℎ

reaction

� ′′nk is the stoichiometric coefficient of the ktℎ product species in the ntℎ

reaction

[Xk] is the molar concentration of the ktℎ species

The molar production of the ktℎ species, !k, is expressed as

!k =M∑k=1

(� ′′nk − � ′nk)�n (3.10)

where

�n is the progress variable of the ntℎ reaction, given in Equation 3.11

�n = kfn

N∏k=1

[Xk]�′nk − krn

N∏k=1

[Xk]�′′nk (3.11)

Each ntℎ reversible reaction is characterized by a forward reaction rate, kfn following

the Arrhenius form shown in Equation 3.12. The reverse reaction rate constant, krn, is

calculated from thermochemistry.

kfn = A ⋅ (T )n exp−EaR ⋅ T

(3.12)

where

kfn is the reaction rate constant

A is the pre-exponential collision frequency factor in cm3

mol⋅s

T is temperature in kelvin

n is the temperature dependence factor

Ea is the activation energy in calmol

R is the ideal gas constant in calmol⋅K

The reaction rate constant, kfn, in Equation 3.12 depends on the temperature, activa-

tion energy, and the collision frequency factor. A temperature dependence term is incor-

porated into the equation (i.e., n ∕= 0) for reactions that exhibit non-Arrhenius behaviour

over the range of temperatures encountered in combustion. Typically, experiments are

conducted to determine the coefficients in the rate equation; however estimations and

calculations using ab initio quantum chemistry methods are also employed. This study


uses, wherever possible, rate coefficients from published experimental and computational

studies available through the “NIST Chemical Kinetics Database” [12]. For reactions

that have not been studied previously, this study estimates rate coefficients heuristically.

3.3.2 Thermochemical Database

Thermochemical data for each species in the chemical kinetic mechanism are required for

CHEMKIN to calculate thermodynamic properties, thermal transport properties, and

reaction equilibrium constants. Contained within the thermochemical data file are the

species’ name, elemental composition, electronic charge, and phase. In addition, fourteen

polynomial fitting coefficients are provided to calculate the constant pressure molar heat

capacity (C0p), molar enthalpy of formation (ΔfH

0), and molar entropy of formation

(S0p) at any temperature. From these calculated properties, CHEMKIN can calculate

other important thermochemical properties, such as the constant volume heat capacity,

internal energy, Gibb’s free energy, and Hemholtz free energy. Mass based properties are

generated by dividing the property in molar units by the molecular weight [9].

Both computational and experimental methods can be used to determine thermo-

chemical data properties, and the “NIST Chemistry WebBook” has a good compilation

of previously published data [13]. For larger molecules which have not been experimen-

tally studied and for which quantum chemical calculations are computationally expensive,

thermochemical properties can be estimated based on group additivity methods. Ben-

son [14] has proposed a systematic way of estimating thermochemical properties for a

molecule from data on the bonded atomic groups which comprise it. The additivity

law determines the property X of a complex molecule by adding the tabulated bond

properties for simple molecules (e.g.,∑Xmolecule =

∑Xbonds). The THERGAS [15] and

THERM [16] softwares, which is based on Benson’s method, are used in the present study

to estimate thermochemical properties of new species. The user inputs the structure of

the molecule in a specified format, and the computer determines its thermochemical

properties by adding together tabulated bond properties.

3.3.3 Transport Database

Combustion is typically a combination of chemical kinetic driven processes (i.e., produc-

tion and destruction of species) and transport driven processes (i.e., convection, diffusion,

and conduction). In certain combustion applications, such as a perfectly stirred reactor or


plug-flow reactor, the overall rate is assumed to be kinetically controlled since the trans-

port processes occur infinitely fast. However in other cases, such as laminar and diffusion

flames, the transport processes are rate-controlling. Therefore, the molecular transport

of species, momentum, and energy in the gas mixture must be evaluated from the dif-

fusion coefficients, viscosities, thermal conductivities, and thermal diffusion coefficients.

CHEMKIN determines these temperature and pressure dependent flow properties of each

individual species using standard kinetic theory expressions, and then determines the gas

mixture properties using mixture averaging rules. Note, in some cases CHEMKIN sub-

stitutes the mixture-averaged approach with a multicomponent approach to determine

the transport properties of the gas mixture. The interested reader is referred to the

“CHEMKIN Theory Manual” for further elaboration on the methodology and the ex-

pressions used in determining the flow properties of individual species and gas mixtures

[9].

In this study, priority is placed on determining the molecular transport parameters for

each species in the gas mixture, such that the CHEMKIN can determine the flow prop-

erties using its standard kinetic theory expressions. The molecular transport parameters

are inputted into CHEMKIN via a specified data format, as follows, in order:

1. An index indicating the geometrical configuration of the molecule. If the index is

0, then the molecule is a monoatomic. If the index is 1, then the molecule is linear.

If the index is 2, then the molecule is nonlinear.

2. The Lennard-Jones potential well depth, �/kb, in Kelvin.

3. The Lennard-Jones collision diameter, �, in angstroms.

4. The dipole moment, �, in Debyes (10−18cm3/2ergs1/2).

5. The polarizability, �, in cubic angstroms

6. The rotational relaxation collision number, Zrot, at 298 K.

The molecular transport parameters can be obtained from a variety of sources, and

CHEMKIN itself contains a transport database of over 200 species. However, more

species are often required when dealing with new fuels. The modeler can then turn to

previous modeling work to search for transport parameters. Alternatively, much data can

be found in standard reference texts such as “Molecular Theory of Gases and Liquids”


[17] or other chemistry handbooks. If the data is still not found, then estimation and

analogy with related molecules can be used.

For new molecules, the Lennard-Jones collision diameter and potential well depth can

be estimated via different methods. Svehla describes how these parameters are obtained

by computing “best fits” to experimental data of a macroscopic transport property (e.g.,

viscosity) [18]. When experimental data is not available, as is the case for most gases,

these parameters are estimated using a variety of techniques. Svehla describes how

they can be calculated using the physical-chemical properties (e.g., boiling point, molar

volume, etc.), several empirical or combining rules, or other theoretical relations [18].

This study uses the correlations developed by Tee, Gotoh, and Stewart [19] and described

in Wang and Frenklach [20]. The correlations allow for the calculation of the Lennard-

Jones collision diameter and potential well depth using the critical pressure (Pc) and

critical temperature (Tc) of the gas. The critical temperature of a substance is the

temperature at and above which separate gas and liquid phases do not exist, and only

the supercritical state exists. The critical pressure is the vapor pressure at the critical

point (refer to Figure 3.1).

Figure 3.1: A typical phase diagram showing critical point

The equations for calculating the Lennard-Jones collision diameter and potential well

depth are given below. The accentric factor ! in Equations 3.13 and 3.14 is evaluated


using the Lee-Kesler vapor-pressure relations shown in Equation 3.15 [21]. The critical

temperature, critical pressure, and boiling point (Tb) used in the calculations are obtained

from the “NIST Chemistry WebBook” [13]. If Pc, Tc, and (Tb) are not readily available

for the species, they can be approximated from species with similar molecular structures.

�(PcTc

)1/3

= 2.3551− 0.0874! (3.13)

��

kbTc= 0.7915 + 0.1693! (3.14)

! =−ln(Pc)− 5.927 + 6.096Tb

Tc

−1+ 1.289lnTb

Tc− 0.169Tb

Tc

6

15.252− 15.688TbTc

−1 − 13.472lnTbTc

+ 0.436TbTc

6 (3.15)

The dipole moment (�) is a measure of the extent of polarity in covalent molecules.

It is dependent on the difference electronegativity of the bonding atoms, and is precisely

defined as the product of the magnitude of the charge and the distance between the

charges. Nonpolar compounds, such as fully saturated hydrocarbons have zero dipole

moments while oxygenated compounds display higher dipole moments. Many experi-

mentally measured dipole moments are available in McClellan’s “Tables of Experimental

Dipole Moments” [22]. If experimental data is not available then the the molecular dipole

moment, which is a vector property, can be calculated for using vector addition of known

bond moments [23]. Such a method requires detailed information of the geometry of

molecular bonds and their electronegativities.

The polarizability (�) of a molecule quantifies the tendency of a molecules charge

distribution (i.e., electron cloud) to be distorted from its normal shape by an external

electric field (e.g., a nearby dipole or ion). Experimentally measured polarizability values

in cubic Angstroms can be obtained from the “CRC Handbook of Chemistry and Physics”

[24]. Bosque and Sales [25] have presented an empirical additive formula that allows the

estimation of polarizability from the molecular formula (i.e., # of C, H, and O atoms),

as shown in Equation 3.16.

� = 0.32 + 1.51 ∗#C + 0.17 ∗#H + 0.51 ∗#O (3.16)


3.4 Developing Chemical Kinetic Mechanisms

The process for developing and validating chemical kinetic mechanism was outlined by

Frenklach et al. [26] and summarized by Simmie [8]. Figure 3.2 is a flowchart of the

mechanism development process. This flowchart indicates that developing and validat-

ing a mechanism is a continuously evolving process wherein experiments and modeling

symbiotically achieve a satisfactory mechanism. The process can be summarized as fol-

lows:

1. Generate a list of elementary reactions.

2. Determine reaction rate constants for each reaction using literature sources or es-

timation, paying attention to temperature and pressure dependencies. Provide

thermochemical data to calculate equilibrium reverse rate constants.

3. Conduct controlled experiments that can be used to validate the reactions and rate

parameters given in the model.

4. Solve the reaction mechanism kinetics and transport equations using a computer

simulation of the experimental configuration. Conduct a sensitivity analysis to

determine the impact of specified rate constants on the final result.

5. Compare the experimental data to the model predicted values. Optimize reac-

tion rate parameters that have the greatest impact on fitting desired experimental

values.

The comprehensiveness of a mechanism is measured by its ability to describe com-

bustion phenomenon extensively. A mechanism is not considered comprehensive if it has

been tested against a single experiment because the role of each elementary reaction varies

with temperature, pressure, and composition. For example, reactions between hydrogen

atoms and fuel molecules are dominant in fuel-rich conditions, while reactions between

hydroxyl radicals and fuel molecules dominate in fuel-lean conditions. Many reactions

are important only at low temperatures, while others are dominant at high tempera-

tures. In an early treatise on chemical kinetic mechanisms for hydrocarbon combustion,

Westbrook [5] explains that a comprehensive mechanism must be validated against ex-

perimental data covering chemically reacting flows at various temperatures, pressures,

and reactant compositions.


Determine thermo-

chemical & transport properties

Conduct controlled validation

experiments

Simulate experiments

using computer

model

Compare experiments

with computer simulation

Formulate elementary reaction set

and determine reaction rates

Figure 3.2: Flowchart for developing and validating chemical kinetic mechanisms

Autoignition characteristics are typically studied in shock tubes or rapid compression

machines, while reactions in a flameless premixed environment are studied in a jet stirred

reactor (i.e., perfectly stirred reactor). Laminar premixed and non-premixed flames are

often used to study combustion kinetics occurring at high temperatures. Each apparatus

can be operated at various temperature and pressure regimes to determine the effects of

these parameters on kinetic processes.

Since combustion of hydrocarbon fuels consists of sequential fragmentation of fuel

molecules into intermediates species, a comprehensive mechanism for any fuel must con-

tain detailed sub-mechanisms for the fuel’s intermediates. For example, since hydrogen

and carbon monoxide are products of hydrocarbon combustion, any hydrocarbon mecha-

nism must include reaction sub-mechanisms for hydrogen and carbon monoxide [7]. This

observation allows for the systematic and hierarchical development of kinetic mechanisms

by sequentially incorporating new species and reaction schemes in order of increasing

complexity [5].


3.4.1 Mechanisms for Hydrocarbon Fuels

Chemical kinetic mechanisms for hydrocarbon fuels have been the focus of intense re-

search for several decades. Chemical kinetic mechanisms for biofuels, such as ethanol

and biodiesel, have only received attention recently. This section provides a background

on the combustion pathways of alkanes and alkenes because the same reaction types and

classes can be applied to biofuel combustion. In addition, detailed reaction rate stud-

ies for hydrocarbon fuels can be used to determine rate constants for similar reactions

for which no detailed reaction rate studies exist. Readers that are interested in a de-

tailed discussion of hydrocarbon combustion are directed towards recent review articles

by Battin-Leclerc [4] and Simmie [8].

A clear and simple explanation of the oxidation of fuels is given by Glassman [27].

Combustion reactions are driven by the formation of highly reactive radical, such as O,

OH, and H. During combustion, fuels are oxidized by a series of chain reactions which

can be categorized as one of following:

1. chain initiating,

2. chain propagating and chain branching,

3. chain terminating.

Chain initiating occurs when radical species are produced by dissociation of the re-

actants. The chain is propagated and branched as radicals react with stable compounds

to form additional radical species. Finally, the chain terminates when two radicals re-

combine to form stable species. The following subsections describe the major reaction

pathways for the combustion of alkanes and alkenes under low and high temperature

conditions. Comprehensive mechanisms would contain a number of minor reactions;

however, for the sake of simplicity, they have not been included here.

Combustion of Alkanes

Low Temperature Combustion of Alkanes

The combustion of hydrocarbons is different at low temperatures than high tempera-

tures. The general mechanism for the low temperature combustion of hydrocarbons was

developed by Semenov [28]. Benson [29] introduced the isomerization reaction of large


hydrocarbons (Equation 3.20) to the Semenov mechanism. The following is a simplified

form of the Semenov mechanism including isomerization of large hydrocarbons:

RH +O2 → R ⋅+HO2⋅ (3.17)

R ⋅+O2 → alkene+HO2⋅ (3.18)

R ⋅+O2 → RO2⋅ (3.19)

RO2⋅ → ROOH (3.20)

ROOH +O2 → RO ⋅+OH⋅ (3.21)

HO2 ⋅+RH → H2O2 +R⋅ (3.22)

H2O2 +M → OH ⋅+OH ⋅+M (3.23)

The chain is initiated by low temperature combustion of the hydrocarbon (RH) to

form an alkyl radical (R⋅) and a hydroperoxy radical (HO2⋅), as shown in Equation 3.17.

Next, the chain is propagated by one of two parallel reactions between alkyl radicals

and oxygen to form an alkene, HO2⋅, and RO2⋅ (Equations 3.18 and 3.19). These reac-

tions compete with each other depending on the temperature. At temperatures above

500 K, Equation 3.18 predominates, wherein the oxygen abstracts a hydrogen from the

alkyl radical to form an alkene and a hydroperoxy radical. At temperatures below 500

K, Equation 3.19 is favored, wherein the oxygen adds to the alkyl radical to form an

alkylperoxy radical.

At low temperatures (below 500 K), propagation continues by the isomerization of

RO2⋅ to produce peroxide species (ROOH) (Equation 3.20). The radical pool then builds

up by degenerate branching of ROOH to form RO⋅ and OH⋅ radicals (refer to Equation

3.21). Further developments on this low temperature mechanism have been published by

Zhao et al. [30].

At intermediate temperatures (above 500 K), the HO2⋅ radical is more abundant,

so the reaction is propagated by hydrogen abstraction on the hydrocarbon by HO2⋅to form hydrogen peroxide (H2O2) and an alkyl radical (refer to Equation 3.22). As

the temperature increases, hydrogen peroxide decomposes to form two hydroxyl radicals

(refer to Equation 3.23). The fuel-air mixture explodes once the radical pool builds up,

and then high temperature combustion predominates.


Intermediate and High Temperature Combustion of Alkanes

The intermediate and high temperature combustion (e.g., above 900 K) of alkanes larger

than methane proceeds via either unimolecular decomposition or H-atom abstraction.

Unimolecular decomposition involves the breaking of the fuel’s C-C and/or C-H bonds,

and such reactions are typically favored at very high temperatures (e.g., above 1300-1400

K) and fuel rich conditions. The breaking of C-C bonds is favored over the breaking of

C-H bonds because of the bond dissociation energy is lower, and the reaction proceeds

as shown in Equation 3.24.

RH + (M)→ R′ ⋅+R′′ ⋅+(M) (3.24)

where

RH is an alkane molecule

R′⋅ and R′′⋅ are alkyl radicals such as CH3, C2H5, etc.

(M) is a non-reacting collision partner

When highly reactive radicals, such as O, OH, and H, are present they can abstract

H atoms from the fuel, as shown in Equation 3.25. These reactions are favored at

intermediate temperatures (e.g., 900-1300 K) and fuel lean conditions. Relative rate

coefficients for H abstraction by radicals from tertiary, secondary, and primary CH bonds

are given in Table 3.1 [27]. Tertiary CH bonds are those on a carbon atom connected

to three other carbon atoms. Secondary CH bonds are on a carbon atom connected to

two other carbons. A primary CH bond is one on a carbon connected only to one other

carbon, such as the carbon at the end of a hydrocarbon chain. The table indicates that

tertiary CH bonds are the weakest and primary CH bonds are the strongest. If the fuel is

an alkene then radicals will abstract H from carbon atoms that are singly bonded because

CH bonds on doubly bonded carbon atoms are very strong.

RH +X⋅ → R ⋅+XH (3.25)

where X is any radical specie, usually O⋅, OH⋅, H⋅, and CH3⋅

The alkane radical then decays to form an alkene and a radical specie, as shown

in Equation 3.26. For example, the isopropyl radical, obtained from H abstraction of

the secondary CH bond in propane, will decay to propene and a H atom, as shown in


Table 3.1: Relative magnitudes of rate constants for H abstraction from different CH

bonds [27]

Tertiary Secondary Primary

H 13 4 1

O 10 5 1

HO2 10 3 1

OH 4 3 1

Figure 3.3. The process by which the alkyl radical decomposes is called �-scission. In �-

scission, the bond once removed from the radical site will break to form an alkene without

a hydrogen shift. Furthermore, C-C bonds are more likely break than C-H bonds since

C-C bonds are weaker.

R→ alkene+R′⋅ (3.26)

where

R′ is a hydrocarbon radical or H atom

Figure 3.3: Decay of isopropyl radical

Combustion of Alkenes

Alkane combustion ends with the formation of alkenes and a pool of radical species, so

the combustion of these alkene compounds will now be discussed, taking ethene as an

example. First, the C=C double bond is attacked primarily by the biradical O⋅ , which

forms an intermediate species that subsequently decays, as shown in Figure 3.4. Thus,

the two primary addition reactions are shown in Equations 3.27 and 3.28. Some minor


reactions involving H abstraction by OH⋅ and H⋅ radicals also play a role, as shown in

Equations 3.29 and 3.30, respectively.

C2H4 +O⋅ → CH3 ⋅+HCO⋅ (3.27)

C2H4 +O⋅ → CH2 ⋅+CH2O (3.28)

C2H4 +OH⋅ → C2H3 ⋅+H2O (3.29)

C2H4 +H⋅ → C2H3 ⋅+H2 (3.30)

Figure 3.4: Addition of O radical to ethene from [27]

Then the vinyl radical (C2H3) decays to acetylene, as shown in Equation 3.31. The

acetylene is consumed by a reaction with the biradical O⋅ to form a methylene radical and

carbon monoxide, as shown in Equation 3.32. The fate of CH3, CH2O (formaldehyde),

CH2, and CO are described in the mechanism for methane [27].

C2H3 ⋅+M → C2H2 +H ⋅+M (3.31)

C2H2 +O⋅ → CH2 ⋅+CO (3.32)

Mechanism of Soot Formation

The term soot refers to tiny amorphous carbon particles produced from the combustion

of hydrocarbon fuels. Soot emissions have become an environmental concern due to the

negative impacts of particulate matter on the human respiratory system. In addition, soot

particles in the atmosphere can contribute to global warming by altering the radiative

balance of the atmosphere [31].


The presence of soot particles in hydrocarbon flames is identifiable by their char-

acteristic yellow-orange appearance. This color is generated by photons emitted from

the solid carbon particulates. On the other hand, flames that do not contain soot are

blue in color. The formation of soot particles in a combustion environment is a highly

complex mechanism and depends on a number of factors, e.g. fuel composition, temper-

ature, fuel-oxygen ratio, flame configuration, etc.. However, the formation of soot has

been shown to be highly controlled by chemistry related phenomenon [32]. Glassman

[27] has summarized the work of other researchers to describe the dominant route of soot

formation.

Initially, the fuel breaks down to acetylene, as mentioned in the previous discussion

on hydrocarbon oxidation. In the high-temperature post-flame regime, soot formation is

initiated by the growth of small straight-chain alkenes (acetylene) to small aromatic com-

pounds (e.g. benzene). The aromatic hydrocarbons then react sequentially with smaller

hydrocarbons (acetylene, in particular) to form larger polyaromatic hydrocarbon (PAH)

species. Gaseous PAH molecules continue to nucleate until the smallest identifiable soot

particles appear, with diameters of a few nanometers.

Figure 3.6 [27] shows the mechanism for soot formation in more detail. Initially,

the acetylene (C2H2) undergoes H addition to form the vinyl radical. The vinyl radical

then reacts with another acetylene molecule to form the 1,3-butadienyl radical. The

1,3-butadienyl radical can also be readily formed from C4 hydrocarbons via hydrogen

abstraction then �-scission.

In diffusion flames, the alternate route A is then followed, wherein the 1,3-butadienyl

radical reacts again with acetylene to form the cyclic phenyl (C6H5) radical, following ring

closure. The phenyl radical is essentially a benzene molecule missing one hydrogen atom.

The phenyl radical is also produced by alternate route C, wherein methyl acetylene (i.e.,

propyne C3H4) pyrolyzes rapidly to form the aromatic. The phenyl radical can proceed

to grow into a larger aromatic via the two-step hydrogen abstraction carbon addition

(HACA) mechanism. In the HACA mechanism, the aromatic molecule is converted to

a radical by hydrogen abstraction, and then grows in size by the (carbon) addition of

an acetylene molecule. The HACA mechanism continues until larger PAH molecules

appear. As the concentration of gaseous PAH species increases, nucleation occurs and

soot particles begin to appear.

This thesis study does not directly measure the soot levels generated in biofuel diffu-

sion flames. However, the concentrations of many soot precursors are measured, specifi-


cally, acetylene, C4 hydrocarbons, 1,3-butadiene, and benzene. Analyzing the concentra-

tions of these species in the diffusion flame can offer an insight into the sooting potential

of various biofuel fuels.

3.4.2 Determining Rate Coefficients

The ideal method for determining reaction rate coefficients for each reaction in the mech-

anism is to conduct fundamental experiments over the range of temperatures encountered

during combustion (e.g., typically 300-2500 K). However, such experiments are difficult

and have not been performed for many of the fuel and intermediate species encountered

in biofuel combustion. Computational methods based on high-level ab initio quantum

chemistry and density function theory (DFT) are also available for determining rate coef-

ficients. However, ab initio methods are computationally expensive while DFT methods

are considered inaccurate [33, 34]. A simpler heuristic method of determining reaction

rate constants uses analogies to similar well studied reactions [35].

Determining rate constants using analogies requires model compounds of similar

structure and chemical bonding characteristics. Sufficient experimental and theoreti-

cal information must be available for the model compounds to make the analogy valid.

Figure 3.5 provides the chemical structure and bond dissociation energy (BDE) [24] of

some common alkanes and alcohols, and this information is used to display how analogies

can be used to determine reaction rate constants for n-butanol combustion.

First, it is observed that the chemical structure of n-butanol’s functional group is

similar to that of ethanol because both are alcohols. The BDE of the O-H bond in the

hydroxyl group of ethanol and n-butanol is roughly the same, given the reported error

margins. Therefore, reactions involving H-atom abstraction from the hydroxyl group

in n-butanol would have the same reaction rate constant as the analogous reaction in

ethanol. The BDE between the � C and the OH group is also the same in n-butanol

and ethanol; therefore, scission of C-OH bond in butanol would have the same reaction

rate as the analogous reaction in ethanol. The BDE of the � C-H bond is not reported

for butanol. However, the BDE of ethanol’s � C-H bond is observed to be nearly the

same as the BDEs of the central C-H bonds in propane and n-butane, and therefore

it is likely that butanol’s analogous bond has a similar BDE. Reactions involving H-

atom abstraction from n-butanol’s � C are likely to have reactions rates identical to the

analogous reaction in ethanol. The BDE for breaking the � and � carbons in n-butanol is


close to the BDE of the analogous bond in n-butane, ethanol, and propane, and therefore,

it is possible to use analogies to determine the the reaction rate constants. The BDE for

the remaining �, , and � carbons in n-butanol are unknown, but they are likely similar

to those of n-butane because the destabilizing effects of the hydroxyl group, which are

only strong on the adjacent atoms, are minimal.

CH3

C

H2

CH

2

C

H2

OH

CH3

C

H2

OH

CH3

C

H2

CH

2

CH3

CH3

CH

2

CH3

432.3 kJ/mol ± 5 kJ/mol

389.9 ± 4.2 kJ/mol

441± 5.9 kJ/mol421.7 ± 8 kJ/mol

401.2 ± 4.2 kJ/mol

391.2 ± 2.9 kJ/mol

357.3 ± 3 kJ/mol

364.8 ± 4.2 kJ/mol

n-butanol

ethanol

411 .1 ± 2 .2 kJ/mol

421 .3 kJ/mol

363.2 ± 2.5 kJ/mol

372 ± 2.9 kJ/mol

411 .1 ± 2 .2 kJ/mol

n-butane

422 .2 ± 2 .1 kJ/mol

410 .5 ± 2 .9 kJ/mol

379.3 ± 2.1 kJ/mol 379.3 ± 2.1 kJ/mol

422 .2 ± 2 .1 kJ/mol

propane

Figure 3.5: Chemical structures and bond dissociation energies for alcohols and alkanes

[24]


Fig

ure

3.6:

Gen

eral

mec

han

ism

for

soot

form

atio

nfr

omG

lass

man

[27]

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[27] I. Glassman, Combustion, 3rd ed. San Diego, CA: Academic Press, 1996.

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Chapter 4

Experimental Apparatus and

Analytical Methodology

This section provides a detailed description of the experimental setup used to study

opposed-flow diffusion flames of biofuels. The setup has been previously described in the

MASc thesis entitled “Using an opposed-flow diffusion flame to study the oxidation of C4

fatty acid methyl esters” by S.M. Sarathy [1]. The setup was modified in order to study

the proposed biofuels, so this chapter presents a description of the modified setup. This

chapter does not include descriptions of other experimental setups (i.e. the jet stirred

reactor) used to validate chemical kinetic mechanisms for biofuels, since these setups are

not the author’s contribution.

The setup is designed to generate an opposed-flow diffusion flame from a liquid or

gaseous fuel. The concentrations of stable species and the temperature profile in the

flame are then obtained. The setup consists of the fuel delivery system, the opposed

flow diffusion flame burner, the sample collection apparatus, and a variety of analytical

instruments. The fuel delivery system pumps the liquid fuel into a vaporizing chamber

that produces a mixture of vaporised fuel and nitrogen. The gaseous fuel stream and

the oxidizer stream then flow into an opposed flow burner to produce a planar flame.

Samples are extracted from the flame region using a fused silica micro-probe connected

to a vacuum pump. Analysis of hydrocarbon compounds is performed using a gas chro-

matograph flame ionization detector (GC/FID). Carbon dioxide and carbon monoxide

are quantified by an non-dispersive infrared (NDIR) analyzer. The flame temperature

profile is measured using an R-type thermocouple.

43

Chapter 4. Experimental Apparatus and Analytical Methodology 44

4.1 Opposed-flow Diffusion Burner Setup

The opposed-flow diffusion flame offers experimental and modeling advantages over co-

flow flames, although their stability is more sensitive to flow conditions [2]. The opposed-

flow configuration results in a planar flame which simplifies flame analysis to a one-

dimensional system. The species concentration and temperature are a function of axial

distance only. Furthermore, the modeling of fluid mixing is simplified in laminar flames

because fuel and oxidizer mixing is limited to diffusional processes (i.e., turbulent mixing

patterns are not considered).

These experiments utilize two identical burners1 with circular burner ports. The two

burner ports are placed opposite each other in the same vertical plane. A fuel mixture

is fed through the bottom port while an oxidizer mixture is fed through the top. The

two opposing streams flow into each other to create a stagnation plane between the two

ports. The vertical location of the stagnation plane depends on the momentum of the two

streams. The stagnation plane prevents any non-diffusional mixing between the fuel and

oxidizer. As the two streams molecularly diffuse into each other, a flat flame is ignited.

The flame is physically located wherever the stoichiometric mixture fraction of fuel and

oxygen exists. Therefore, the exact location depends on the mole fractions of fuel and

oxygen in their respective streams, as well as each stream’s diffusivity.

A diagram of the burner port is shown in Figure 4.1. Each burner port consists of a

stainless steel housing enclosing a porous sintered bronze matrix. The porous material

is divided into inner and outer coaxial cylinders of diameter 25.4 mm and 38.1 mm,

respectively. The inner cylinder directs the fuel or oxidizer streams towards each other,

and the outer annulus can be used to create a nitrogen shroud around the flame for

minimizing external flow disturbances. The annulus’ shrouding feature was not exploited

in this study. Plug flow boundary conditions were assumed for the modeling of this setup.

The temperature of the gases flowing through the ports was controlled by circulating a

heat transfer fluid through porous plugs packed between two co-axial cylinders. This

ensures that the gases have a uniform laminar flow and flat velocity profile at the port’s

surface. The heat transfer fluid was maintained at set temperature using either a water

heating recirculator2 or an electric air heater3. Water was the heat transfer fluid for

1Purchased from Holthuis & Associates McKenna Flat Flame Burners2PolyScience Heating Recirculator Model 2103Omega process heater


experiments requiring burner port temperatures below 100 ∘C, while air was the heat

transfer fluid in experiments requiring burner port temperatures above 100 ∘C (refer to

Table 4.2). Additional heat input is also provided by wrapping the bottom port with

heating tapes4.

Figure 4.1: Diagram of burner port

A photograph of the actual burner setup is shown Figure 4.2. The two burners

are coaxially mounted 20 mm apart facing each other. A custom-built aluminum holder

provides support to the burners, and a clear quartz shroud 22 cm in diameter protects the

flame from airflow disturbances from the surrounding environment. The entire assembly

is mounted atop a translation stage5 which moves along the vertical axis with the rotation

of a micrometer knob. This made it possible to obtain a vertical profile of the flame

temperature and emissions between the two burner ports. Combustion products from

the burner flow upwards into a ventilated hood which is ducted to the laboratory fume

hood.

4.2 Fuel Preparation and Vaporization

The fuel stream fed to the burner is a mixture of the fuel and nitrogen gas. Properties

of each fuel are shown in the following Table 4.1, which indicates that three of the fuels

4Omega FGS Standard Insulated High Temperature Heating Tapes5Newport M-MVN120 Precision Ball Bearing Vertical Linear Stage, 20mm Travel


Figure 4.2: Photograph of burner setup

are liquids at room temperature. Gaseous fuels are stored in compressed gas cylinders

and mixed in line with nitrogen before being delivered to the burner. Liquid fuels are

pumped from a bottle to an ultrasonic atomizer. The atomizer probe sprays the fuel into

a heated stainless steel mixing chamber where it mixes with nitrogen gas. The mixture

of fuel and nitrogen is then fed to the bottom burner port.

Table 4.1: Physical Chemical Properties of the Fuels Used

Mol. Wt. (g/mole) Phase at STP Normal Boiling Pt. (∘C)

n-butanol 74.1 liquid 117

n-butane 58.1 gas 0

methyl decanoate 186.3 liquid 224

Liquid fuel is pumped from its stock bottle by a peristaltic pump head driven by

a motor6 with a polytetrafluoroethylene (PTFE) pump head. The pumping system is

calibrated for each fuel at the beginning of each experiment using a graduated cylinder

to measure the volume of liquid pumped at a various oump head drive rates. The pump

drive rate is then set to correspond with the desired volumetric flow rate. The selected

flow rates for each fuel are provided in the next section.

A new vaporization system designed by a postdoctoral fellow, Dr. John Z Wen, to

deal with the high boiling point liquid fuels is used in this study. The design consists of

a commercially available ultrasonic atomizer which is threaded to a heated stainless steel

6Cole-Parmer Masterflex L/S Microprocessor Pump Drive


chamber. The pumped fuel is delivered to a 30 kHz ultrasonic atomizer7 unit that breaks

the fuel into micro-droplets. The unit’s ultrasonic power supply converts 60 Hz energy to

a high frequency electrical energy at 30 kHz. Then, a piezoelectric transducer converts

the electrical energy to mechanical vibrations. The vibrations are intensified in a probe

and focused at its tip. The liquid fuel is dispensed in the probe, where it spreads out as

a thin film on the tip. The oscillations at the tip atomizes the liquid into micro-droplets

to form a gentle, low viscosity mist. The body of the atomizer was kept cool at 30 ∘C by

blowing compressed air over it.

A schematic of the fuel vaporization/mixing chamber is shown in Figure 4.3. The

atomized fuel is sprayed into the mixing chamber, where it mixes with nitrogen gas8.

The nitrogen was not preheated in this study because high temperatures near the top

of the mixing chamber can overheat the atomizer. The temperature in the lower half

of the chamber was maintained using heating tapes9 and a thermal blanket10. A high

temperature in only the lower portion of the mixing chamber served to vaporize the fuel

droplets while minimizing the heat transfer to the atomizer. The temperature inside the

chamber is measured using a stainless steel sheathed K-type thermocouple inserted at the

top of the chamber. Specific details on the temperature set point are provided in Table

4.2 in the next section. The mixing chamber is a custom-made stainless steel column11 30

cm in length and 10 cm in outer diameter (OD), with a 15 cm OD, 3/8′′ thick, stainless

steel flange bolted at the top to accommodate insertion of the atomizer, nitrogen, and

thermocouple. The column is packed with a 20 cm long bed of glass marbles. The

bed increases the path length traversed by gaseous fuel and nitrogen molecules; thereby,

increasing mixing of the two streams. The gaseous mixture of fuel and nitrogen then

flows to the bottom port of the burner apparatus via a 1/4′′ stainless steel transfer line

heated to 250 ∘C 12.

It was found that the atomizer did not operate properly when threaded onto a 3/8′′

thick stainless steel flange plate. The thick plate dampens the oscillations transmitted

to the atomizer tip and prevents the tip from vibrating. By working with the atomizer

manufacturer, it was determined that a portion of the flanged plate should be machined

7Sonaer custom built 30 kHz model8Linde Grade 4.89Omega SWH Ultra-High Temperature Heating Tapes

10Unifrax InsulfraxS blanket11Designed by Dr. John Z. Wen12Unique Heated Products Instrument Grade Heating Sample Line


to 1/32′′ thick, in order to minimize the dampening effects. The thin portion of the

machined flange plate allowed the atomizer to function properly while still meeting the

other design requirements of the flange. Refer to Figure 4.4 for a schematic of the

machined flange plate.


Figure 4.3: Schematic of the mixing chamber. Design by Dr. John Z. Wen

Figure 4.4: Schematic of the machined flange plate.


4.3 Supply of Fuel and Oxidizer Streams

The flow rates of fuel and oxidizer streams through the burner ports were key parameters

in these experiments. It is important that the momentums of the two streams be nearly

equal, so that a stagnation plane is created where the streams meet. The molar concen-

trations of fuel and oxidizer in each stream needs to be sufficient enough to light a flame;

however, high sooting flames are not desired. The flow rates of nitrogen, oxygen and air

are controlled using mass flow controllers13, while the flow rate of liquid fuel is controlled

by a peristaltic pump. The mass flow controllers are regularly recalibrated using a pos-

itive displacement gas flow meter 14. The inlet oxidizer and fuel stream concentrations

are selected based on the following criteria:

∙ a low Reynold’s Number to create a laminar flame

∙ a low sooting flame to prevent clogging of sampling probe

∙ avoid a very hot flame that will damage the probe

∙ a balanced momentum of the two streams to form a stagnation plane at their

intersection

∙ avoid excessive unburned fuel

∙ at the flame plane, an N2/O2 ratio near that of air to make the study relevant to

actual flames

The molar composition and measured temperature of the fuel and oxidizer streams

entering the burner for each experiment is indicated in Table 4.2. The temperature

of the oxidizer stream exiting the top burner was near 140 ∘C. The cause of this high

temperature was heat convected towards the top burner port from rising combustion

product gases.

It is important to ensure that the liquid fuel is sufficiently vaporized in the mixing

chamber and that it does not condense before exiting the burner, which has a maximum

operating temperature of 130 ∘C. To prevent condensation, the experiments conducted in

this study were performed at low partial pressures of fuel (i.e., 0.02-0.06 atm) wherein the

13Teledyne-Hastings Mass Flow Controller HFC20214BIOS Definer 220


vaporisation temperature is depressed. The vapour pressure of different FAME species

[3] was plotted to determine which fuels could be used under the temperature constraints

of the burner. Figure 4.5 indicates that FAME with 10 carbons or less can be sufficiently

vaporised at 130 ∘C.

Table 4.2: Experimental conditions

Mixing Chamber Fuel Stream Fuel Port Oxidizer Stream

T (∘C) Composition Exit T (∘C) Composition

n-butanol 110 5.9% Fuel 80 42.1% O2

94.1% N2 57.9% N2

n-butane N/A 5.9% Fuel 30 42.1% O2

94.1% N2 57.9% N2

methyl decanoate 150 1.8% Fuel 110 42.1% O2

98.2% N2 57.9% N2

1.0E-05

1.0E-04

1.0E-03

1.0E-02

1.0E-01

1.0E+00

1.0E+01

1.0E+02

0 50 100 150 200 250 300

Temperature (C)

Vapour

Pre

ssure

(a

tm)

Methyl Hexanoate

Methyl Octanoate

Methyl Decanoate

Methyl Dodecanoate

Methyl Oleate

Figure 4.5: Vapor pressure curves for fatty acid methyl esters


4.4 Reynold’s Number and Strain Rate Calculations

The Reynold’s Number (Re) is used to determined whether the flow exiting the burner

port is laminar (Re < 2300), transient (2300 < Re < 4000), or turbulent (Re > 4000).

The goal of these experiments is to generate a laminar diffusion flame, so the fluid velocity

must be set accordingly. Equation 4.1 is used to calculate the Reynold’s Number for flow

in the burner port assuming a plug flow behaviour.

Re =� ⋅ u ⋅ d�

(4.1)

where Re is the Reynold’s Number

� is the gas density in kgm3

u is the gas velocity in ms

d is the burner port diameter in m

� is the dynamic viscosity in N ⋅sm2

The strain rate is defined as the normal gradient of the normal component of the flow

velocity [4]. The strain is calculated using Equation 4.2.

a1 =2∣V1∣L

(1 +∣V2∣√�2

∣V1∣√�1

)(4.2)

where a1 is the strain rate on the fuel side in s−1

L is the distance between the two burner ports in cm

∣V1∣ is the absolute value of the fuel stream velocity at the fuel boundary

in cms

∣V2∣ is the absolute value of the oxidizer stream velocity at the oxidizer

boundary in cms

�1 is the fuel stream density in gcm3

�2 is the air stream density in gcm3


4.5 Gas Sampling System

The previous sections discussed the procedure for producing an opposed flow diffusion

flame. Once the flame was generated, a sampling system was used to obtain qualitative

and quantitative information about the flame’s characteristics. Specifically, the species

concentrations at various points between the two burner ports was measured to obtain

characteristic profiles. The following sections discuss the gas sampling system, as well as

the sampling procedure.

4.5.1 Sampling Apparatus

Microprobes, due to their small perturbation of flow fields, are commonly used in flame

studies to acquire the concentration of stable species [5, 6, 7, 8]. The gas sampling system

in these experiments consists of a quartz microprobe connected to a dual-stage pump15

via a heated 1/4′′ stainless steel line and a vacuum pressure gauge. The microprobe is

mounted on a sliding stage, allowing it to move into and out of the flame region easily.

The first stage (vacuum) of the pump creates a suction in the sampling line to withdraw

gas samples from the flame. An analytical instrument is connected downstream of the

second stage (compressor) of the pump to study the gases flowing through the line. The

compressor head on the pump pushes samples into the analytical instrument via a 1/4′′

stainless steel transfer line heated to 250 ∘C 16. The heated transfer lines are required to

prevent the condensation of high molecular weight species out of the sample gas.

Previous studies have identified precautions to take when using the microprobe sam-

pling technique. The primary objective is to eliminate chemical reactions within the

probe and sampling lines. A combination of rapidly reducing temperature and pressure

in the probe helps meet this objective.

Kassem and coworkers [9] studied the effect of microprobe cooling on fuel-rich, laminar

flat flames of chlorinated hydrocarbons. Their results indicate that cooled probes, as

opposed to uncooled probes, provided more accurate profiles of species concentration.

Schoenung and Hanson [5] showed that carbon monoxide (CO) measurements in the

post-flame region of a premixed methane/air flame were affected by the pressure within

the probe and sampling lines. Their results indicate that the CO concentration increases

as the pressure in the probe decreases, with the concentration reaching the actual value

15KNF oil-free dual-staged pump Model UN035.3 ST11 with heated heads16Unique Heated Products Instrument Grade Heating Sample Line


around 50 mm Hg. This finding suggests that CO is converted to CO2 in the probe region

unless the pressure is 7 kPA or below. Therefore, low temperatures and pressures in the

probe are required to quench the reactions.

Fristrom and coworkers [10] argue that it is not rapid temperature drop that is re-

quired for successful flame sampling. Instead, a combination of rapid pressure drop and

the destruction of radicals on the probe walls is responsible for quenching reactions at

the probe tip. They explain that 2nd order molecule-radical reaction rates vary with

the square of the gas density, which varies linearly with pressure; therefore, the reac-

tion rate decreases with a decrease in pressure. Furthermore, they argue that if a rapid

temperature drop is induced while keeping pressure constant, then reactions with acti-

vation energies of 20 kJ/mol or lower will increase in rate. These reaction rates vary

quadratically with density, which varies inversely with temperature at constant pressure;

therefore, decreasing the temperature alone would not quench all flame reactions.

The error introduced during microprobe sampling of laminar flame species profiles is

a concern. The insertion of a microprobe into a flame can disturb the fluid flow, provide a

surface for quenching reactions, offer poor spatial resolution, and absorb thermal energy.

These effects are certainly not reproducible in modeling simulations, so some researchers

make corrections to the experimental data to compensate for errors that microprobes

introduce (e.g., shifting species profiles, increasing measured concentrations, etc.). A

recent study by Struckmeier et al. [11] compared the quality of species profiles obtained

using intrusive microprobes and nonintrusive optical techniques. The results indicate

that data acquired using both techniques agreed well, and therefore suggests that probe

sampling techniques are an indispensable technique for reliable experimental data. In

addition, the authors state that only minor corrections (e.g., shifting profiles by 1 mm

and measured concentrations by 30%) should be made to compensate for errors in the

experimental data.

One set of experiments in this study used a quartz microprobe fabricated in the

Department of Chemistry Glass Blowing Shop. The inner diameter (ID) of the probe

tip could not be precisely controlled; therefore, a number of probes were made and

measurements were taken to select the best one. To avoid errors associated with probe

fabrication and the subsequent measurement procedures, a new sampling probe apparatus

was also designed as described below. A previous study by Syed [12] determined the

appropriate probe tip size. The study measured CO2 concentrations in a propane-air

flame using probe tips with various IDs. Syed suggests that a probe tip with an ID


of approximately 150-250�m is ideal. Probes with larger IDs do not successfully quench

reactions at the probe tip. Probes with smaller IDs restrict gas flow through the sampling

line, such that it is not possible to obtain gas samples from the flame.

In addition to having a small ID, the probe tip must also be long enough to allow

for a large pressure drop. As mentioned previously, the microprobe is connected to a

doubled-headed pump via 1/4′′ tubing. If the suction pump head creates an absolute

pressure of 4-6 kPa in the sampling line17, then this is sufficient to quench reactions. At

such low sampling pressures, the probe is not cooled since previous studies have shown

that a rapid pressure drop alone provides accurate sampling [10, 5]. This study found

that a probe tip ID of 250�m and a length of 3.5-4.0 cm achieves the aforementioned

pressure range in the sampling line.

This study also designed a new type of microprobe apparatus that is low-cost, re-

producible, and easily reparable in case of probe tip breakage. This new probe also

eliminated flow field disturbances which were observed with the previous probe design

(refer to Chapters 7 and 9). A schematic of the microprobe setup is shown in Figure

4.6. The novelty of the design is the probe tip and the fittings used for connecting it to

the dual-stage pump via heated 1/4′′ stainless steel tubing. The probe tip is made of a

fused silica tubing typically found in gas chromatography applications. These commer-

cially available tubings are manufactured according to strict inner and outer diameter

specifications. This study used a fused silica tubing with an ID of 200�m and an OD of

360�m18. It should be noted that the fused silica tubing is manufactured with a poly-

imide resin outer coating which aids in sealing within couplings and provides flexibility to

the otherwise brittle silica material. When the probe tip is exposed to high temperatures

in a flame, this coating quickly burns off and does not contaminate the sampling system.

A stainless steel 1/4′′-to-1/16′′ coupling19 with a 1/16′′ graphite-reinforced composite re-

ducing ferrule20 connects the 360�m OD fused silica tubing to the 1/4′′ stainless steel

sampling line. The entire apparatus is capable of being heated to 350 ∘C.

Extra precautions were taken to eliminate leakage of gases from the surrounding

environment into the sampling line. Leaks into the sampling system dilute the sample

gas and lead to incorrect measurements. For this reason, Swagelok couplings are used for

17Gauge pressure measured by a vacuum pressure gauge18Agilent Deactivated Fused Silica Retention Gap19VICI Valco 1/4′′’ to 1/16′′ external-internal reducing20VICI Valco 1/16′′ one piece fused silica adapter, 0.35 mm tubing OD


Figure 4.6: Schematic of microprobe (not to scale)

all connections. Leaks into the sampling line were detected using a container filled with

dry ice (i.e solidified carbon dioxide). The container was placed near a suspected point

of leakage, and carbon dioxide gas fills the surrounding area. The NDIR analyzer (see

section 4.6.1) was connected to the sampling line and the dual-stage pump was turned

on. If a spike in CO2 concentration was observed on the analyzer, then there was a leak

present in the sampling line. The necessary steps were then carried out to eliminate the

leak (e.g., the couplings were changed, the tube was replaced, etc.).

4.5.2 Sampling Procedure

The sampling objective was to obtain samples at various points along the vertical axis

separating the two burner ports. Thus, the sliding stage, on which the microprobe is

mounted, was inserted between the two burner ports. A window was cut into the quartz

shroud enclosing the burner setup to permit insertion of the sampling probe. Figure 4.7 is

a schematic of the probe and burner setup. The tip of the probe was placed approximately

1.5 mm behind the central vertical axis separating the burner ports. This allows samples

to be withdrawn from the middle points of the flame region. After insertion, the probe

was held stationary, while the burner assembly was moved along the vertical axis with

the turn of a micrometer knob on the translation stage. One complete counterclockwise

rotation of the micrometer knob moves the burner assembly downwards by 0.5 mm.

A measuring system is required to define the exact position of the microprobe between

the two burner ports. For this purpose, the bottom (fuel) port was taken as the zero


Figure 4.7: Schematic of microprobe and burner setup (not to scale)

height, while the top (oxidizer) port was taken as the maximum height. The position

of the probe was zeroed by touching its tip to the bottom port. The reading on the

micrometer knob was noted as the zero distance. Each counterclockwise turn of the

micrometer knob moved the burner assembly down; thus, increasing the distance between

the probe and the fuel port (i.e., moving the probe upwards). The exact height at which

the probe withdraws samples is equal to the total distance plus the outer radius of the

probe.

Once the probe was positioned at the desired height, the dual-stage pump was turned

on and gases withdrawn from the flame fill the sampling line. The sampling lines were

purged before any analytical measurements were taken. The purge time varied depending

on the location of the probe in the flame. Sampling points away from the center of the

flame required short purge times (e.g., 15-20 minutes), since the low temperature, high

density gases permit high flowrates. In contrast, sampling points near the center of the

flame required longer purge times (e.g., 40-45 minutes), since gases in this high temper-

ature region have an extremely low density and permit low flowrates. The sampling line

filled with gases from the specified flame region were then ready for analysis.


4.6 Analytical Techniques

The hydrocarbon species and all oxygenated compounds were analyzed by a GC/FID.

Carbon dioxide and carbon monoxide were quantified by NDIR analysis. The flame

temperature was measured by an R-type thermocouple.

4.6.1 Non-Dispersive Infrared Analysis

NDIR analysis is a technique used to measure gas concentrations based on the energy

absorption characteristics of a gas in the infrared red region. The NDIR instrument

passes infrared light through two identical cells, in parallel, and then onto a detector.

The first cell is filled with nitrogen, which does not absorb the light and serves as a

reference cell. The second cell contains the sample gas, which absorbs infrared energy.

The detector measures the difference in energy between the two streams of light. This

difference is the absorption, which is proportional to the concentration of sample gas by

the Beer-Lambert Law in Equation 4.3:

A = � ⋅ b ⋅ c (4.3)

where

A is the gas absorbance.

� is the molar extinction coefficient (concentration−1 ⋅ length−1).

b is the path length that the beam travels in the sampling tube.

c is the gas concentration.

CO and CO2 Measurements

The NDIR instrument21 was used to quantify levels of CO and CO2 in the flame samples.

The instrument is capable of measuring concentrations from 0% to 40%. Initially, the

instrument is zeroed and calibrated. To zero the instrument, nitrogen was flowed through

the unit and the unit was zeroed. Next, the unit was calibrated with a gas mixture

containing 9.9% CO and 9.9% CO2. The gas mixture was passed through the detector,

and the span was set to match the calibration gas concentration.

Measurements were taken using the dual-stage pump to withdraw samples from the

flame and then pushing them towards the NDIR analyzer. Water, which is damaging

21NOVA NDIR Analyzer


to the analyzer, was removed from the sample gas by passing it through a cooling box

and a coalescing filter. The analyzer’s built-in pump was not used during sampling. The

concentrations were recorded once the displayed reading remains constant for 5 minutes.

As mentioned previously, samples withdrawn from points away from the flame center

have higher flowrates than samples from within the middle of the flame. Therefore, the

analyzer’s display reading stabilized much quicker for the former (e.g., 15-20 minutes)

than the latter (e.g., 40-45 minutes).

4.6.2 Gas Chromatography

Gas chromatography (GC) is a technique used for separating volatile organic compounds

based on their differences in partitioning between a flowing mobile phase and a stationary

phase. In this study, the GC method was used to measure C1 - C8 hydrocarbons and

C1 - C11 oxygenated species (e.g., esters, aldehydes, alcohols, etc.) A GC instrument

consists of a flowing mobile phase (carrier gas), a stationary phase (separation column),

an injection port, an oven, and a detector.

The carrier gas carries the sample gas through the separation column, and compounds

are separated due to partitioning between the two phases. Since partitioning behaviour is

a strong function of temperature, the separation column is placed inside a temperature-

controlled oven. Separation of compounds with a range of boiling points is achieved by

starting at low temperatures and then increasing the temperature until high boiling point

compounds are eluted. The injection port is always maintained at a temperature higher

than the boiling point of the least volatile compound in the mixture.

The amount of time a given component spends in the separation column is called the

retention time. The retention time of a given component remains the same provided the

mobile phase, stationary phase, temperature control, and gas flowrates remain constant.

As each component of the separated sample falls onto the detector, a quantitative re-

sponse in the form of a peak is generated. A series of peaks with the retention time on

the x-axis and the detector (e.g., voltage) on the y-axis is called a chromatogram. The

peak retention time is used to identify each compound, and the peak area is used to

determine the quantity of the compound.

The instrument used in these experiments is a Varian 3800 GC 22 with electronic

flow controllers, a 1079 injector, a methanizer, and two flame ionization detector (FID)s.

22Remotely controlled by a PC using STAR Chromatography Workstation 6.41


The FID consists of a hydrogen/air flame and a collector plate. As gases flow from the

separation column, the flame burns organic molecules to produce ions. The ions are

attracted to the collector plate, which generates a voltage depending on the quantity of

ions collected. Additional details of the GC setup are discussed in the follow subsections.

GC Carrier Gas

The carrier gas (mobile phase) used for the GC was 99.997 % helium. Hydrocarbon

and oxygen traps were placed before the column to filter out any contaminants from the

carrier gas. The flowrate of helium through the separation column was varied depending

on the compounds being studied. Details are available in subsection “GC Measurement

Procedures” below.

Injection System

The purpose of the injection system is to load the sample gas onto the separation column.

It consists of a sample loop, gas sampling valve (GSV), and an injector. Flame samples

were pushed into the GC sampling loop by the dual-stage pump. After the sample loop

was purged and the sample was ready for analysis, the GSV 23 rotated and delivered the

sample to the GC.

The GSV is a 10 port rotary valve which directs the sample and carrier gases into

the injector. The GSV has two positions; the fill position and the load position. In the

fill position, the sample gas flows through the 0.25 mL sample loop, while helium carrier

gas flows to the injector and separation column. As the GSV turns to the load position,

the sample trapped in the loop comes in-line with the carrier gas flow. The sample is

carried through into the injector and is directed into the column. The GSV’s duration

in the load position is a user controlled parameter, which is set to 2 minutes in these

experiments. As the GSV returns to the fill position, the normal flow pattern resumes.

The GC has a 1079 universal capillary injector, which can be run in several modes

based on the type of injector insert used. The injector temperature was set at 250 ∘C to

prevent condensation of sample components. An unpacked 3.4 mm ID insert for splitless

mode operation was used. The sample gas in the injector can be introduced to the column

via split mode or splitless mode. In splitless mode, the entire sample is loaded onto the

column. This mode is advantageous for detecting trace compounds in the sample gas.

23VICI Valco 10 port valve with air actuator


However, highly concentrated samples can damage the separation column, so split mode

can be used to load only a portion of the sample onto the column. In these experiments,

a dual column GC method was used, so splitless injection was employed to maximize the

amount of analyte reaching the columns.

GC Measurement Procedures

Two different GC measurement procedures have been developed to analyze the gaseous

samples. The first method was used to study a number of hydrocarbon compounds and

oxygenates in all the flames presented earlier. However, this method was not suitable for

analyzing fatty acid methyl ester compounds because the columns used are not capable

of separating them. Therefore, a second method was developed using a column suitable

for fatty acid methyl ester separation.

The first method used two separation columns, a methanizer, and two FIDs to study

a number of hydrocarbon and oxygenated compounds in a single run. A schematic of

this method is shown in Figure 4.8 and the specific operating parameters are in Table

4.3. As the sample passes through the injector, it enters a 0.5 meter fused-silica retention

gap24. The sample then passes through a y-splitter25 where it is split to two columns

for separation. C1-C8 hydrocarbons are separated on a non-polar phase HP-Al/S PLOT

capillary column26, and then detected on the front FID. Aldehydes, ketones, and alcohols

are separated on a polar phase Poraplot U capillary column27, and then passed through

a methanizer before detection.

The FID provides a detector response proportional to the analyte’s concentration and

number of carbon atoms. For example, 1 mole methane would produce exactly 1/2 the

detector signal of 1 mole ethane, and 1/3 the signal of 1 mol propane. Schofield [13] has

provided a table of molar response factors for a number of hydrocarbons and oxygenates.

Oxygenated species provide lower responses on an FID because carbon-oxygen bonds are

not broken in the flame. Therefore, species such as formaldehyde and carbon monoxide

provide zero response on an FID, while higher oxygenated hydrocarbons provide weaker

responses than their non-oxygenated counterparts. For example, 1 mole acetaldehyde

has two carbon atoms but produces the same detector signal of 1 mole of methane. In

24Varian 0.53mm ID retention gap methyl deactivated25Varian universal quick seal splitter26Agilent Technologies HP-Al/S 50 m x 0.53 mm (L x ID)27Varian Poraplot U 25m x 0.53mm (L x ID)


order to improve the FID’s response to oxygenated hydrocarbons, this study passed the

separated sample gas through a methanizer prior to the FID. The methanizer is a 1/16′′

stainless steel tube packed with a powdered nickel catalyst. The methanizer is heated to

380 ∘C and requires a constant flow of hydrogen gas. As the sample flows through the

methanizer, the nickel catalyst breaks the carbon-oxygen bonds and then saturates them

with hydrogen. Therefore, all carbon-oxygen bonds are effectively converted to carbon-

hydrogen bonds, and the analyte now appears on the FID as an alkane with the original

number of carbon atoms. For example, formaldehyde appears as methane, acetaldehyde

appears as ethane, etc.

FrontFIDGSV

Sample in

1079 injector

GC column oven

Retention gap

Plot column

Poraplot Ucolumn

Y-splitter

RearFID

methanizer

H2 Air He

Figure 4.8: Schematic of dual column GC Setup

The second GC method used in these experiments was designed to analyze fatty acid

methyl ester compounds in the methyl decanoate flame using a polar phase DB-WAX col-

umn28. This method used only one column and one FID. The GC parameters are provided

in Table 4.4. The method was capable of separating and identifying methyl propanoate,

methyl 2-proepnoate, methyl butanoate, methyl 2-butenoate, methyl 3-butenoate, methyl

pentanoate, methyl 4-pentenoate, methyl hexanoate, methyl 5-hexenoate, methyl hep-

tanoate, methyl 6-heptenoate, methyl octanoate, methyl 7-octenoate, methyl 2-octenoate,

and methyl decanoate.

28Agilent Technologies DB-WAX 30 m x 0.53 mm (L x ID)


Table 4.3: Dual Column GC Method Parameters

Front Injector 250 ∘C

2.0 mL/min He flow to column

Splitless injection

Gas Sampling Valve 200 ∘C

Switch to sampling position for 2 min

Column Oven Program 50 ∘C, hold for 3 min

20 ∘C/min to 150 ∘C, hold for 5 min


Front FID 300 ∘C

30 mL/min H2, 300 mL/min air

26 mL/min He makeup

Rear FID 300 ∘C


28 mL/min He makeup

Methanizer 380 ∘C

20 mL/min H2

GC Calibration Procedure

The GC was calibrated for hydrocarbons using four different Scotty calibration gas mix-

tures: 100 ppm C1-C6 alkanes in nitrogen; 1000 ppm C2-C6 alkenes in nitrogen; 15 ppm

C2-C4 alkynes in nitrogen; and 100 ppm benzene in air. The calibration was performed

by flowing each gas mixture directly into the GC sample loop. The operating condi-

tions for the GC were identical to those mentioned above for the dual column method.

Each calibration gas mixture was used to determine the retention time for each species,

thereby providing qualitative information. The retention times for 1-heptene, 1-octene,

methanol, ethanol, methyl-2-propenoate, methyl-3-butenoate, methyl-4-pentenoate, and

methyl-5-hexenoate were obtained by injecting the saturated vapor above the pure com-

ponent liquid. Quantitative calibration was performed using the 1000 ppm mixture of

ethylene, propene, 1-butene, 1-pentene, and 1-hexene to obtain the FID’s response sig-

nal. The response signal for equivalent concentrations of other hydrocarbons was then

calculated using Schofield’s [13] table of molar response factors for hydrocarbon species,

as summarized in Table 4.5. For example, if the FID’s measured response for 1000 ppm


Table 4.4: GC Method Parameters for Fatty Acid Methyl Esters

Front Injector 250 ∘C

0.5 mL/min He flow to column

Splitless injection

Gas Sampling Valve 200 ∘C

Switch to sampling position for 2 min

Column Oven Program 50 ∘C


Front FID 300 ∘C


26 mL/min He makeup

ethylene was 60,000 counts, then the response for 1000 ppm methane was set at 30,000

counts since the FID carbon number for methane is 1.0 and that of ethylene is 2.0.

GC calibration for the aldehyde and ketone species was difficult because gas cylinders

are not reliable for calibration. These compounds are highly reactive and susceptible to

rapid degradation when stored in gas cylinders. In this study, aldehydes and ketones

species were calibrated using permeation devices. The permeation device is an inert per-

meable tube filled with a pure chemical compound in gas-liquid or gas-solid equilibrium.

When heated to a specified temperature in a passivated glass-coated chamber, the device

emits the compound through the permeable tube wall at a constant mass flow rate (e.g.,

ng/min). An inert carrier gas sweeps over the permeation tube at a constant flow rate

to generate a gas mixture with a known concentration.

Permeation devices were used for calibration of formaldehyde, acetaldehyde, acetone,

propanal, butanal, and acrolein. A schematic of the permeation tube calibration setup

is shown in Figure 4.9. The permeation device was placed within an oven 29 to maintain

the device at a constant temperature. The nitrogen carrier gas was first purified and then

delivered to the oven chamber using a mass flow controller30. A calibration gas mixture

was generated as the carrier gas swept over the permeation device, and this mixture was

sent to the GC. Various calibration concentrations were established by varying the carrier

gas flowrate. The concentration of the gas mixture was determined using Equation 4.4.

29VICI Dynacalibrator Model 15030Brooks Mass Flow Controller Model 5850E


Table 4.5: Measured FID relative molar response factors for organic molecules [13]

Molecule FID relative molar response factor

Methane 1.0

Ethane, Ethylene 2.0

Acetylene 2.2

Propane, Propylene 3.0

Propyne 3.4

Butane, Isobutane 4.0

1-Butene, trans-2-Butene, Cis-2-Butene 4.0

1-Butyne, 2-Butyne, 1,3 Butadiene 4.0

Pentane, 1-Pentene 5.0

Hexane, 1-Hexene 6.0

Benzene 6.0

1-Heptene 7.0

1-Octene 8.0

C =P ⋅ (24.46/mw)

Fc(4.4)

where

C is the concentration in ppm by mole

mw is the molecular weight of the compound in g/mole

P is the permeation rate in ng/min (provided by the manufacturer)

Fc is the total flow of the carrier gas in mL/min

24.46 L is the molar volume of nitrogen at STP

GC calibration for the biofuels (i.e., n-butanol, methyl octanoate, and methyl de-

canoate) involved using the fuel delivery and burner setup because gas cylinders or per-

meation tubes were not available. The liquid fuels were pumped into the vaporization and

diluted with nitrogen gas. The exact concentration of the nitrogen-oxygenate mixture

was determined from the nitrogen gas flowrate and the liquid flow rate into the mixing

column. This “calibration gas” then flowed to the bottom burner port, and samples were

obtained using the microprobe sampling technique. The aforementioned GC sampling

methods were used to calibrate the system.


permeation device

N2

gas purifier

mass flow controller

to GC

oven

90 °C

Figure 4.9: Schematic of the permeation tube setup

4.6.3 Temperature Measurement

The flame temperature profile was obtained by measuring the local temperature at var-

ious regions in the flame using an apparatus similar to that described by McEnally et

al. [14]. The measurements were obtained by the most direct method: inserting a ther-

mocouple into the flame. The thermocouple is small compared to the thickness of the

flame front so it does not disturb the flame. The drawbacks of using a thermocouple are

the aerodynamic wake behind the flame front and the possible catalytic activity of the

thermocouple material[10].

The thermocouple measures the temperature by employing the difference in thermo

electrical properties of different metals. When two dissimilar conductors are welded to-

gether and the junction is place in a hot spot, an electric potential is generated which is

proportional to the difference in temperature between the hot and cold junction. Thermo-

couples made of thin noble metal wires such as platinum and rhodium are advantageous

for the following reasons: they allow a high resolution to be obtained; the aerodynamic

disturbance of the flame front is minimized; and the materials can withstand high tem-

perature environments [10].


A schematic of the thermocouple apparatus is shown in Figure 4.10. The flame

temperature was measured by inserting the apparatus between the two burners. The

two R-type thermocouple wires31 (legs) are made of dissimilar metals butt-welded at a

junction. The positive leg is made of pure platinum while the negative leg is comprised of

87% platinum and 13% rhodium. Each wire’s diameter is 254 �m while the butt-welded

junction is approximately 500 �m in diameter. The wires are housed in ceramic tubes

which provide support while spreading the wires apart. The junction lies between the

ceramic tubes and it is placed where the temperature is measured,. At the opposite end,

the ceramic tubes are attached to an aluminum plate which is mounted on a sliding rail.

One tube is fixed in position with a lock nut while the other tube rotates freely upon a

threaded bolt. A spring pulls the free tube towards the fixed tube at one to keep the

wires taught at the other end. The positive and negative legs are connected to an R-type

extension wire which carries the measured signal to a digital thermometer32.

R-type extension wire

_

+

Thermocouple wires

Fixed ceramic tube

Freely rotating ceramic tube

Thermocouple junction

SpringBase plate

Digital reader

Figure 4.10: Thermocouple Schematic

31Omega R-type butt-welded unsheathed fine-gauge thermocouple wires32Digisense DualLog R Thermocouple Thermometer


Correction for Radiation Losses

The temperature measured by the thermocouple differs from the true flame temperature

due to aerodynamic, thermal, and/or chemical perturbations. The methods of minimizing

the effect of these perturbations are discussed in detail by Fristrom and Westenberg [6].

However, even if all disturbances are minimized, the thermocouple will register a different

temperature than the true stream temperature due to radiation losses. Correcting for

these losses is estimated by equating the heat transferred to the thermocouple from the

gas to the heat lost by radiation from the wires. The equation given for a spherical device

(i.e., Nusselt number of 2) is:

Tg − Tc =� ⋅ � ⋅ d ⋅ (T 4

c − T 4w)

2 ⋅ k(4.5)

where

Tg is the true gas temperature (K)

Tc is the measured gas temperature (K)

� is the emissivity of the thermocouple element (dimensionless)

� is the Stefan-Boltzmann Constant = 5.67x10−08 ( Wm2⋅K4 )

d is the wire diameter (m)

k is the thermal conductivity of gas ( Wm⋅K )

Tw is the wall (ambient) temp. to which heat is radiated = 300 (K)

The thermal conductivity, k, of the gases was estimated as the thermal conductivity of

air at the measured temperature. The thermal conductivity of air at various temperatures

was obtained from the CRC Handbook [3]. Linear interpolation and extrapolation was

used to determine thermal conductivity at temperatures not listed in the handbook.

The emissivity, �, of the thermocouple element was obtained from the study by

Bradley and Entwistle [15].

Literature Cited

[1] S. Sarathy, “Using an opposed flow diffusion flame to study the oxidation of C4 fatty

acid methyl esters,” Master’s thesis, University of Toronto, 2006.

[2] I. Glassman, Combustion, 3rd ed. San Diego, CA: Academic Press, 1996.



[4] K. Seshadri, T. Lu, O. Herbinet, S. B. Humer, U. Niemann, W. J. Pitz, R. Seiser,

and C. K. Law, “Experimental and kinetic modeling study of extinction and ignition

of methyl decanoate in laminar non-premixed flows,” Proceedings of the Combustion

Institute, vol. 32, no. Part 1, pp. 1067–1074, 2009.

[5] S. M. Schoenung and R. K. Hanson, “CO and temperature measurements in a flat

flame by laser absorption spectroscopy and probe techniques.” Combustion Science

and Technology, vol. 24, no. 5-6, pp. 227 – 237, 1981.

[6] R. M. Fristrom, “Comments on quenching mechanisms in the microprobe sampling

of flames,” Combustion and Flame, vol. 50, pp. 239–242, 1983.

[7] A. M. Vincitore and S. M. Senkan, “Polycyclic aromatic hydrocarbon formation in

opposed flow diffusion flames of ethane,” Combustion and Flame, vol. 114, no. 1-2,

pp. 259–266, July 1998.

[8] A. Sinha and M. J. Thomson, “The chemical structures of opposed flow diffu-

sion flames of c3 oxygenated hydrocarbons (isopropanol, dimethoxy methane, and

dimethyl carbonate) and their mixtures,” Combustion and Flame, vol. 136, no. 4,

pp. 548–556, March 2004.

69

Literature Cited 70

[9] S. S. M. Kassem, M. Qun, “Chemical structure of fuel-rich 1,2-

C2H4Cl2/CH4/O2/Ar flames: Effects of micro-probe cooling on the sampling

of flames of chlorinated hydrocarbons,” Combustion Science and Technology,

vol. 67, pp. 147–157, 1989.

[10] R. M. Fristrom, Flame structure. New York: McGraw-Hill, 1965.

[11] U. Struckmeier, P. Osswald, T. Kasper, L. Boehling, M. Heusing, M. Koehler,

A. Brockhinke, and K. Kohse-Hoeinghaus, “Sampling Probe Influences on Temper-

ature and Species Concentrations in Molecular Beam Mass Spectroscopic Investiga-

tions of Flat Premixed Low-pressure Flames,” Zeitschrift Fur Physikalische Chemie-

International Journal of Research in Physical Chemistry & Chemical Physics, vol.

223, no. 4-5, pp. 503–537, 2009.

[12] S. A. Syed, “Oxidation studies of surrogate bio-diesel fuels in opposed flow diffusion

flames,” 2005.

[13] K. Schofield, “The enigmatic mechanism of the flame ionization detector: Its over-

looked implications for fossil fuel combustion modeling,” Progress in Energy and

Combustion Science, vol. 34, no. 3, pp. 330–350, June 2008.

[14] C. McEnally, U. Koylu, L. Pfefferle, and D. Rosner, “Soot volume fraction and

temperature measurements in laminar nonpremixed flames using thermocouples,”

Combustion and Flame, vol. 109, no. 4, pp. 701–720, June 1997.

[15] D. Bradley and A. Entwistle, “Deterimination of the emissivity, for total radiation,

of small diameter platinum-10% rhodium wires in the temperature range 600-450

degrees C,” British Journal of Applied Physics, vol. 12, pp. 708–711, December 1961.

Part II

Biobutanol

71

Chapter 5

Background

In 2006, British Petroleum (BP) and DuPont announced that they would start selling

biobutanol made from sugar beets, as a gasoline blending component in the United

Kingdom [1]. The biobutanol, would be produced using a fermentation process similar

to that of ethanol, and its feedstock include sugar beet, sugar cane, corn, wheat, and

potentially lignocellulosic biomass. Proponents [2, 3, 4] of biobutanol highlight many

characteristics that make it a superior biofuel, such as:

∙ the ability for blending with gasoline at higher concentrations than ethanol without

modifying current vehicle and engine technologies;

∙ a higher energy density than ethanol, which provides better fuel economy at higher

blend ratios;

∙ enhanced water tolerance compared to ethanol, allowing use of the existing fuel

distribution infrastructure;

∙ the use of existing feedstock and refineries (with minor modifications) used for

bioethanol production;

∙ the potential to produce from lignocellulosic and waste biomass feedstock;

∙ and a lower vapour pressure than ethanol and gasoline, thereby decreasing volatile

organic compounds (VOC) emissions.

It is apparent that biobutanol is being proposed as an eventual replacement for the

bioethanol currently used in SI engines. BP and DuPont stated that a complete environ-

mental LCA based on actual manufacturing design models is underway, but to date no

72


results have been published [5]. If biobutanol is to replace bioethanol, then its sustain-

ability needs to be critically assessed. In addition, the combustion kinetics of biobutanol

must be understood to aid in the design of combustion systems. This chapter provides

background information relevant to the development of an LCA and a chemical kinetic

mechanism for biobutanol.

5.1 Biobutanol History

The production of biobutanol from biomass feedstock is not novel. Jones and Woods [6]

have provided a detailed history of biobutanol production via fermentation. Figure 5.1

is a visual representation of this history and following is a summary.

Figure 5.1: Timeline of biobutanol history

The fermentation of sugars to biobutanol, specifically the isomer n-butanol 1, was first

documented by Louis Pasteur in 1861, although the yields he achieved were not sufficient

for commercialization. In the early 1900s, a German born chemist, Chaim Weizmann, was

attempting to produce butanol for synthetic rubber production, and discovered that the

Clostridium acetobutylicum organism was capable of converting large amounts of sugars

into a mixture of acetone-butanol-ethanol (ABE) in the molar ratio of 3:6:1. Weizmann

1Henceforth, the terms biobutanol, n-butanol, and butanol are used interchangeably


had performed his pioneering work while living in England, and at the onset of World

War 1, the English sought his help in producing bioacetone from biomass as a precursor

to cordite, a smokeless propellant used in munition. During the war, a large amount of

ABE was produced in England, Canada, and the U.S. from feedstock such as corn and

sugar beet molasses. During this period, the unwanted butanol was stored in large vats

waiting for some commercial use.

After World War 1, the automotive industry in the U.S. was rapidly growing and

the demand for paint lacquers increased dramatically. One company commercialized a

method of producing high quality lacquers using butanol and its ester, butyl acetate, as

solvents. Thus, in the 1920s and 1930s, there was a large industry built around convert-

ing biomass sugars into ABE for the automotive paints industry. In a 1927 Scientific

American article, Killeffer [7] states that the butanol production in the U.S. doubled to

60 tons per day in just 18 months.

In 1938, World War 2 began and all the U.S. fermentation facilities reverted to pro-

ducing acetone for cordite manufacturing. Japan, South Africa, India, Australia, China

and the U.S.S.R also opened a number of ABE fermentation facilities using wheat, rye,

and corn as feedstock. After World War 2, there was a rapid decline in ABE production

from biomass for two reasons. The cost of biomass feedstock rose sharply as corn, wheat,

and molasses became staples for animal feed. In addition, the petrochemical industry

boomed in the 1950s and 1960s, leading to superior solvents for use in automotive paint

lacquers. The demand for butanol decreased sharply, and by the 1980s virtually all the

ABE plants in the world had closed.

5.2 Biobutanol Production

2 Butanol can be produced from the same feedstock as ethanol (e.g., sugar beet, sugar

cane, corn, wheat, and lignocellulosic biomass) using a fermentation process. During

the first part of the last century, the production of butanol was a large-scale industrial

process. The conventional production process used C. acetobutylicum to produce acetone,

n-butanol, and ethanol (termed the ABE process). Almost two thirds of the total butanol

produced in the late 1940s was produced by fermentation. The traditional fermentation

process could not compete economically with the production of butanol from petroleum

2This sections includes background research conducted by S.M. Sarathy and Y. Zhang (PhD Candi-date, Dept. of Civil Engineering, University of Toronto).


due primarily to high feedstock (e.g., corn) costs and the large energy requirements for

butanol recovery [6].

The traditional process for production of butanol and ethanol from corn is as follows,

[8, 9]:

1. The corn crop is cultivated and milled.

2. The milled corn is slurried with water. For ethanol, the milled corn undergoes liq-

uefaction and sacharrification to convert starches to monosaccharides. This process

is not required for butanol because the fermentation organism is capable converting

starches to ABE.

3. The slurried mixture is then fermented in a batch reactor to convert sugars (and

starches) into a beer solution containing the desired product(s).

4. The desired solvent, or biofuel, is then recovered from the beer by a series of

separation processes.

The major consumer of energy in the production of butanol and ethanol are the

separation processes (i.e., distillation) required for product recovery. The production of

butanol via fermentation is severely handicapped due to the low concentrations of butanol

in the fermented beer. The C. acetobutylicum used to convert starches and sugars into

ABE cannot tolerate high solvent concentrations, so typical end-product concentration

is 20 g of ABE per liter of beer [8].

Several recent advances have occurred in butanol production, including the develop-

ment of strain, C. beijerinckii BA 101, with increased solvent tolerance to 33g/L total

solvents [10, 11], and the development of advanced fermentation techniques and down-

stream product recovery processes [12]. Environmental Energy Inc. (EEI), now Butyl-

Fuels LLC, has patented a novel dual-stage process and claimed the process significantly

improves butanol yield and minimizes undesired byproducts [3]. Green Biologics, a com-

pany based in the United Kingdom, has obtained significant funding to commercialize

the conventional ABE fermentation process by utilizing waste feedstock and advanced

fermentation and separation processes [4].

Historically, starch and sugar feedstock have been utilized for butanol production,

however, butanol could be produced from lignocellulosic biomass, a more abundant feed-

stock. During the last several decades, research has been conducted to convert lignocel-

lulosic biomass to butanol. Yu et al. [13] report that C. acetobutylicum can convert both


hexose and pentose sugars present in biomass hydrolyzates to butanol. Recently, Zverlov

et al. [14] documented the production of butanol from hydrolyzates of lignocellulosic

waste at a full-scale industrial plant in the 1980s in Russia.

Current metabolic engineering research is attempting to resolve the end-product in-

hibition associated with the conventional ABE process, so that higher yields of butanol

can be achieved. BP and DuPont disclosed that their partnership is patenting novel

biocatalysts for high yield production of n-butanol, as well as its higher octane isomers,

sec-butanol and iso-butanol [5]. Atsumi et al. are genetically modifying the metabolic

pathways of E. Coli to develop non-fermentative pathways for biofuel n-butanol, sec-

butanol, and iso-butanol production [15].

The aforementioned genetic engineering research may lead to novel biobutanol pro-

duction technologies in the long term (e.g., 15-20 years); however, in the short term (e.g.,

5-10 years), biobutanol could only be produced at industrial scales by reintroducing the

traditional fermentation technology, albeit with process improvements. Presently, there

is no commercial-scale production of biobutanol from starch, sugar or lignocellulosic feed-

stock and there is limited data on the processes. In spite of this, there is renewed interest

in butanol, particularly considering its apparent attractiveness as an automotive fuel.

5.3 Biobutanol Fuel Properties

3Although limited research has been conducted on the use of butanol in vehicles, re-

cent testing by BP found that butanol has several fuel property advantages compared

to ethanol [16]. Selected fuel properties are presented in Table 5.1. Butanol has a

higher energy density (i.e., LHV) than ethanol, which leads to better vehicle fuel econ-

omy. While ethanol has a higher octane rating than gasoline, butanol is octane neutral,

thus lessening requirements for additional fuel modification when blending with gasoline.

The lower oxygen content of butanol allows it to be blended at higher proportions than

ethanol without requiring engine modifications or exceeding current blending regulations.

When blended with gasoline, butanol contributes much less to the reid vapour pressure

equivalent (RVP) than ethanol, even having a negative impact on the RVP when bu-

tanol and ethanol are co-blended. Although ethanol/gasoline blends were found to lead

to distillation curve abnormalities, which may negatively impact driveability, this was

3This sections includes background research conducted by S.M. Sarathy and S. Sleep (UndergraduateResearch Assistant, Dept. of Civil Engineering, University of Toronto).


Table 5.1: Selected fuel properties of butanol, ethanol, and gasoline

n-Butanol Ethanol Gasoline

Chemical Formula C4H9OH C2H5OH C4 to C12 hydrocarbons

Energy Density (LHV)(MJ/L) 26.9 21.2 32.2-32.9

Fuel Density at 20 ∘C (kg/L) 0.81 0.79 0.72-075

Boiling Point (∘C) 117 78 <210

Octane Number (R+M/2)a 87 116 87

RVP at 10% v/v, (kPa) 34 130 <60/90b

Oxygen (%wt) 21.6 34.7 <2.7

a Octane number of alcohols is given for blends containing 5 vol% alcohol in

gasoline.b Summer/Winter specifications provided for gasoline DVPE.

not the case with butanol/gasoline blends, as no vapour pressure or distillation curve

abnormalities were noted. Butanol, unlike ethanol, is immiscible with water, so it can

be distributed in existing pipelines without the risk of water contamination; ethanol re-

quires an alternate distribution infrastructure. In addition, butanol is not corrosive to

engine components while ethanol corrodes copper and brass. Preliminary results of BP’s

vehicle testing of gasoline/butanol blends containing 5% and 10% butanol showed no

significant changes in carbon monoxide, hydrocarbon, and NOx emissions compared to

regular gasoline and gasoline/ethanol blends containing 5% ethanol. These combined fac-

tors indicates that butanol may be a superior gasoline blending component than ethanol.

However, further combustion testing and evaluation of the life cycle performance of bu-

tanol compared with ethanol and gasoline are required.

Literature Cited

[1] G. Hess, “BP and DuPont to make biobutanol,” Chemical & Engineering News,

vol. 84, pp. 9–10, 2006.

[2] BP-DuPont, “Bp and duPont biofuels fact sheet,” British Petroluem and DuPont,

Tech. Rep., June 2006.

[3] D. Ramey and S. Yang, “Production of butyric acid and butanol from biomass,”

US Department of Energy, Morgantown, Washington, Tech. Rep. DE-F-G02-

00ER86106, 2004.

[4] G. Clark, “Green biologics secures 1.58 million British pounds to develop next gen-

eration biofuel,” Biofuel Review, vol. October, 2007.

[5] BP and DuPont, “DuPont and BP disclose advanced biofuels partnership targeting

multiple butanol molecules,” BP America Press Release, 2008.

[6] D. Jones and D. Woods, “Acetone-butanol fermentation revisited,” Microbiological

Reviews, vol. 50, no. 4, pp. 484–524, December 1986.

[7] D. Killeffer, “A microbe in international affairs,” Scientific American, 1927.

[8] N. Qureshi and H. Blaschek, “Economics of butanol fermentation using hyper-

butanol producing clostridium beijerinckii ba101,” The Institution of Chemical En-

gineers, vol. 78, pp. 139–144, 2000.

[9] J. R. Kwiatkowski, A. J. McAloon, F. Taylor, and D. B. Johnston, “Modeling the

process and costs of fuel ethanol production by the corn dry-grind process,” Indus-

trial Crops and Products, pp. 288–296, 2006.

78

Literature Cited 79

[10] T. Ezeji, N. Qureshi, and H. Blaschek, “Production of acetone, butanol and ethanol

by clostridium beijerinckii ba101 and in situ recovery by gas stripping,” World Jour-

nal of Microbiology & Biotechnology, vol. 19, pp. 595–603, 2003.

[11] N. Qureshi and H. Blaschek, “Evaluation of recent advances in butanol fermentation,

upstream, and downstream processions.” Bioprocess and Biosystems Engineering,

vol. 24, pp. 219–226, 2001.

[12] T. Ezeji, N. Qureshi, and H. Blaschek, “Bioproduction of butanol from biomass:

from genes to bioreactors.” Current Opinion in Biotechnology, vol. 18, pp. 220–227,

2007.

[13] E. K. C. Yu, L. Deschatelets, and J. Saddler, “The bioconversion of wood hy-

drolyzates to butanol and butanediol,” Biotechnology Letters, vol. 6, pp. 327–332,

1984.

[14] V. Zverlov, O. Berezina, G. Velikodvorskaya, and W. Schwarz, “Bacterial acetone

and butanol production by industrial fermentation in the soviet union: use of hy-

drolyzed agricultural waste for biorefinery,” Applied Microbiology Biotechnology,

vol. 71, pp. 687–597, 2006.

[15] S. Atsumi, T. Hanai, and J. Liao, “Non-fermentative pathways for synthesis of

branched-chain higher alcohols as biofuels,” Nature, vol. 451, pp. 86–89, 2008.

[16] L. Wolf. (2007, March) 1-butanol as a gasoline blending biocomponent.

U.S. Environmental Protection Agency Clean Air Act Advisory Com-

mittee Mobile Sources Technical Review Subcommittee. [Online]. Available:

http://www.epa.gov/air/caaac/mstrs/March2007/Wolf.pdf

Chapter 6

LCA of Biobutanol for use in

Transportation

6.1 Introduction

Ethanol from sugarcane and corn, and biodiesel from soybean and rapeseed are being

commercially produced today; however, the biofuels of choice for the future and their

methods of production are still uncertain [1, 2]. Initiatives to substantially increase bio-

fuel production and upcoming low carbon fuel standards motivate a critical examination

of the environmental implications of current and potential future fuel production and

end use pathways.

Over the past two decades, there have been many studies examining the environmen-

tal performance of corn-based ethanol. However, few studies have been conducted on

corn-based butanol. It is only recently that butanol production technology has become

competitive due to improved product yields. The following subsections include a review

of important studies on bioethanol LCA, as well as some recently published LCA research

on biobutanol.

6.1.1 Bioethanol LCA

The sustainability of bioethanol has been under intense scrutiny by the scientific com-

munity. Pimentel and Patzek [3, 4] concluded that the total energy required to produce

corn-based ethanol is greater than the energy content of the ethanol produced. However,

the majority of studies [5, 6, 7, 8, 9, 10] indicate that corn-based ethanol can displace

80

Chapter 6. LCA of Biobutanol for use in Transportation 81

fossil energy sources and reduce GHG emissions. A study by Farrell et al. developed

a meta-model to evaluate various LCA studies [11] and demystify the aforementioned

discrepancies. The results indicate that Pimentel and Patzek [3, 4] used obsolete data

from ethanol production technologies in the early 1980’s, and the authors did not cor-

rectly allocate credits for the ethanol coproducts. Farrell et al. also made the following

recommendations for future LCA evaluations:

∙ use the displacement method to credit coproducts,

∙ obtain accurate and current data,

∙ clearly define future scenarios, and

∙ define performance metrics such as GHG emissions, fossil energy use, soil erosion,

etc.

Kim and Dale [8, 9] used the system expansion approach to credit coproducts and also

considered the carbon sequestration effect from increasing the soil organic carbon level

during biomass production. The system boundaries include upstream processes (e.g.,

fertilizer production and transport), ethanol production and coproducts by dry milling,

urea production, and the corn farming system. The study concludes that using ethanol

can reduce GHG emissions and fossil energy use.

Studies at the Argonne National Laboratory (ANL) [10, 12] used the Greenhouse

Gases, Regulated Emissions, and Energy Use in Transportation (GREET) model to

conclude that ethanol could reduce GHG emissions and fossil energy use. The authors

used the displacement method to assign credits to the fuel ethanol coproducts. The

results by Wu et al. [10] project that corn ethanol produced by the dry milling process in

the year 2012 reduces fossil energy use by 38% and GHG emissions by 21%. The GREET

model version 1.8 by ANL is an LCA model with detailed descriptions of all the data sets

and procedures. Therefore, it will be used in this study to determine the environmental

performance of biobutanol and to compare it with bioethanol.

It is clear from the literature that conventional ethanol production from food crops,

most notably corn, has limited potential [13, 14]. Reasons for this include competition

from the food industry [15], limited agricultural land for crop growth [13], and high

energy input requirements for agricultural chemicals and harvesting [14]. For biofuels

to live up to their potential as attractive alternative fuels, lignocellulosic biomass must


be considered. Consisting of energy crops, agricultural and forest residue as well as

other sources, lignocellulosic biomass offers advantages over food crop feedstock. These

advantages include: the ability to be cultivated on marginal agricultural land, lower

agricultural chemical requirements, lower energy requirements, and the potential of uti-

lizing the lignin portion of the biomass as an energy source for the production process.

For these reasons the net energy gains of lignocellulosic ethanol are much higher than

those of corn ethanol, and the resulting GHG emissions are much lower. Despite these

advantages, the conversion of lignocellulosic feedstock to fuels is not yet at commercial

scale, primarily because lignocellulosic biomass is more challenging to convert to alcohols

[13, 14, 15, 16].

Land Use Change and Food Security Issues

Recently, there have been a number of studies citing food security and land use change is-

sues associated with biofuel production. Prices of food, including corn and rice, increased

sharply in 2007 and 2008. Due to media sensationalisation, the rise in food prices was

largely attributed to the increasing diversion of food crops for biofuel use. In reality, the

rise in food prices can be attributed to a number of factors, including, ”increasing energy

costs, climate change, stagnation in crop productivity, and diversion of crops or crop-

lands to biofuel production” [17]. Furthermore, rising food prices may actually benefit

the largest population of undernourished individuals who are farmers [18]; however, the

urban poor would suffer.

As the value of biofuels and the price of their feedstock (i.e., corn) increase, farmers

worldwide are converting forests and grassland to cropland for additional grain produc-

tion. Searchinger et al. and Fargione et al. report [19, 20] that the conversion of carbon-

rich forest lands to cropland inevitably release CO2 emissions into the atmosphere. The

magnitude of the GHG released is much larger than the GHG reductions the biofuels offer

by displacing fossil fuels, suggesting that biofuels actually increase GHG emissions via

indirect land use change. Proponents of biofuels argue that the aforementioned studies

overestimate the carbon debt associated with biofuels. In any case, the use of biofuels

derived from traditional crops and croplands is likely unsustainable in the long term,

and therefore, it is vital that biofuels be produced from waste feedstock and/or feedstock

grown on degraded or abandoned land.


6.1.2 Biobutanol LCA

For all the research on ethanol, very little has been published on the life cycle implications

of butanol. S&T Squared Consultants Inc., a consulting company performed an LCA of

biobutanol using the GHGenius model[21]. During the course of the present study, Wu

et al. published results using the GREET model [22]. A comparison of the two studies

shows some inconsistent results primarily due to Wu et al. utilizing recent data [23, 24]

while S&T Squared Consultants Inc. utilized data for the same process (ABE) but from

the mid-1980s. Although both studies found that the butanol production process was

more energy intensive than that of ethanol, they differed substantially in the energy

inputs estimated for the butanol production. S&T Squared Consultants Inc.Squared

Consultants Inc. found that butanol production consumed more fossil energy than the

energy the butanol contained. Wu et al. concluded that butanol resulted in a net

energy gain with GHG emissions slightly higher than ethanol, but still 20% to 60% lower

than gasoline. They also found that the butanol benefits were very dependent on the

treatment of the acetone coproduct due to the coproduct credit scheme. It should be

noted that neither study considered lignocellulosic biomass feedstock, or the possible

benefit of using existing gasoline pipeline infrastructure for butanol distribution, as both

assumed delivery by diesel truck.

The objective of this study is to examine the potential attractiveness of butanol as

a transportation fuel to replace gasoline use in the U.S. Selected life cycle environmen-

tal metrics associated with the production of butanol from corn grains are compared

with ethanol using the same feedstock. The goal is to determine the fossil energy use,

petroleum energy use, and GHG emissions associated with the production and use of

corn-based butanol. The results of this study are also compared with those of a previous

study [22].

6.2 Methodology

The LCA [25] method is utilized to evaluate the environmental performance of butanol

and ethanol derived from corn in the U.S. The environmental metrics examined are fossil

and petroleum energy use and GHG emissions. The GHGs considered are CO2, CH4,

and N2O which are aggregated as CO2 equivalent (CO2-eq.) based on the 100-year global

warming potentials recommended by [26].


The butanol and ethanol LCA models consist of similar activities. The life cycle

begins with corn farming and the associated manufacture of upstream agronomic inputs

(i.e., fertilizers and herbicides). The corn is collected from the field and transported to a

conversion facility where it is converted to ethanol or butanol with associated coproducts.

The alcohol is then distributed to a facility for blending with gasoline. The life cycle of

the biofuel ends with the combustion of the alcohol/gasoline blend in an automobile.

Transportation between the various fuel production processes is also included in the

models. Figure 6.1 is a simplified diagram showing the life cycle of butanol, including its

use in a light duty vehicle (LDV).

This study divides the lifecycle into well-to-pump (WTP) and pump-to-wheel (PTW)

portions. The WTP portion includes the life cycle results solely associated with the

production and distribution of neat alcohol fuels, so as to distinguish from those when

gasoline is blended with them. The WTP results for neat alcohol are reported with

the functional unit (FU) being 1 MJ of alcohol available at the pump, where the pump

is defined as the gasoline blending facility. The PTW portion includes blending of the

biofuel with gasoline and subsequent processes, and this study reports the total well-to-

wheel (WTW) results for 85% alcohol and 15% gasoline blends. The FU for the WTW

analysis is one km driven by a midsize passenger vehicle.

6.2.1 Data sources and uncertainties

The often-cited, publicly available GREET 1.8b model contains corn ethanol and butanol

pathways [12]. The mix of energy sources (e.g., coal, natural gas, electricity, etc.) used

for biofuel production are considered the same for both ethanol and butanol, and the

values are the default presented in GREET 1.b. The comparison ethanol pathways are

obtained directly from this model and default values for the year 2010 are assumed. The

year 2010 was selected for the analysis as it is expected that butanol could be available

by that time based on the recent announcements. Models for butanol production are

developed in this paper based on production data available in the research literature, as

described below. The production data are based on experimental work which has not

been optimized from a process engineering perspective. There is considerable uncertainty

as to the feasibility and performance of the process at commercial scales.


Figure 6.1: A simplified life cycle flowchart for corn-derived butanol and ethanol

6.2.2 Corn ethanol and butanol production

Corn production and its life cycle implications are identical whether the ultimate fuel

to be produced is ethanol or butanol. Activities included in corn production are: agri-

cultural chemical manufacture, including transport to the field and application; grain

harvest; and transport to the biofuel production facility. Corn ethanol plants can be

classified into dry milling and wet milling operations. The GREET 1.8b dry mill facility

pathway is utilized since all recent facilities constructed in the U.S. have been of this type.

The resulting ethanol yield is 405 L/Mg of corn with dried grains with solubles (DDGS)

produced as a co-product at a yield of 290 bone-dry kg/Mg of corn [12]. Prior to leaving

the facility, the ethanol is usually denatured with at least 1-5 vol% gasoline; however, in

this study the denaturant is excluded, in order to compare the life cycles of neat biofuels

leaving the ethanol and butanol facilities.

The production of ethanol and butanol from corn using the dry milling process is quite

similar, although as noted earlier ethanol production is a mature process while butanol is

not currently produced at commercial scale. For the butanol life cycle model, all stages

of the life cycle up to the corn delivery to the production facility are assumed identical


to those modeled in the ethanol process. After delivery to the production facility, the

two models diverge as the saccharification, fermentation and separation processes differ.

The energy required for grain handling and saccharification in the butanol facility was

estimated using the USDA AspenPlus (Aspen Technologies, Inc., Cambridge, MA) model

for corn dry mill ethanol [27, 28]. The AspenPlus model was modified for the butanol

process to produce a fermentation broth containing 60 g/L of sugars and assuming 95%

of substrate utilization1. The conversion of glucose to acetone, butanol, and ethanol was

estimated based on lab-scale yields using the C. beijerinckii BA101 organism in a batch

reactor [29]. Estimates were based on batch conversion technology since it is widely used

in industrial fermentation technology, and other newer processes based on continuous

fermentation technologies, in-situ gas stripping, and fed-batch technologies [23, 24, 30]

are not.

The energy requirements for the downstream processes (i.e., separation of solvents

from broth) were estimated using an AspenPlus model consisting of a gas stripper and

two distillation columns based on previous work by Liu [31]. The model does not employ

advanced separation technologies, such as adsorption and pervaporation, which have

been proven to reduce energy consumption in recent laboratory experiments [32]. The

downstream process model was not optimized for energy efficiency, so it was estimated

that 40% of the energy prediction for the the separation processes could be saved using

heat-integrated distillation [33, 34]. It should be noted that since the completion of the

present study, an AspenPlus simulation of a complete process for producing butanol via

acetone, butanol, and ethanol corn fermentation has been published by Liu [35]. It is

expected that this new simulation could predict energy use better than the simulations

mentioned above since it includes all processes from grain processing through to product

purification in one model.

Beside DDGS, the butanol fermentation process also produces ethanol and acetone

as co-products. Data on butanol and co-product yields are presented in Table 6.12. Co-

product allocation methods could significantly impact the energy and emissions results

for corn ethanol and butanol. Various scenarios for butanol production were investigated

using different co-product allocation methods. The butanol scenarios developed in this

1These conversion estimates were determined by Dr. M. Griffin (Dept. of Civil and EnvironmentalEngineering, Carnegie Mellon University).

2The yield and energy use estimates were estimated by S.M. Sarathy and Y. Zhang (Dept. of CivilEngineering, University of Toronto).


study are as follows3

∙ Butanol with no co-products

∙ Butanol with DDGS co-product credited by the displacement method (assumes

DDGS displaces corn and soybean meal as an animal feed)

∙ Butanol with DDGS, acetone, and ethanol co-products credited by the energy allo-

cation method (i.e., energy use and associated GHG emissions are allocated among

products based on their energy output shares)

In addition, our butanol scenarios are compared to those of Wu et al. [22] and ethanol

results in GREET 1.8b. The comparison scenarios are as follows:

∙ Wu et al. [22] results for butanol with DDGS and ethanol co-products credited by

the displacement method

∙ Wu et al. [22] results for butanol with DDGS, acetone, and ethanol co-products

credited by the energy allocation method

∙ Ethanol with no co-product (GREET 1.8b) [12]

∙ Ethanol with DDGS co-product credited by the displacement method (GREET

1.8b) [12]

6.2.3 Post Production Life Cycle Activities

Following the production of the biofuels they are transported from the plants to bulk

terminals, where they are blended with gasoline. Barge, rail and truck are assumed to

transport 40%, 40%, and 20% of the ethanol, respectively [12]. The distances for the

barge, rail and truck transport are 837 km, 1287 km, and 129 km, respectively. For

butanol, it is assumed that 100% pipeline transportation with an average distance of 966

km (i.e., the weighted average of the transportation distances for ethanol). 85% of each

biofuel is blended with 15% conventional gasoline (CG) (i.e., not reformulated) before

use in the vehicle to produce 85% ethanol-15% gasoline (E85) and 85% butanol-15%

gasoline (Bu85) blends. After blending, the fuel is distributed to refueling stations by

3The scenarios presented in this study were selected by Dr. H. MacLean (Dept. of Civil Engineering,University of Toronto.)


Table 6.1: Data for estimating energy use and GHG emissions for corn butanol production

Assumptions Data Source

Corn Production 2010 Assumptions GREET 1.8b

in GREET 1.8b

Butanol production

Butanol yield (L/Mg of corn) 200 Estimated here

Acetone yield (L/Mg of corn) 100 Estimated here

Ethanol yield (L/Mg of corn) 34 Estimated here

DDGS yield (bone-dry kg/Mg of corn) 280 GREET 1.8b

Process fuel requirement (MJ/L of butanol)a 40 Estimated here

a Process fuel requirements are assumed to be met 80% by natural gas and 20% by

coal based on GREET 1.8b’s corn ethanol pathway [12].

diesel truck (48 km). The GREET 1.8b model is used to estimate energy use and GHG

emissions of the aforementioned distribution processes.

For the PTW analysis, the fuels are assumed to be utilized in a recent model year,

flexible fuel vehicle (FFV), the 2006 Chevrolet Impala. Flexible fuel vehicles can utilize

100% gasoline or an alcohol/gasoline blend with up to 85% alcohol. It is assumed that

there would be only minor modifications needed for the vehicle to utilize butanol blends

and that these would not impact vehicle performance with respect to energy use and GHG

emissions. The vehicle operation stage of the life cycle is based on fuel economy values

published by the U.S. Department of Energy [36]. The combined fuel consumption (fuel

economy) (55% city and 45% highway driving) for the Impala when fueled with gasoline

is 9.2 L/100km (25.5 mpg), whereas it is 12.3 L/100km (19.2 mpg) when fueled with E85.

Ethanol’s lower energy density accounts for the decrease in fuel economy; though, this is

very slightly offset by the greater thermal efficiency associated with ethanol combustion

[37]. Butanol’s higher energy density results in a smaller decrease in volumetric fuel econ-

omy than with ethanol. However, the combustion efficiency improvement possible with

ethanol blends does not occur for butanol [37]. Therefore, the calculated fuel economy

for Bu85 is 10.7 L/100km (21.8 mpg), assuming the vehicle efficiency is the same as when

fueled with gasoline. When inputted into the GREET model, the fuel economy values

are convereted to gasoline-equivalent units by multiplying the fuel economy by the ratio

of LHV gasoline/LHV alcohol-gasoline blend.


6.3 Results and Discussion

In this section, the life cycle results for the butanol production scenarios are presented

and compared to the model and literature results for butanol and ethanol.

6.3.1 WTP fossil energy use

Figure 6.24 presents the WTP fossil energy use for the various scenarios. The results for

scenarios with no co-product(s) are broken down into, corn farming (consisting of the

activities associated with feedstock production), production (conversion of the feedstock

to alcohol) and transportation (distribution of the fuel to blending terminals). Butanol

has a high fossil energy input, as can be seen from the bar showing butanol with no co-

products. The inputs are more than double the inputs of ethanol under similar conditions.

The much higher fossil energy is attributed to the lower yields and higher downstream

product recovery energy requirements in butanol production. However, when the DDGS,

acetone, and ethanol co-products are accounted for using the energy allocation method,

the fossil energy used for butanol production is substantially lower. The results of this

study are higher than those of Wu et al. [22] when no co-products are considered due to

the more conservative butanol yields assumed in this study. Figure 6.2 also indicates that

fossil energy for biofuel production are highest followed by the fossil energy required for

feedstock production. Transportation energy contributes a small portion to the overall

fossil energy use.

6.3.2 WTP petroleum use

Figure 6.3 shows the WTP petroleum use for the scenarios listed above. In comparing

Figures 6.2 and 6.3, the petroleum contribution to fossil energy use is small indicating that

butanol (and ethanol) can contribute to reducing petroleum use. The WTP petroleum

input requirements for corn butanol and ethanol are comparable due to petroleum not

being used directly in the biofuel production processes5 but in the activities of corn

farming and transportation which are assumed to be very similar in the life cycles of

4Notes for WTP Figures: Wu (2008) refers to Wu et al. [22]. The scenarios are: ButOH w/DDGS= butanol with DDGS co-product credited by the displacement method; ButOH w/DDGS and EtOH= butanol with DDGS and ethanol credited by the displacement method; ButOH w/DDGS, Acetoneand EtOH = butanol with the three co-products credited by the energy allocation method. The EtOHresults are those for dry mill ethanol in GREET 1.8b

5Natural gas is the main fossil energy source used in the production of biofuels.


0.0

0.5

1.0

1.5

2.0

2.5

ButOH no

credits

ButOH w/

DDGS

Wu (2008)

ButOH

w/DDGS

and EtOH

ButOH

w/DDGS,

Acetone,

and EtOH

Wu (2008)

w/DDGS,

Acetone,

and EtOH

EtOH no

credits

EtOH w/

DDGS

We

ll t

o P

um

p f

os

sil e

ne

rgy

us

e

(MJ

/MJ

of

fue

l p

rod

uc

ed

)

Transport

Production

Farming

WTP with credit

Figure 6.2: WTP fossil energy use (MJ of fossil fuel input/MJ of fuel produced)

the two fuels. Inclusion of co-product credits reduces the WTP petroleum use of corn

butanol by 50% to 75%.

6.3.3 WTP GHG emissions

Figure 6.4 presents the WTP GHG emissions for the various scenarios. A credit is

given for CO2 uptake during feedstock growth, but this is applied only when the biofuel

is combusted in the vehicle as per GREET convention [12]. Therefore, these WTP

results do not include a CO2 uptake credit. Corn butanol WTP results range from

60 to 180 g CO2-eq./MJ. The lower results are those which assume co-product credits.

Results for ethanol are comparable to those of butanol when co-products are considered.

It is interesting to note that fossil energy use in the most optimistic case for butanol

(considering all co-product credits) is greater than the fossil energy use for the ethanol

production with co-product credits, yet the GHG emissions are greater for ethanol than

butanol in the same scenarios. This result is because butanol consumes more fossil

energy in the form of less carbon intensive natural gas (i.e., during alcohol production)

while ethanol consumes more carbon intensive petroleum during transportation. Thus,


0.0

0.1

0.1

0.2

0.2

0.3

ButOH no

credits

ButOH w/

DDGS

Wu (2008)

ButOH

w/DDGS

and EtOH

ButOH

w/DDGS,

Acetone,

and EtOH

Wu (2008)

w/DDGS,

Acetone,

and EtOH

EtOH no

credits

EtOH w/

DDGS

Well

to

Pu

mp

petr

ole

um

en

erg

y u

se

(MJ/M

J o

f fu

el

pro

du

ced

)

Transport

Production

Farming

WTP with credit

Figure 6.3: WTP petroleum energy use (MJ of petroleum input/MJ of fuel produced)

butanol’s ability to be transported by pipelines helps in reducing net GHG emissions.

6.3.4 WTW Fossil Energy Use and GHG Emissions

For the WTW results, only the most optimistic scenarios for butanol (i.e., DDGS, ace-

tone, and ethanol co-products credited by the energy allocation method) and ethanol

(i.e., DDGS co-product credited by the displacement method) are compared to that of

conventional gasoline for E85 and Bu85 blends. Figures 6.5 and 6.6 present the WTW

fossil energy use and GHG emissions per km driven by a passenger vehicle fueled by

conventional gasoline (CG), E85, and Bu85.

The trends for fossil energy use and GHG emissions closely match each other, as is

expected from the strong relation between fossil energy combustion and GHG emission.

E85 and Bu85 can offer reductions in fossil energy use by approximately 37-38% when

compared to CG. The fossil energy use and GHG emissions for E85 and Bu85 are almost

the same. Although not graphed, it is evident that when coproduct credits are not

considered for Bu85, then the fossil energy use and GHG emissions would largely exceed

those of CG.


0

20

40

60

80

100

120

140

160

180

200

ButOH no

credits

ButOH w/

DDGS

Wu (2008)

ButOH

w/DDGS

and EtOH

ButOH

w/DDGS,

Acetone,

and EtOH

Wu (2008)

w/DDGS,

Acetone,

and EtOH

EtOH no

credits

EtOH w/

DDGS

Well

to

Pu

mp

GH

G e

mis

sio

ns

(g C

O2 e

q./

MJ o

f fu

el

pro

du

ced

)

WTP with credit

Transport

Production

Farming

Figure 6.4: WTP GHG emissions (CO2-eq/MJ of fuel produced)

6.4 Conclusions

The fossil and petroleum energy input as well as GHG emissions resulting from the

production and use of butanol from corn assuming three co-product allocation scenarios

were modeled. The co-product allocation method was found to have considerable impact

on the resulting environmental metrics. Uncertainty in the modeling was not examined

in this work but is a critical issue that should be examined in future research.

Butanol had a high fossil energy input, more than double the input to ethanol as

reported in GREET 1.8b if no co-product allocation was included. The much higher fossil

energy was attributed to the lower yields and higher product recovery energy requirements

for butanol production (i.e., primarily distillation energy). When energy was allocated

to the co-products, results were more in line with those of ethanol. Under all scenarios,

the petroleum contribution to fossil energy use was small, indicating that butanol can

contribute to reducing petroleum use in the transportation sector. The butanol scenarios

presented herein generally reported higher energy use and GHG emissions than those of

Wu et al. [22] due to our lower yield assumptions and higher energy requirements for

product recovery. On a WTW basis Bu85 and E85 can offer reductions in fossil energy


0

1

2

3

4

5

CG

E85

Bu85

We

ll to

Wh

ee

l fo

ssil

en

erg

y u

se

(MJ/k

m)

Figure 6.5: WTW fossil energy use (MJ of fossil energy input/km driven)

use and GHG emissions when compared to conventional gasoline. Additional modeling of

butanol production based on state-of-the-art experimental and pilot-scale data is needed,

as is additional data from vehicle testing to facilitate the development of more detailed

WTW analyses.

6.4.1 Recommendations

The environmental performance of corn-butanol is poor because of low butanol yields and

high energy use for recovery (i.e., distillation). The use of lignocellulosic feedstock for

biobutanol production would greatly improve environmental performance, as has been

shown in bioethanol LCA studies [13, 16]. LCA need to be conducted on biobutanol

derived from lignocellulosic feedstocks in order to determine the fuel’s environmental

performance. Future LCA studies should also include additional functional units, such

as life cycle water use, emissions of volatile organic compounds, and the effects of land

use change.

From a biofuel production perspective, it would beneficial to use entire ABE mixture

as a biofuel rather than expending energy to separate the acetone, butanol, and ethanol

products from each other. ABE biofuel is likely to offer a better environmental perfor-

mance due to the higher yield of total solvents and lower energy required for solvent


0

50

100

150

200

250

300

350

CG

E85

Bu85

We

ll to

Wh

ee

l G

HG

em

issio

ns

(g C

O2

eq

./km

)

Figure 6.6: WTW GHG emissions (CO2-eq/km driven)

recovery; however, this needs to be critically assessed using the LCA methodology. The

feasibility of ABE biofuel and its combustion performance in vehicles would first need to

be assessed, as is discussed in the following chapter.

A future research project would determine the net GHG emissions, fossil energy use,

and petroleum use associated with the production of ABE biofuel from corn and lignocel-

lulosic feedstock. An ASPEN model can be created to determine the energy requirements

for the ABE production process, and then the GREET software can be used to calculate

the net environmental impacts associated with feedstock (corn and corn stover) produc-

tion, through to conversion of the feedstock to fuel, and finally distribution of the fuel to

retail refueling stations.

The potential use of ABE biofuel would eliminate the need for sequential distilla-

tion to separate butanol from acetone and ethanol; however, water in the fermentation

broth would still require large amounts of energy to remove via distillation. Alternative

separation processes, such as pervaporation, gas stripping, liquid-liquid extraction, and

adsorption-desorption have been pursued [32]. Of these advanced separation processes,

adsorption-desorption requires the least amount of energy. Therefore, future research

should be directed towards developing a novel adsorbent for ABE removal from the fer-

mentation broth. The ideal adsorbent would have the following characteristics: quick


adsorption kinetics, high capacity, low cost, quick desorption kinetics, and ease of re-

generation. Initially, this research should explore various materials (e.g. silicalite and

activated carbon) as adsorbents for ABE mixtures in water. Particular attention should

be paid to determining the effects of material composition, surface area, and particle size.

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Chapter 7

An Experimental and Kinetic

Modeling Study of Butanol

Combustion

7.1 Introduction

7.1.1 Engine Studies

There have been several engine studies using n-butanol as a fuel or as a blending agent

with gasoline [1, 2, 3, 4, 5, 6]. In the most notable, Yacoub et al. [6] used gasoline

blended with n-butanol) to fuel a single-cylinder SI engine. They found that the butanol

blends had less knock resistance than neat gasoline. The butanol blends also had reduced

CO and hydrocarbon emissions but increased NOx emissions. This may be due to the n-

butanol blends having a higher flame temperature and earlier spark timing. Of particular

interest to the present study is that the primary oxygenated hydrocarbon emissions were

butanol, formaldehyde and to a lesser extent, acetaldehyde. A study by Miller et al.

successfully operated unmodified gasoline and diesel engines on blends containing 0-20%

butanol in gasoline and 0-40% n-butanol in diesel fuel [7].

A recent study by Wallner et al. [8] studied 10%butanol-gasoline and 10%ethanol-

gasoline blends in a modern direct-injection four-cylinder SI engine at varying engine

speeds. The difference in heat release rate was found to be neglible between the biofuel

blends and neat gasoline. At high engine loads, the butanol blend had a reduced knock

resistance, in agreement with the aforementioned study [6]. Due to differences in energy

100

Chapter 7. Chemical Kinetic Modeling of Butanol Combustion 101

density, the brake specific volumetric consumption was the lowest for gasoline, second

lowest for the butanol blend, and highest for the ethanol blend. The regulated emissions

profiles for the ethanol and butanol blends were comparable, and the study concluded

that butanol use in existing engines is feasible.

7.1.2 Combustion Chemistry Studies

Predictive models provide a better understanding of the combustion performance and

emissions characteristics of biofuel compositions and why they differ from petroleum

derived materials. The development of a butanol model requires understanding of its

fundamental pyrolysis and oxidation kinetics. However, only a few studies have examined

the combustion chemistry of butanol.

The pyrolysis of n-butanol was studied by Barnard using a static reactor [9]. The

author suggests that pyrolysis is initiated by fission at the C3H7-CH2OH bond to pro-

duce the n-propyl radical and hydroxymethyl radical. The hydroxymethyl radical further

decomposes to formaldehyde and a hydrogen radical, while the n-propyl radical decom-

poses to ethylene and a methyl radical. In another study, Roberts measured the burning

velocities of n-butanol using shadowgraph images of the flame cone [10]. The results

indicate that the maximum burning velocity of n-butanol is similar to that of n-propanol

and isopentyl alcohol (i.e., approx. 46 cm/sec). A recent study by McEnally and Pfef-

ferle measured temperature and species in atmospheric-pressure coflowing laminar non-

premixed methane flames doped with one of four isomers of butanol (including n-butanol)

[11]. They concluded that unimolecular dissociation dominated over H-atom abstraction.

This consisted of C-C fission followed by beta-scission of the resulting radicals. Complex

fission involving four-center elimination of water was estimated to account for only 1% of

n-butanol decomposition. The most important measured species included propene and

ethylene. Yang and coworkers [12] studied laminar premixed flames fuelled by one of

four isomers of butanol (including n-butanol). Their results identified the combustion

intermediates in the butanol flames, but did not provide concentration profiles. The

qualitative data provided lends support to the aforementioned dissociation mechanism

[11].

Recently, Moss et al. published a chemical kinetic mechanism for four butanol iso-

mers (i.e., n-butanol, sec-butanol, iso-butanol, and tert-butanol) and validated it against

ignition delay times measured in a shock tube [13]. Under the given experimental con-


ditions, the study concluded that n-butanol is mainly consumed by H-atom abstractions

leading to the formation of C3CH7CHO, acetaldehyde, and ethylene, and, to a lesser

extent, propene and 1-butene. It should be noted that the chemical kinetic mechanism

was not validated against species profiles in the shock tube.

Recent work by Dagaut et al. studied the oxidation of 85%butanol-15%gasoline surro-

gate mixtures in a jet stirred reactor (JSR) at 10 atm, equivalence ratios spanning 0.3-2.0,

and temperatures ranging from 770-1270 K [14]. The surrogate mixture was comprised

of iso-octane, toluene, 1-hexene and n-butanol. A novel chemical kinetic mechanism was

derived using mechanisms for each pure component in the butanol-gasoline surrogate

mixture, and it was shown to provide good agreement with the experimental data. The

mechanism indicates that H-abstraction reactions are the main consumption pathway for

the butanol-gasoline surrogate fuel.

The goal of this study is to provide additional experimental data on pure n-butanol

oxidation in several well-defined environments. This study is the first to model detailed

chemistry in an n-butanol flame. Species and temperature profiles are provided for an

n-butanol opposed-flow diffusion flame. The paper also presents species profiles for an

n-butane opposed-flow diffusion flame to elucidate the differences in combustion between

an alkane and an alcohol. In addition, species profiles are presented for n-butanol in a

JSR at 101.325 kPA and 1013 kPa (i.e., 1 atm and 10 atm) with a range of equivalence

ratios (�=0.25-2.0) and temperatures (T)1. Burning velocity data is presented for an n-

butanol premixed laminar flame at various equivalence ratios2. This comprehensive data

set is used to validate an improved chemical kinetic model of n-butanol oxidation.

7.2 Experimental Methods

7.2.1 Opposed-flow Diffusion Flame

A detailed explanation of the experimental opposed-flow diffusion flame and correspond-

ing sampling setup was presented in Chapter 4. Briefly, the setup consists of two identical

flat flame burners with circular burner ports of diameter 25.4 mm, facing each other and

spaced 20 mm apart. A fuel mixture of 94.11% N2 and 5.89% fuel (99% pure n-butanol

or 99% pure n-butane) was fed through the bottom port at a mass flux rate of 0.0131

1The JSR experimental data was obtained by M.J. Thomson, P. Dagaut, and C. Togbe2The laminar flame speed data was obtained by F. Halter and C. Mounaim-Rousselle


g/cm2-sec, while an oxidizer mixture of 42.25% O2 and 57.75% N2 was fed through the

top port at a mass flux rate of 0.0126 g/cm2-sec. At these plug flow conditions, the

Reynold’s Number is in the laminar flow regime (i.e. Re < 400), the flame is on the fuel

side of the stagnation plane, and the fuel side strain rate is approximately 33 s−1. An

ultrasonic atomizer was used to spray the liquid fuel into a stream of N2 gas preheated

to 356 K. The gaseous fuel mixture was delivered to the burner via heated stainless steel

tubing. The temperatures of the gases exiting the top and bottom burner ports were

423 K and 356K , respectively. The gas sampling system in these experiments consists

of a quartz microprobe (250 m ID, 300 m OD) connected to a dual-stage pump with

heated heads (388 K) containing PTFE diaphragms. The suction side of the sampling

system consisted of 1/4′′ tubing and a vacuum pressure gauge connected to the quartz

microprobe. An absolute pressure of 4-6 kPa was measured downstream of the micro-

probe. This was sufficient to quench most reactions and ensure accurate data on flame

composition. The compression side of the pump delivered the samples to the analytical

instruments via 1/4′′ stainless steel tubing heated to 388 K.

Analytical techniques used to analyze the species in the sample included: NDIR for

CO and CO2; GC/FID with an HP-Al/S PLOT column for C1 to C5 hydrocarbons; and

GC/FID equipped with a methanizer (i.e., Ni catalyst) and Poraplot-U column for bu-

tanol, butanal, acetaldehyde, and formaldehyde. The precision of species measurements

is estimated to be ± 15%. Temperature measurements were obtained using a 254 �m

diameter wire R-type thermocouple (Pt-Pt/13% Rh) in an apparatus similar to that used

by McEnally et al. [15]. The measured temperatures were corrected for radiation losses.

7.2.2 Jet Stirred Reactor

The JSR experimental setup3. used in this study has been described earlier [16, 17]. The

reactor consists of a 4 cm diameter fused silica sphere equipped with four nozzles of 1

mm ID that promote rapid mixing of the gases as they enter the reactor. Prior to the

injectors, the reactants were diluted with high-purity nitrogen (<100 ppm H2O, <50 ppm

O2, <1000 ppm Ar, <5 ppm H2) and mixed. A high degree of dilution (0.1% mol. of fuel)

was used, reducing temperature gradients and heat release in the JSR. The reactants were

high-purity oxygen (99.995% pure) and high-purity n-butanol (99% pure butanol from

3The JSR setup is located at CNRS, Orleans, France. The experiments were performed by M.J.Thomson, C. Togbe, and P. Dagaut.


Aldrich), which was sonically degassed before use. The reactants were preheated before

injection to minimize temperature gradients inside the reactor. A Shimadzu LC10 AD

VP pump with an on-line degasser (Shimadzu DGU-20 A3) was used to deliver the fuel to

an atomizer-vaporizer assembly maintained at 473 K. Good thermal homogeneity along

the vertical axis of the reactor (gradients of approximately 1 K/cm) was observed for each

experiment by thermocouple (0.1 mm Pt-Pt/Rh (10%) located inside a thin-wall silica

tube) measurements. The reacting mixtures were sampled by a fused silica low pressure

sonic probe, and then analyzed online by fourier transform infrared spectroscopy (FTIR)

and off-line after collection and storage in 1 L Pyrex bulbs. Off-line analyses were done

using gas chromatographs equipped with capillary columns (DB-624 and Carboplot-P7),

a thermal conductivity detector (TCD), and a FID.

The experiments were performed at steady state under constant mean residence times

(�) of 0.07 seconds and 0.7 seconds corresponding to constant pressures (P) of 101.325

kPa (1 atm) and 1013 kPa (10 atm), respectively. The reactants were continually flowing

in the reactor, whereas the temperature of the gases inside the JSR was varied stepwise. A

good repeatability was observed in the experiments and reasonable good carbon balance

of 100 ± 15% was achieved.

7.2.3 Laminar Flame Speed Setup

The device to measure the laminar flame speed consists of a stainless steel cylindrical

combustion chamber with an inside volume of 2.4 L4. Two tungsten electrodes linked to a

conventional capacitive discharge ignition system, are used to form the spark gap (2.8mm)

at the center of this chamber. Four windows provide optical accesses into the chamber.

The air-fuel mixture was prepared directly in the chamber by adding the fuel and the

air at appropriate partial pressures to reach the total initial pressure. The pressure and

temperature conditions (i.e., 350 K and 90 kPa) were selected to optimize the saturated

vapour pressure of butanol. The chamber was warmed at the desired temperature and an

electric fan, located inside the chamber, mixed all the gases. Gas phase chromatography

analysis was performed to ensure adequate mixing. High-purity n-butanol was injected

by a gasoline injector. The spray of butanol was rapidly vaporised due to the relative

high temperature and the forced convection created by the fan. A delay before ignition

4The laminar flame speed setup is located at the University of Orleans, France. The experimentswere performed by F. Halter and C. Mounaim-Rousselle.


avoided any perturbation during the flame propagation. Measurements were limited to

flames having diameters less than 60 mm, implying that the total volume of burned gases

was less than 0.5%. In this initial part of the flame expansion, the total chamber pressure

can be considered constant. Observation times were lower than 15 ms for all the cases

investigated (depending on the equivalence ratio). A range of equivalence ratios, from

0.8 to 1.2, was tested. The error in the flame speed measurements is ± 2 cm/s.

The image of the flame was obtained by the classical shadowgraphy technique. The

parallel light from an Ar-Ion laser source was created by two plano-convex lenses (25 mm

and 1000 mm focal lengths respectively). The shadowgraphic images were recorded by

using a high speed video CMOS camera5 operating at 6000 frames per second with an

exposure time of 20 �s. The temporal evolution of the expanding spherical flame was

then analyzed to determine the laminar flame speed. A global schematic view of the

system is presented in Figure 7.1.

Figure 7.1: Schematic of the laminar flame speed measurement setup

7.3 Computational Methods

The kinetic modeling for n-butanol oxidation in the three experimental setups was per-

formed using the CHEMKIN modeling package [18]. The JSR was modeled using the

perfectly stirred reactor (PSR) code, the opposed-flow diffusion flame was modeled using

the OPPDIF code, and the laminar flame speed was modeled using the premix flame code

5Photron APX


(PREMIX). The inputs to each simulation include a detailed chemical kinetic reaction

mechanism, a dataset of thermochemical properties, and a dataset of transport proper-

ties. These input files are available as supplemental material to the journal publication

of this study [19].

7.3.1 Chemical Kinetic Mechanism

The chemical kinetic reaction mechanism developed here is based on a previously pro-

posed mechanism for 85%butanol-15%gasoline surrogate mixtures in a JSR [14]. The

combustion of n-butanol proceeds via unimolecular initiation and hydrogen abstraction

reactions. The fuel radical species formed are consumed via unimolecular decomposition

(beta-scission) and bimolecular reactions. Isomerization of radical species is also included

in the mechanism. Table 7.1 presents the structure of species produced during the oxi-

dation of n-butanol. Modifications have been made to the original mechanism to provide

better agreement with JSR data at 1 atm and 10 atm and opposed-flow diffusion flame

data. Below is a description of the proposed mechanism.

The Dagaut et al. n-butanol oxidation submechanism [14] was built upon a previ-

ously proposed C1-C4 hydrocarbon mechanism [20, 21, 22]. From the C1-C4 mechanism,

Dagaut et al. added 136 reactions to represent the oxidation of n-butanol and the vari-

ous species formed during its decomposition. Due to the absence of fundamental kinetic

studies on n-butanol unimolecular decomposition reactions and abstraction reactions, the

authors allocated reaction rate constants based on published rate data for structurally

similar hydrocarbons and oxygenates. The reaction rates were allocated to provide better

agreement with JSR data for 85%butanol-15%gasoline surrogate mixtures at 10 atm [14].

The present author (i.e., S.M. Sarathy) has modified Dagaut et al.’s mechanism to

better predict the experimentally measured opposed-flow diffusion flame species profiles.

The revised mechanism consists of 878 reactions involving 118 species. The following is

a description of specific changes made to the Dagaut et al. mechanism.

The simulations using the OPPDIF code were unable to converge upon a solution

using the aforementioned C1-C4 hydrocarbon mechanism. In order to obtain convergence,

the following modifications were made:

∙ removal of the reaction C3H6 + C2H⇀↽ BUTYNE + CH,

∙ replacement of the reaction nC3H7 + (M)⇀↽ C3H6 + H + (M) by the reaction


Table 7.1: Chemical structures of species during the oxidation of n-butanol

nC3H7 ⇀↽ C3H6 + H with reaction rate constants proposed by Curran [23].

This study has also made the following modifications to the former n-butanol sub-

mechanism, in order to provide better agreement with the experimental data presented

herein:

∙ The reaction rate constant6 for the reaction C4H9O ⇀↽ H + C3H7CHO has been

changed to

8.89x1010 ⋅ T 0.75 exp

(−21060 cal

mol

RT

)cm3

mol ⋅ s

based on the rate expression recommended by Curran for C3H7O ⇀↽ H + C2H5CHO

[23].

6Units are presented in cal, K, mol, cm3, and s according to the CHEMKIN convention


∙ The reaction rate constant for the reaction C4H9OH + O ⇀↽ OH + C4H9O has

been changed to

1.58x1010 ⋅ T 2.00 exp

(−448 cal

mol

RT

)cm3

mol ⋅ s

based on the rate expression recommended by Marinov for the ethanol reaction

C2H5OH +O ⇀↽ OH +C 2H5O [24].

∙ The reaction rate constant for the reaction C4H9OH + H ⇀↽ H2 + C4H9O has been

changed to

5.36x104 ⋅ T 2.53 exp

(−8754 cal

mol

RT

)cm3

mol ⋅ s

based on the rate expression recommended by Park et al. for the ethanol reaction

C2H5OH + H ⇀↽ H2 + C2H5O [25].

∙ The reaction rate constant for the reaction C4H9OH + OH ⇀↽ H2O + C4H9O has

been changed to

7.46x1011 ⋅ T 0.30 exp

(−1634 cal

mol

RT

)cm3

mol ⋅ s

based on the rate expression recommended by Park et al. for the ethanol reaction

C2H5OH + OH ⇀↽ H2O + C2H5O [25].

∙ The reaction rate constant for the reaction C4H9OH + H ⇀↽ H2 + aC4H8OH has

been changed to

2.58x107 ⋅ T 1.65 exp

(−2827 cal

mol

RT

)cm3

mol ⋅ s


C2H5OH + H ⇀↽ OH +C2H4OH [24].

∙ The reaction rate constant for the reaction C4H9OH + OH ⇀↽ H2O + aC4H8OH

has been changed to

4.64x1011 ⋅ T 0.15 cm3

mol ⋅ s


C2H5OH + OH ⇀↽ H2O +C2H4OH [24].


∙ The original mechanism [14] employed the same reaction rate constants for uni-

molecular dissociation at the C2H5-C2H4OH and C3H7-CH2OH bond sites. How-

ever, the reaction rate constant for the reaction C4H9OH ⇀↽ C2H5 + C2H4OH has

been changed to

5.0x1016 ⋅ T exp

(−86221 cal

mol

RT

)cm3

mol ⋅ s(7.1)

since the BDE of the C2H5-C2H4OH bond is lower than the BDE of the C3H7-

CH2OH due to the proximity of the OH group in the latter.

7.3.2 Thermochemical Data

The original thermochemical data for the butanol related species was calculated using

the software THERGAS [26], based on the group and bond additivity methods proposed

by Benson [27].

7.3.3 Transport Properties

In certain combustion applications, such as the JSR, the overall rate is assumed to

be kinetically controlled since the transport processes occur infinitely fast. Therefore,

the original Dagaut et al. mechanism [14] did not include transport properties of any

species. However, the transport processes are rate-controlling in laminar diffusion flames.

This study obtained the molecular transport parameters for species using a variety of

methods. The transport properties for the majority of compounds were already available

in the previously published C1-C4 hydrocarbon mechanism [20, 21, 22]. In addition, the

transport properties of several species were obtained from a study by Gail et al. on

the combustion of methyl butanoate [28]. The transport properties of species with no

previously published data were determined as follows. For stable species, this study used

the correlations developed by Tee, Gotoh, and Stewart [29], as described in Wang and

Frenklach [30], to calculate the Lennard-Jones collision diameter and potential well depth

using the critical pressure (Pc), critical temperature (Tc), and boiling point (Tb) of the

species. The Pc, Tc, and Tb for stable species were obtained from the NIST Chemistry

WebBook [31]. The polarizability in cubic Angstroms of stable species was obtained from

the CRC Handbook of Chemistry and Physics [32]. The dipole moment was obtained

from [33]. The index factor which describes the geometry of the molecule was determined


from the molecular structure. For new radical species, the aforementioned literature data

is not readily available, so the transport properties of their stable counterpart were used.



The JSR7 allows studying n-butanol oxidation in a flameless premixed environment. The

concentration of species at each equivalence ratio and temperature condition in the JSR

was measured by sonic probe sampling and GC and FTIR analyses. The measured

species included hydrogen (H2), oxygen (O2), water (H2O), carbon monoxide (CO), car-

bon dioxide (CO2), methane (CH4), acetylene (C2H2), ethylene (C2H4), ethane (C2H6),

propene (C3H6), acetaldehyde (CH3CHO), formaldehyde (CH2O), butanal (C3CH7CHO),

1-butene (1-C4H8), and n-butanol (C4H9OH).

The following oxygenated products were detected: ethyloxirane (C4H8O), propanal

(C3CH5CHO), 2-propenal (C2CH3CHO), methyloxirane (C3H6O), oxirane (C2H4O), bu-

tanal, formaldehyde and acetaldehyde. The oxiranes, 2-propenal, and propanal are

formed at ppm levels, and therefore no concentration profiles are reported. Enols, which

are unsaturated alcohols, were not detected. A comparison with results obtained for

ethanol in similar conditions and keeping the initial carbon content constant shows bu-

tanol oxidation produces less aldehydes overall. The maximum amount of acetaldehyde

production is reduced by approximately 70% when changing the fuel from ethanol to

butanol.

Initially, the chemical kinetic mechanism was used to predict JSR data at 1013 kPa

and three equivalence ratios (i.e., �=0.5, �=1.0, and �=2.0). Figures displaying the

modeling (open symbols with line) and experimental results (solid symbols) for all the

experimental conditions are not presented here, but are available in Appendix B. Fol-

lowing is a comparison of the experimental data and the proposed model for the �=1.0

equivalence ratio. Unless otherwise mentioned, similar trends were observed at other

equivalence ratios.

The figures are plotted on a logarithmic y-axis and the models ability to reproduce the

experimental data is discussed qualitatively and quantitatively. The model’s prediction is

7The JSR setup is located at CNRS, Orleans, France. The experiments were performed by M.J.Thomson, C. Togbe, and P. Dagaut.


considered good if the shape of the model profile closely matches the experimental profile,

and if the predicted maximum mole fraction is within a factor 2 of the measured maximum

mole fraction. Figure 7.2 presents the experimental measurements (solid symbols) and

modeling results (open symbols with line) of n-butanol obtained at P=1013 kPa and

�=1.0. The experimental results show that with increasing temperature, the n-butanol

levels drop significantly between 800 K and 900 K. This corresponds to a large increase

in the concentrations of C3CH7CHO, 1-C4H8, and C3H6, all of which are products of H

abstraction pathways. The concentrations of these compounds then quickly decrease as

the temperature increases. C2H4, C2H6, CH3CHO, and CH2O concentrations are also

shown to increase between 800 K and 900 K. However, as the temperature increases

further, the concentrations of these species tends to diminish at a slower rate than the

aforementioned species.

The model predictions (open symbols with line) for P=1013 kPa, �=1.0 are also

shown in Figure 7.2. Reasonably good agreement is obtained for all measured species.

The major product species (i.e., CO, CO2, and H2O) are well predicted by the model.

CH4, C2H6, C2H4, H2, and CH2O are also reasonably well predicted across the entire

temperature range. The reactivity of n-butanol is well predicted between 800 K and 950

K, but at greater temperatures the reactivity is overpredicted. Species concentrations of

C3CH7CHO, C3H6, 1-C4H8, and CH3CHO are well predicted until approximately 1000 K,

above which they become underpredicted. C2H2 concentrations are only well predicted

at intermediate temperatures (i.e., 950-1050 K).

To further validate the model, jet stirred reactor data set for n-butanol oxidation is

presented at 101.3 kPa, four equivalence ratios (i.e., �=0.25, �=0.5, �=1.0, and �=2.0)

and a range of temperatures between 800-1250 K. Temperatures below 800 K are not

presented since the fuel was not found to react at these lower temperatures.

Figure 7.3 presents the experimental measurements and modeling results of n-butanol

obtained at �=1.0. The experimental results (solid symbols) show that with increasing

temperature, the n-butanol levels drop significantly between 950 K and 1050 K. This

corresponds to an increase in the concentrations of C3CH7CHO and propene, which are

products of H abstraction pathways. The C3CH7CHO concentration peaks around 980 K

and then decreases quickly, while propene concentration peaks at approximately 1060 K.

The concentrations of CO, CO2, CH4, C2H4, C2H6, C2H2, CH3CHO, H2O, H2, 1-C4H8,

and CH2O concentrations are also shown to increase above 950 K. The model predictions

(open symbols with line) for �=1.0 are also shown in Figure 7.3. The reactivity of


1E-06

1E-05

1E-04

1E-03

750 850 950 1050 1150

Temperature (K)

Mo

le F

racti

on

C4H8

C4H9OH

C3H7CHO

C3H6

1E-06

1E-05

1E-04

1E-03

1E-02

750 850 950 1050 1150

Temperature (K)

Mo

le F

rac

tio

n

CO

CO2

CH4

1E-06

1E-05

1E-04

1E-03

750 850 950 1050 1150

Temperature (K)

Mo

le F

rac

tio

n

C2H2

C2H4

CH3CHO

C2H6

1E-06

1E-05

1E-04

1E-03

1E-02

750 850 950 1050 1150

Temperature (K)

Mo

le F

rac

tio

n

H2O

H2

CH2O

Figure 7.2: Comparison of the experimental and predicted concentration profiles obtained

from the oxidation of n-butanol in a JSR at �=1, P=1013 kPa, �=0.7 s.


n-butanol is well predicted below 950 K, but at higher temperatures the reactivity is

underpredicted by the model. Due to this lower reactivity, the model’s prediction of

C3CH7CHO is underpredicted and shifted towards higher temperatures. H2O is well

predicted by the model at all temperatures, while CO2 and CH2O are underpredicted

below 1150 K. CH4, C2H6, 1-C4H8, and O2 are also reasonably well predicted across the

entire temperature range. CH3CHO and C3H6 are well predicted below 1100 K, but are

overpredicted at higher temperatures. The model underpredicts the concentration CO,

H2, C2H4, and C2H2 across most of the temperature range.

1E-05

1E-04

1E-03

750 850 950 1050 1150 1250

Temperature (K)

Mo

le F

rac

tio

n

C4H9OH

C3H7CHO

C3H6

1-C4H8

1E-05

1E-04

1E-03

1E-02

750 850 950 1050 1150 1250Temperature (K)

Mo

le F

rac

tio

n

CO

CO2

CH4

1E-05

1E-04

1E-03

750 850 950 1050 1150 1250Temperature (K)

Mo

le F

racti

on

C2H2

C2H4

CH3CHO

C2H6

1E-05

1E-04

1E-03

1E-02

750 850 950 1050 1150 1250

Temperature (K)

Mo

le F

rac

tio

n

H2O

H2

CH2O

O2


from the oxidation of n-butanol in a JSR at �=1, P=101.3 kPa, �=0.07 s.

Figure 7.4 presents the experimental and modeling results obtained at �=2.0 in the

JSR. The experimental data shows a similar trend as observed at �=1.0. The model’s pre-

diction of n-butanol reactivity is underpredicted above 1000 K. In addition, the C3H7CHO

concentrations are poorly predicted by the model across the entire temperature range.


The concentrations of CO, CH4, H2O, C2H6, C2H4, C2H2, 1-C4H8, and O2 are well pre-

dicted by the model. CO2, CH2O, and H2 are generally underpredicted by the model

across the range of temperatures studied, while CH3CHO and C3H6 are well predicted

below 1100 K and overpredicted at higher temperatures.

1E-05

1E-04

1E-03

750 850 950 1050 1150 1250

Temperature (K)

Mo

le F

rac

tio

n

C4H9OH

C3H7CHO

C3H6

1-C4H8

1E-05

1E-04

1E-03

1E-02

750 850 950 1050 1150 1250Temperature (K)

Mo

le F

rac

tio

n

CO

CO2

CH4

1E-05

1E-04

1E-03

750 850 950 1050 1150 1250

Temperature (K)

Mo

le F

rac

tio

n

C2H2

C2H4

CH3CHO

C2H6

1E-05

1E-04

1E-03

1E-02

750 850 950 1050 1150 1250

Temperature (K)

Mo

le F

rac

tio

n

H2O

H2

CH2O

O2


from the oxidation of n-butanol in a JSR at �=2, P=101.3 kPa, �=0.07 s.

Figures 7.5 and 7.6 present the modeling and experimental data obtained at �=0.5

and �=0.25, respectively. Under these fuel lean conditions, the experimental data shows

similar trends to the higher equivalence ratios, except considerably lower concentrations

of C2H2 are detected. Model predictions for C2H2 are below 10 ppm, and therefore

not included in the figures. The model still underpredicts the reactivity of n-butanol

above 1000 K, and its prediction of the C3H7CHO profile is shifted towards higher tem-

peratures. The model performs well at predicting the concentrations of CH2O, H2O,

CH3CHO, C3H6, C2H6, CH4, CH2O, 1-C4H8, O2, and C2H4. As observed at �=1.0, the


concentration of CO2 is underpredicted below 1150 K. CO is well predicted below 1000 K,

but the maximum mole fraction is underpredicted. H2 concentrations are well predicted

at �=0.25, but at �=0.5 the maximum mole fraction is underpredicted.

1E-05

1E-04

1E-03

750 850 950 1050 1150 1250

Temperature (K)

Mo

le F

rac

tio

n

C4H9OH

C3H7CHO

C3H6

1-C4H8

1E-05

1E-04

1E-03

1E-02

750 850 950 1050 1150 1250

Temperature (K)

Mo

le F

rac

tio

n

CO

CO2

CH4

1E-05

1E-04

1E-03

750 850 950 1050 1150 1250Temperature (K)

Mo

le F

racti

on

C2H2

C2H4

CH3CHO

C2H6

1E-05

1E-04

1E-03

1E-02

1E-01

1E+00

750 850 950 1050 1150 1250

Temperature (K)

Mo

le F

rac

tio

n

H2O

H2

CH2O

O2


from the oxidation of n-butanol in a JSR at �=0.5, P=101.3 kPa, �=0.07 s.

Sensitivity analyses and reaction path analyses were conducted to interpret the mod-

eling results at 101.3 kPa. Reaction paths analyses were performed for �=1.0, �=2.0,

�=0.5, and T=1160 K using the normalized reaction rates (refer to Figure 7.7). Accord-

ing to the proposed model at �=1.0, the leading consumption pathway is complex fission

leading to the formation of 1-C4H8 and H2O (25%). H-atom abstraction is responsible

for consuming more than 60% of the n-butanol, with H atoms (29%) and OH (57%)

radicals being the main contributors. At the given temperature, over 35% of the initial

n-butanol goes to form CH3CHO, C2H5 radical, C3H6, and CH2OH radical, while over

40% leads to the formation of 1-C4H8. Most of the 1-C4H8 breaks down to form C3H6 via


1E-05

1E-04

1E-03

750 850 950 1050 1150 1250

Temperature (K)

Mo

le F

racti

on

C4H9OH

C3H7CHO

C3H6

1-C4H8

1E-05

1E-04

1E-03

1E-02

750 850 950 1050 1150 1250

Temperature (K)

Mo

le F

rac

tio

n (

PP

M)

CO

CO2

CH4

1E-05

1E-04

1E-03

750 850 950 1050 1150 1250

Temperature (K)

Mo

le F

rac

tio

n

C2H2

C2H4

CH3CHO

C2H6

1E-06

1E-05

1E-04

1E-03

1E-02

750 850 950 1050 1150

Temperature (K)

Mo

le F

rac

tio

n

H2O

H2

CH2O


from the oxidation of n-butanol in a JSR at �=0.25, P=101.3 kPa, �=0.07 s.


Table 7.2: Comparison of maximum measured and predicted product concentrations

(ppm) in the JSR at P=101.3 kPa, �=0.07s. Italicized numbers represent measured

values, bold numbers represent predicted values, and the shaded column is the ratio of

measured to predicted.Measured Parameter φ =0.25 φ = 0.5 φ = 1.0 φ = 2.0

5397 5363 4794 1796

4911 4940 4745 1696 H2O

1.1 1.1 1.0 1.1

3126 3833 1970 279

3425 3517 3281 125 CO2

0.9 1.1 0.6 2.2

2814 1499 2533 2223

1147 730 1137 1652 CO

2.5 2.1 2.2 1.3

223 206 184 163

162 139 123 108 CH2O

1.4 1.5 1.5 1.5

147 203 264 353

131 131 272 334 CH4

1.1 1.6 1.0 1.1

12 23 84 349

7 6 37 194 C2H2

1.6 3.9 2.3 1.8

531 684 750 884

367 346 506 581 C2H4

1.4 2.0 1.5 1.5

27 50 75 90

31 42 74 92 C2H6

0.9 1.2 1.0 1.0

121 116 118 132

144 158 181 207 C3H6

0.8 0.7 0.7 0.6

49 46 46 44

55 49 44 41 1-C4H8

0.9 0.9 1.0 1.1

55 75 84 74

92 88 83 77 CH3CHO

0.6 0.8 1.0 1.0

57 47 27 38

37 32 27 23 C3H7CHO

1.5 1.5 1.0 1.6

492 780 1498 2271

378 325 603 1042 H2

1.3 2.4 2.5 2.2


the aC3H5 radical. Figure 7.7 also indicates that unimolecular decomposition reactions

become more important as the equivalence ratio is increased, but H-atom abstraction

reactions are still predominant. As the equivalence ratio is decreased, the unimolecular

decomposition reaction become negligible and the fuel is consumed by H-atom abstrac-

tion reactions.

Besides CO, CO2, and H2O, the most abundant measured species at �=1.0 were C2H4,

H2, and CH4. The proposed model indicates that H2 is largely formed via the following

pathways:

1. H abstraction from CH2O (28%) which is mainly formed by the eventual decom-

position of the cC4H8OH fuel radical via CH2OH;

2. H abstraction from the fuel (22%).

The leading pathway to C2H4 formation is decomposition of the C2H5 radical (46%)

which is formed via decomposition of the fuel radicals, dC4H8OH and aC4H8OH. Another

major pathway is via CH3 abstraction from C3H6 (16%), which is a major product of

1-C4H8 decomposition and cC4H8OH fuel radical decomposition. The model indicates

that CH4 is mainly produced via H abstraction by methyl radicals from CH2O (30%),

C2H4 (22%), and H2 (12%).

As mentioned above, the model underpredicts n-butanol reactivity at higher temper-

atures. A sensitivity analysis was conducted at 1160 K to determine which reactions

have a large affect on n-butanol concentration. A positive sensitivity coefficient implies

that an increase in the reaction’s forward rate will increase the n-butanol concentration

at the specified temperature and equivalence ratio. Figure 7.8 displays the sensitivity

coefficients in the JSR at the four equivalence ratios studied. There is a strong negative

sensitivity to the chain-branching reaction H+O2=O+OH across all the equivalence ra-

tios. At the fuel lean equivalence ratios, chain branching reactions become increasingly

important since H-atom abstraction reactions are dominant. However, the inability of

the model to well predict the n-butanol reactivity is unlikely due to errors in the reac-

tions rates of these extensively studied reactions. The sensitivity analysis indicates that

at �=1.0 and �=2.0, unimolecular decomposition of the fuel is important. In addition,

C3H6 chemistry plays an important role as a key intermediate and final stable product,

as shown in the rate of production analysis. At �=0.5 and �=0.25, H-atom abstrac-

tion reactions from the fuel show strong negative sensitivities. Therefore, the proposed


OH

C

OH

C

OH

C

OH

C

OH

O

C

OH

CH3

OHC

CH3

CH2

CH3

CH2

O

C

OH

O

H

OH

+

13%

7%

16%

8%

16%

+

25%

7%

5%

+

95%

96%

+

97%

+

aC4H

8OH

bC4H

8OH

dC4H

8OH

cC4H

8OH

C4H

9O

H2O +100%

+

87%

8%

31%

6%

14%

7%

12%

6%

13%

0%

0%

0%

26%

18%

26%

13%

17%

Figure 7.7: Reaction pathway diagram for n-butanol oxidation in the JSR at �=0.5

(normal text), �=1 (bold text),�=2 (italicized text), P=101.3 kPa, �=0.07s, and T=1160

K.

model would benefit from fundamental rate studies on n-butanol H-atom abstraction and

unimolecular decomposition reactions.


The proposed model was further validated against experimental data obtained in an

opposed-flow diffusion flame. The opposed-flow diffusion flame allows us to study the

oxidation of n-butanol in a non-premixed flame environment. Concentration profiles

for species were obtained by sampling the product gas at various points between the two

burner ports and then analyzing it using a variety of analytical techniques. The measured

species included n-butanol (C4H9OH), carbon monoxide (CO), carbon dioxide (CO2),

methane (CH4), acetylene (C2H2), ethylene (C2H4), ethane (C2H6), propane (C3H8),


-1 -0.8 -0.6 -0.4 -0.2 0 0.2 0.4

HCO+M<=>H+CO+M

H+O2<=>OH+O

C4H9OH=>CH3+CC3H6OH

C4H9OH=>C2H5+C2H4OH

C4H9OH+OH=>H2O+BC4H8OH

C4H9OH+H=>H2+C4H9O

C4H9OH+H=>H2+AC4H8OH

C4H9OH(+M)<=>C4H8+H2O(+M)

C3H6+H<=>AC3H5+H2

AC3H5+H(+M)<=>C3H6(+M)

Normalised Sensitivity Coefficient

phi = 0.25phi = 0.5phi = 1.0phi = 2.0

Figure 7.8: Sensitivity of n-butanol concentraion to select reactions in the JSR at �=1.0,

P=101.3 kPa, �=0.07 s, and T=1160 K.

propene (C3H6), propyne (pC3H4), acetaldehyde (CH3CHO), formaldehyde (CH2O), bu-

tanal (C3CH7CHO), 1-butene (1-C4H8), 1,3-butadiene (1,3-C4H6, cis-2-butene ((Z)-2-

C4H8), and cis-2-butene ((E)-2-C4H8). Species below the experimental limit of quan-

tification (5 ppm) included 1-butyne (1-C4H6), n-butane (C4H10), 1-pentene C5H10, n-

pentane (C5H12), n-hexane (C6H14), 1-hexene C6H12, and benzene (C6H6). Measurable

levels of acrolein, acetone, ethanol, methanol, and unsaturated alcohols were not detected

in the flame.

Figure 7.9 displays the experimentally measured (solid symbols) and model predicted

(open symbols with line) species and temperature profiles obtained in the opposed-flow

diffusion flame 8. (Z)-2-C4H8 and (E)-2-C4H8 not plotted since both maximum measured

and predicted concentrations were below 100 ppm. The experimental results (solid sym-

bols) show that the n-butanol concentration begins decreasing quickly at a distance of 4

mm from the fuel port. As the fuel is consumed, the CO and CO2 concentrations begin

rising. All of the n-butanol is consumed at a distance of approximately 7 mm from the

fuel port, which corresponds closely to the visually observed flame front. Just before

the flame front, at around 6.5 mm from the fuel port, the concentrations of hydrocarbon

8Larger figures are available in Appendix B


species reach their maximum. Besides CO and CO2, the most abundant measured species

are C2H4, C2H2, CH4, and C3H6. Thus, there is a general agreement with the �=1.0 JSR

data which also showed high levels of C2H4 and CH4.

Table 7.3: Comparison of maximum measured species in n-butanol (C4H9OH) and n-

butane (C4H10) opposed-flow diffusion flames.

Measured Parameter C4H9OH C4H10

CO2 Carbon Dioxide (%) 10 9.7

CO Carbon Monoxide (%) 4.0 3.9

CH4 Methane (ppm) 3621 2608

CH2O Formaldehyde (ppm) 592 366

C2H6 Ethane (ppm) 1035 1068

C2H4 Ethylene (ppm) 10260 8862

C2H2 Acetylene (ppm) 3964 2864

C3H6 Propylene (ppm) 1750 1420

C3H8 Propane (ppm) 119 75

1-C4H8 1-Butene (ppm) 852 101

CH3CHO Acetaldehyde (ppm) 1173 23.9

C3H7CHO Butyraldehyde (ppm) 49 below detection limit

In order to elucidate the differences in combustion between alkane combustion and

alcohol, opposed-flow diffusion flame profiles were also generated for n-butane under sim-

ilar conditions. The supplemental material contains flame profiles for measured species

and model predictions using the present mechanism with n-butane as the fuel. Ta-

ble 7.3 displays the maximum measured mole fractions in the n-butanol and n-butane

flames. Considering that the experimental error is 15%, the two flames have similar

concentrations of CO2, CO, CH4, C2H6, C2H4, C2H2, C3H6, and C3H8. The n-butanol

flame has higher measured levels of CH2O, CH3CHO, C3H7CHO, 1-C4H8. Modeling

predictions for both the flames yield similar results. The higher levels of oxygenated

compounds are expected in the n-butanol flame since the fuel is oxygenated. 1-C4H8 is

formed during the combustion of n-butanol via important unimolecular decomposition

and via H-atom abstraction pathways, as is shown in the discussion below. n-Butane

reacts via H-abstraction from primary and secondary carbon atoms to form 1-C4H9 and

2-C4H9 radicals, unimolecular decomposition to form C2H5 radicals, and unimolecular

decomposition to form C3H7 and CH3 radicals. The 1-C4H9 radicals primarily undergo

�-scission to form C2H2 and C2H5, while 2-C4H9 radicals break down to produce C3H6,


0

200

400

600

800

1000

1200

1400

1600

1800

2000

0 5 10 15 20

DISTANCE FROM FUEL PORT (mm)

TE

MP

ER

AT

UR

E (

K)

measured

corrected

predicted

0%

2%

4%

6%

8%

10%

12%

0 2 4 6 8 10 12 14 16 18 20


MO

LA

R C

ON

CE

NT

RA

TIO

N (

%)

CO2

CO

C4H9OH

0

2000

4000

6000

8000

10000

12000

0 2 4 6 8 10 12


MO

LA

R C

ON

CE

NT

RA

TIO

N (

pp

m) C2H4

C2H2

CH4

0

500

1000

1500

2000

2500

3000

3500

4000

4500

0 2 4 6 8 10 12


MO

LA

R C

ON

CE

NT

RA

TIO

N (

pp

m)

C2H6

C4H8

C3H6

0

50

100

150

200

250

300

350

400

0 2 4 6 8 10 12


MO

LA

R C

ON

CE

NT

RA

TIO

N (

pp

m)

C3H7CHO

C3H8

C3H4

1,3-C4H6

0

200

400

600

800

1000

1200

1400

0 2 4 6 8 10 12


MO

LA

R C

ON

CE

NT

RA

TIO

N (

pp

m) CH2O

CH3CHO

Figure 7.9: Experimental and computed profiles obtained from the oxidation of n-butanol

in an atmospheric opposed-flow flame (5.89% C4H9OH, 42% O2).


1-C4H8, (Z)-2-C4H8, and/or (E)-2-C4H8. The pathways leading to 1-C4H8 in n-butanol

are more direct than the pathways in n-butane, thus rationalizing the higher measured

and predicted 1-C4H8 levels in the n-butanol flame.

The model’s prediction (open symbols with line) of temperature species profiles in

the opposed-flow diffusion is also shown in Figure 7.9. The model well-reproduces the

experimentally measured temperature profile. The reactivity of n-butanol is also well

predicted by the model. The maximum concentration of CO2 is well-predicted by the

model, while the maximum concentration of CO is underpredicted by approximately

0.6%. There is a shift in the measured CO and CO2 profiles towards from the fuel port.

This shift is caused due to flow field disturbances introduced by the quartz probe used in

these experiments. The probe 9 had a large inner diameter and short tip which caused

the flame to be drawn towards the fuel port. This probe effect was resolved by moving

to a new probe design 10.

The model performs well qualitatively, in that it well reproduces the shape of the

experimental profiles; however, there is a shift in the measured profiles towards from

the fuel port. In the following discussion, the model’s prediction is considered good if

predicted maximum mole fraction is within a factor 1.5 of the measured maximum mole

fraction. The model performs well at predicting the maximum concentrations of C2H4,

C2H6, 1-C4H8, and CH3CHO. The model moderately overpredicts (1.5-2.5 times) the

concentration of CH4, C2H2, pC3H4, C3H6, and CH2O. There are large overpredictions

(greater than 5 times) of 1,3-C4H6 and C3H7CHO, and large underpredictions of C3H8

(6 times). However, these compounds are only found in small quantities in both the

modeling and experimental results.

Another measure of the model’s qualitative performance is to compare the relative

concentration of species. Although the model poorly predicts the maximum concentra-

tions of several species, the relative concentration of species is reasonably well reproduced

by the model. For example, both the experimental data and model predictions show the

minor hydrocarbon species in order of decreasing maximum concentration are C2H4,

C2H2, CH4, C3H6, C2H6, 1-C4H8, 1,3-C4H6, pC3H4, C3H8. For oxygenated compounds,

both the experimental data and model predicted values show that maximum mole frac-

tions decrease in the order of CH3CHO, CH2O, and C3H7CHO.

9This probe was made at the Department of Chemistry Glass Blowing Shop10The new probe design is described in Chapter 4 and improved results for methyl decanoate opposed-

flow diffusion flames are shown in Chapter 9


Sensitivity analyses and reaction path analyses were performed to interpret the mod-

eling results in the opposed-flow diffusion flame. Reaction path analyses were performed

at three different temperatures, as follows:

∙ low temperature, T=858 K, corresponding to 5.25mm from the fuel port and 34%

of the fuel consumed;

∙ intermediate temperature, T=1170 K, corresponding to 6.38mm from the fuel port

and 78% of the fuel consumed;

∙ and high temperature, T=1520 K, corresponding to 7.5mm from the fuel port and

99% of the fuel consumed.

Figure 7.10 displays the primary reactions paths involved in the consumption of n-

butanol at the three aforementioned temperatures, with italicized, regular, and bold texts

referring to low, intermediate, and high temperature conditions, respectively. Percentages

corresponding to each pathway are rounded to the nearest whole number.

The low temperature analysis provides an understanding of how fuel consumption

initiates in the flame. According to the proposed model, H-atom abstraction accounts

for the nearly 100% of the fuel consumption, with abstraction being dominated by H

atoms (43%), OH radicals (22%), CH3 radicals (11%), and aC3H5 radicals (9%). At

intermediate temperatures, the analysis provides a comparison to the JSR study, while

providing an understanding of reactions occurring near the flame front. As in the JSR,

fuel consumption is dominated by H-atom abstraction reactions (70%) by H atoms (55%)

and OH radicals (25%). At high temperatures, H-abstraction reactions are negligible and

the energy is now available to activate unimolecular decomposition reactions. However,

the amount of fuel that reaches these conditions is small, and therefore these reactions

do not contribute significantly to the formation of product species.

The fuel radicals formed via H-atom abstraction are primarily consumed via �-

scission. At the low temperature condition, over 60% of the initial fuel goes to form

CH3CHO, C2H5 radical, C3H6, and CH2OH radical. Another 38% goes to form C3H7CHO,

1-C4H8, H atoms, and OH radicals. At the intermediate temperature condition, H-atom

abstraction reactions are still predominant, but complex fission leading also plays a role

(17%), and 35% of the fuel goes to form 1-C4H8. As the temperature increases, the

1-C4H8 is consumed to form C3H6 via the aC3H5 radical.


Besides CO, CO2, and H2O, the most abundant measured species was C2H4. At

its maximum predicted concentration (T=1280 K, distance=6.75 mm), the proposed

model indicates that C2H4 is formed via decomposition of the C2H5 radical (39%), which

was formed via decomposition of the fuel radicals (53%), dC4H8OH and aC4H8OH, and

unimolecular decomposition of the fuel (32%). The three other pathways contributing

to more than 10% of the total flux are via �-scission of the cC3H6OH radical (17%), �-

scission of the C2H4OH radical (13%), and via CH3 abstraction from C3H6 (12%). Both

the cC3H6OH radical and the C2H4OH radical are formed via unimolecular decomposition

of the fuel (refer to Figure 7.10)

OH

C

OH

C

OH

C

OH

C

OH

O

C

OH

CH3

OHC

CH3

CH2

CH3

CH2

O

C

OH

OH

O

H

+

16%

20%

25%

14%

25%

+

0%

0%

0%

+

99%

99%

+

99%

+

aC4H

8OH

bC4H

8OH

dC4H

8OH

cC4H

8OH

C4H

9O

+

95%

+

1%

0%

1%

0%

1%

13%

39%

28%

88%

94%

76%

20%

10%

18%

6%

18%

17%

4%

3%

95%

96%

97%

100%

99%

99%

87%

85%

H2O

Figure 7.10: Reaction pathway diagram for n-butanol oxidation in the opposed-flow

diffusion flame at T=858 K (italicized text), T=1170 K (bold text), and T=1520 K

(normal text).

A sensitivity analysis was conducted for n-butanol at the low and intermediate tem-

peratures. Figure 7.11 displays the reactions to which the n-butanol concentration is


sensitive to under these conditions. Strong negative sensitivity coefficients are observed

for unimolecular decomposition reactions that are responsible for consuming the fuel.

Increasing the forward rates of these reactions will serve to decrease the fuel concen-

tration. Negative sensitivities are also observed for H abstraction from the �, �, and

carbons, which were shown to be major pathways for fuel consumption in the reaction

path analysis. As in the JSR, it can be concluded that the proposed model would ben-

efit from fundamental rate studies for n-butanol H-atom abstraction and unimolecular

decomposition reactions. Positive sensitivity coefficients are observed for several C2 and

C3 reactions that compete for H and CH3 radicals.

-0.06 -0.04 -0.02 0 0.02 0.04

C4H9OH+H=>H2+BC4H8OH

C4H9OH(+M)<=>C4H8+H2O(+M)

C4H9OH=>CH3+CC3H6OH

C4H9OH=>C2H5+C2H4OH

C4H9OH+H=>H2+CC4H8OH

C4H9OH+H=>H2+DC4H8OH

C2H4+H<=>C2H3+H2

C2H4+CH3<=>C2H3+CH4

C3H6+H<=>AC3H5+H2

2CH3(+M)<=>C2H6(+M)

AC3H5+H(+M)<=>C3H6(+M)

Normalised Sensitivity Coefficient

858K

1170 K

Figure 7.11: Sensitivity of n-butanol concentration to select reactions in the atmospheric

opposed-flow diffusion flame (6% C4H9OH, 42% O2).

7.4.3 Laminar Flame Speed

The proposed model was also validated against experimentally measured laminar burn-

ing velocities11. This experiment allows the study of the combustion of butanol in a

11The laminar flame speed setup is located at the University of Orleans, France. The experimentswere performed by F. Halter and C. Mounaim-Rousselle.


premixed flame environment. The laminar burning velocity was determined over a range

of equivalence ratios and was compared to the data previously obtained by Roberts [10].

Figure 7.12 displays the experimental and model predicted laminar burning velocities

at a number of equivalence ratios. The experimental data obtained in this study indicates

that the burning velocity increases between �=0.8 and �=1.1, which corresponds to a

maximum burning velocity of 47.7 cm/s, and then decreases at higher equivalence ratios.

There is good agreement between the presently obtained data and the data obtained by

Roberts nearly 50 years ago. The model well predicts the change in laminar burning

velocity as the equivalence ratio is increased from 0.7 to 1.4. The maximum burning

velocity predicted by the model is 45.8 cm/s at �=1.1, which is 1.9 cm/sec less than the

experimentally obtained value. Thus, it is observed that the proposed model performs

well in premixed flame environments.

25

30

35

40

45

50

0.7 0.9 1.1 1.3 1.5

Equivalence Ratio

Lam

inar

Bu

rnin

g V

elo

cit

y (

cm

/s)

Current Study

Roberts (1959)

Numerical

Figure 7.12: Laminar burning velocities of n-butanol/air mixtures, T=350 K, P=90 kPa.

7.5 Conclusions

In the JSR and opposed-flow diffusion flame, the proposed model indicates that H-

abstraction is the major pathway for n-butanol consumption, followed by �-scission of

the resulting fuel radicals. These findings differ from previous studies on n-butanol con-


sumption because of the peculiarity of each experimental apparatus used. In a pyrolysis

study of n-butanol, Barnard suggested that fission at the C3H7-CH2OH bond to produce

the n-propyl radical and hydroxymethyl radical was dominant [9]. Barnard’s findings are

consistent with what one would expect from a pyrolysis study where oxygen is absent,

hence unimolecular dissociation dominates. McEnally and Pfefferle studied coflowing

laminar non-premixed methane flames doped with n-butanol, and concluded that uni-

molecular dissociation dominated over H-atom abstraction [11]. Their findings are logical

since it applies to a doped coflow flame in the centerline region, where the temperatures

are very high (>1300 K), the fuel concentration is at a maximum, and the concentra-

tions of radical species are at a minimum. It is not clear if the proposed unimolecular

decomposition mechanism would dominate in coflow flames of pure n-butanol (i.e. un-

doped flames). H-abstraction dominates in the experimental configurations presented

herein due to the nature of the combustion processes. The JSR is a premixed apparatus

so there is a rapid formation of radical species, which contribute to H-atom abstraction

reactions being dominant. H-atom abstraction dominates in the opposed flow diffusion

flame because radical species generated near the flame front are able to diffuse into the

fuel stream to consume the fuel. Unimolecular decomposition is not significant even at

regions where radical species concentrations are small because the temperature of the

fuel stream is low at these points.

In this study, experimental data for n-butanol oxidation in a JSR (101.3 kPA and 1013

kPa), opposed-flow diffusion flame, and premixed laminar flame have been compared to

a chemical kinetic mechanism. The experimental data in the JSR and opposed-flow dif-

fusion flame indicate that the most abundant measured species were CO, CO2, H2, H2O,

CH4, and C2H4. Appreciable quantities of the oxygenated species, butanal, acetaldehyde,

and formaldehyde, were also detected. The proposed model provides good qualitative

agreement with the data obtained across the three experimental configurations. Good

quantitative agreement is also observed for a number of species, however the potential

for improvement exists.

7.6 Recommendations

Both the proposed model and the experiments suggest that enols (i.e., R1R2C=CH(OH))

are minor species produced during the oxidation of n-butanol in both the JSR and the

opposed-flow diffusion flame. Their predicted maximum concentration at low ppm levels


(<5 ppm) in the opposed diffusion flame and sub-ppm levels in the JSR suggest that their

chemistry is of little importance for modeling n-butanol oxidation in these experiments.

However, it is well known that enols rapidly tautomerize into aldehydes, so it is possible

that the experimental data presented here did not accurately measure enol concentrations

in the flame. Other studies have identified enols as important species in the combustion

of oxygenates [34, 12] and hydrocarbons [35, 36]. Recent ab initio rate calculations by

Simmie et. al [37] suggest that butenol (i.e., 1-buten-1-ol) should be as abundant as

butanal during the combustion of n-butanol. An improved n-butanol chemical kinetic

mechanism has been developed by the authors, which includes reaction pathways and

rate estimates for the production and destruction of enols [38], but additional experi-

mental data is required to validate the mechanism. In summary, an improved n-butanol

mechanism would benefit from the following:

1. Fundamental studies, experimental and theoretical, on n-butanol H-abstraction

reaction rates across a wide range of temperatures.

2. Fundamental reaction rate studies on simple and complex n-butanol unimolecular

decomposition reactions at intermediate and high temperatures.

3. Well validated chemical kinetic sub-mechanisms for enols and aldehydes.

It was mentioned previously that ABE is a biofuel that has a potential for use in

combustion applications; however, there are currently no systems designed to use this

fuel. Future research should be directed towards enabling the use of ABE biofuel in

combustion applications through detailed analysis of the fuel’s combustion properties.

In addition, the studies should focus on identifying and mitigating pollutant formation

associated with ABE combustion.

Detailed chemical kinetic models have been developed for butanol, ethanol, and ace-

tone [39], but none are capable of dealing with ABE mixtures. Research should be

directed towards developing a validated chemical kinetic model for ABE mixtures. The

validated kinetic model will enable engine designers to optimize engine systems for ABE

combustion. The species measured in different experimental setups will provide a better

understanding of pollutant formation under varying operating conditions (i.e. tempera-

ture, pressure, air-fuel ratios, etc.).

In addition to detailed chemical kinetic combustion models, future research should be

directed towards developing skeletal mechanisms for butanol and ABE biofuels. These


simplified mechanisms are advantageous because they require less computational power

to solve, while still allowing the prediction of combustion product species. In order to

develop these simplified models, one must first start with the aforementioned detailed

chemical kinetic model and set of experimental data. A reduction methodology, such as

the direction relation graph method, can then be used to create a skeletal mechanism

[40]. Thus, the detailed chemical kinetic mechanism provides a fundamental scientific

basis for biofuel combustion while the skeletal mechanism is used in the modeling of

practical combustion systems.

Supplemental Material

The journal publication [19] corresponding to this study includes the following supple-

mental material. Figures corresponding to data and modeling predictions are also avail-

able in Appendix B:

1. Raw experimental data, modeling predictions, and corresponding graphs for n-

butanol in the JSR at 10 atm. (.XLS format)


butanol in the JSR at 1 atm. (.XLS format)


butanol in the opposed-flow diffusion flame. (.XLS format)


butane in the opposed-flow diffusion flame. (.XLS format)

5. The proposed n-butanol chemical kinetic mechanism in CHEMKIN format. (.INP

format)

6. The proposed n-butanol thermodynamic datafile in CHEMKIN format. (.DAT

format)

7. The proposed n-butanol transport datafile in CHEMKIN format. (.DAT format)

Literature Cited

[1] M. Gautam, D. Martin, and D. Carder, “Emissions characteristics of higher alco-

hol/gasoline blends,” Proceedings of the Institution for Mechanical Engineers, vol.

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Part III

Biodiesel

136

Chapter 8

Background

Biodiesel is a fuel comprised of fatty acid alkyl esters derived from vegetable oils or

animal fats and meeting the requirements of ASTM D6751 [1]. Biodiesel is of particular

interest because it can replace petroleum diesel for use in compression ignition engines.

Commercial biodiesel fuels are produced via transesterification of triglycerides extracted

from a variety of biolipid feedstocks including the following: virgin vegetable oil feedstocks

such as rapeseed, soybean, canola, mustard, palm oil, and sunflower; waste vegetable oils;

animal fats such as beef tallow, chicken fat, lard and yellow grease; and non-edible oils

such as jatropha, neem oil, castor oil, tall oil, and microalgal oil [2, 3]. The advantages

of biodiesel include the following[4]:

∙ It is a renewable fuel.

∙ It displaces the use of petroleum diesel.

∙ It has a low toxicity and high biodegradability.

∙ It may help reduce greenhouse gas emissions.

∙ It can reduce particulate matter (PM), carbon monoxide (CO), sulphur oxides

(SOx), and total hydrocarbon (THC) emissions.

∙ It can be made locally from agricultural and/or recycled feedstocks.

Biodiesel refers to the pure fuel before blending with diesel fuel. Biodiesel blends are

denoted as, “BXX” with “XX” representing the percentage of biodiesel contained in the

blend (i.e., B20 is 20% biodiesel, 80% petroleum diesel)” [5]. Biodiesel can be used in its

137


pure form (i.e., B100), or it can be blended with petroleum diesel, as is more common in

practice. A blend of 20% biodiesel (i.e., B20) is the most common in the United States

because it balances performance, cost, emissions, and handling with petroleum diesel

[4]. Biodiesel is typically comprised of a mixture of saturated and unsaturated fatty acid

alkyl esters (e.g., methyl esters, ethyl esters, etc.) with chain lengths ranging from 12

to 18 carbon atoms. The spectrum of fatty acid alkyl ester composition depends on the

feedstock oil and the alcohol (i.e., methanol or ethanol) used during production.

In order to optimize engine systems for biodiesel, designers require information about

the fundamental combustion chemistry of the fuel (e.g., chemical kinetic mechanisms).

Developing chemical kinetic mechanisms for biodiesel has been challenging due to the

large size of the fatty acid alkyl esters found in practical fuels and the added complex-

ity of varying chain length and degrees of unsaturation. Nevertheless, much progress has

been made in understanding biodiesel combustion chemistry. This chapter presents back-

ground information on biodiesel so that the reader understands the fuel’s complexity and

the need for combustion studies to better understand the effects on engine performance.

Biodiesel Sustainability

Biodiesel is considered the most sustainable amongst current food-based biofuels because

the energy needed for processing oils and fats into biofuel is small [6]. The most widely

used feedstock for biodiesel are soybean oil in the U.S. and canola/rapeseed oil in Canada

and Europe. A large number of studies have applied the LCA approach for assessing

the sustainability of biodiesel fuels compared to conventional petroleum diesel. Several

LCA review papers have reported that soybean methyl ester can offer reductions in

fossil energy use ranging from 50% to 93%, and reductions in GHG emissions ranging

from 16% to 63% [6, 7, 8]. However, some studies report substanial increases in GHG

emissions for some seed-based biodiesel fuels [9] The wide error margins around the exact

values for reductions in fossil energy use and GHG emissions are because of the different

assumptions, coproduct credit allocation methods, and other case-specific information

used in different studies.

Biodiesel has also been cited as potentially harmful to the environment and society.

Fargione et al. calculated that the conversion of tropical rainforests to cropland for palm

oil methyl ester results in carbon debts that may take hundred of years to repay [10]. As

palm oil is also used for food, its diversion towards biodiesel has societal impacts, and the


deforestation occurring for crop production threatens tropical biodiversity [11]. Oilseed

biodiesel fuels offer limited environmental benefits because they harness only a small

portion of the available above-ground biomass, and thus they are disadvantaged from a

yield perspective [9]. Agricultural, environmental, and societal impacts can be largely

reduced by using crops that require less agricultural inputs (i.e., fertilizer, pesticide,

energy, and water), using marginal or low agricultural value land, and using non food-

based feedstock [6, 11].

Waste vegetable oils and animal fats are a technologically and economically viable

feedstock for biodiesel production [12, 13]. However, the limited quantity, high contami-

nant levels, and poor cold performance characteristics [14] limit the ability of these waste

feedstock to significantly displace petroleum diesel. Nevertheless, such waste feedstock

technologies are considered sustainable alternatives to conventional oilseed biodiesel.

Recently, biodiesel fuel derived from microalgae has become the focus of research in-

terest. Microalgae are small photosynthetic organisms that convert sunlight and carbon

dioxide into biomass. Chisti [3] calculated that there is not enough land in the U.S. for

sustainable cropping of oilseeds to displace 50% of petroleum diesel consumption; how-

ever, only 1-3% of total U.S. cropping area would be needed to replace 50% of transport

fuel needs using microalgal biodiesel. The present state of the technology is technically

feasible, but substantial improvements in genetic and metabolic engineering are needed to

make microalgal biodiesel economically competitive at commercial scales [3]. Additional

research is also needed to understand the fuel properties (e.g., physical chemical proper-

ties, combustion properties, etc.) of microalgal biodiesel, since its chemical composition

is different than conventional vegetable oil and animal fat based biodiesels.

8.1 Biodiesel Fuel Chemistry

As previously mentioned, biodiesel is derived from fats and oils, which are comprised of

compounds termed triglycerides1. Triglycerides consist of one molecule of glycerol com-

bined with three molecules of fatty acid. A fatty acid is a hydrocarbon chain terminating

in a carboxyl group. If the three fatty acids in the triglyceride are identical, then it is

a simple triglyceride. However, most triglycerides are mixed, meaning they contain a

mixture of different fatty acids. Thus, the properties of mixed triglycerides, and the fats

1Triglycerides are properly known as trialcylglycerols


and oils which they comprise, depend on the fatty acid content.

Most vegetable oils and animal fats consist of fatty acids containing 12 and 18 carbon

atoms. If a double bond exists between two carbon atoms, then the chain is described as

unsaturated because all available carbon valences for hydrogen are not satisfied. Unsat-

urated fatty acids with two or more double bonds are called polyunsaturated fatty acids,

and the extent of unsaturation is quantified by the iodine value.

There are a variety of feedstocks available for biodiesel production, but most can be

classified as vegetable oils, first-use animal fats, or waste greases [15]. The molecular

structure and physical chemical properties of a given biodiesel are directly related to the

fatty acid composition of the fat or oil from which the biodiesel was derived [16]. Table 8.1

presents the fatty acid composition of several common feedstock for biodiesel production.

Biodiesel derived from highly unsaturated feedstock, such as canola or soybean oil, will be

equally unsaturated. Similarly, biodiesel derived from palm kernel or coconut oil is highly

saturated because the feedstock consists of primarily saturated fatty acids. Microalgal

oils used for biodiesel are rich in polyunsaturated fatty acids with four or more double

bonds and chain lengths exceeding 20 carbon atoms [3].

Studying the combustion chemistry of biodiesel fuels is challenging because the molec-

ular structure and physical chemical properties depend strongly on feedstock selection.

The development of detailed chemical kinetic mechanisms is difficult due to the numer-

ous possible reaction pathways for long chain fatty acid alkyl ester molecules. The added

complexity of varying chain length and degrees of unsaturation has led to the use of surro-

gate fuels of well characterized composition for chemical kinetic studies. The purpose of

using surrogates is to simplify the combustion mechanism by using a single fuel molecule

to represent the mixtures of alkyl esters, and by using smaller molecules to reduce the

number of possible chemical reactions.

8.1.1 Biodiesel Production

Producing biodiesel that meets the ASTM D6751 standards requires the feedstock oil

or fat to be processed, so that its viscosity is reduced. Several methods by which the

viscosity can be reduced are micro-emulsification, cracking, and transesterification. Ma

and Hanna have reviewed these processes [18]; however, only transesterification produces

a fuel that meets the ASTM’s definition of biodiesel [1], wherein biodiesel is comprised

of “long chain mono alkyl esters”.


Table 8.1: Typical fatty acid composition (wt%) of various biodiesel feedstocka [17]

Lauric Myristic Palmitic Stearic Oleic Linoleic Linolenic

C12:0 C14:0 C16:0 C18:0 C18:1 C18:2 C18:3

Beef Tallow 0 3 24 19 43 3 1

Canola 0 0 4 2 62 22 10

Coconut 47 18 9 3 6 2 0

Palm 0 0 45 4 40 10 0

Palm kernel 48 16 8 3 15 2 0

Soybean 0 0 7 5 19 68 7

a The fatty acid molecular structure is indicated by Cx:y, where x is the carbon

number and y is the number of double bonds. Compositions may not add to

100% because trace fatty acids are not listed.

Transesterification is the process by which a triglyceride is converted to a mono alkyl

ester. It involves reacting the fat or oil with an alcohol, in the presence of a catalyst,

to produce glycerol and esters [19]. Methanol is the most commonly used alcohol, and

therefore the resulting biodiesel is often referred to as a mixture of FAME. Ethanol has

also been used [20], and in such cases the resulting biodiesel is comprised of fatty acid

ethyl esters. The present study focuses on biodiesel comprised of FAME moieties since

most biodiesel in production is of this type [16].

The transesterification reaction between a typical triglyceride and methanol is shown

in Figure 8.1. Three moles of methanol are required to react with each mole of triglyceride

in the oil or fat. The reaction is carried out in the presence of a catalyst to improve the

reaction rate [19]. The catalyst, alcohol, and vegetable oil are combined in a batch reactor

at approximately 65 ∘C and stirred continuously. The duration of the reaction can range

from 1-6 hours, depending on the desired yield. Two immiscible layers are formed once

the reversible reaction reaches equilibrium. The lower layer is glycerol and the upper

layer contains FAME and unreacted feedstock. Many processes separate the glycerol and

conduct a second transesterification reaction to increase the yield. If the biodiesel is to

meet ASTM D6751 specifications, it must undergo a series of separation processes for

the removal of alcohol, catalyst, water, soaps, glycerol, and unreacted triglycerides and

free fatty acids [21]. The separated crude glycerol can be further purified and used in

other applications (e.g., cosmetics, pharmaceuticals, etc.).


Figure 8.1: Transesterification Reaction

8.2 Biodiesel Fuel Properties

Biodiesel must have physical-chemical properties similar to that of diesel fuel in order

to successfully operate in a compression ignition engine. As mentioned previously, the

properties of biodiesel depend on the molecular structure of the FAME which comprise

it (i.e., their chain length and degree of unsaturation). The important fuel properties

which are influenced by the FAME profile are exhaust emissions, LHV, ignition quality

(i.e., cetane number), viscosity, oxidative stability, lubricity, and cold flow characteristics.

This section summarizes the important combustion related properties of several biodiesel

fuels, pure FAME, and standard low sulfur diesel2.

The LHV is a measure of the fuel’s energy density. Diesel engines are capable of

accepting a variation in heating values, so there is no specified LHV in the ASTM D 975

Standard. However, it is beneficial for biodiesel to have a volumetric LHV similar to that

of standard diesel so that differences in fuel economy (i.e., L/100 km) are not experienced

[19]. The volumetric LHV of biodiesel is slightly lower than that of standard diesel, so

fuel economy would be lower [8]. It should be noted that some studies have reported an

improvement in engine efficiency when using biofuels, which offsets the lower volumetric

heating value, and results in no net change in fuel economy [23]. For pure FAME, the

2Information on viscosity, oxidative stability, lubricity, and cold flow characteristics is available in theliterature [21, 16, 8, 22]


LHV increases with chain length [16].

The cetane number is a measure of the fuel’s ignition delay. Diesel combustion requires

the fuel to self-ignite as it is sprayed into the compressed cylinder gas. The self-ignition

leads to the characteristic diesel “knock”, wherein an explosion of premixed air and fuel

causes a rapid heat release and pressure rise. The magnitude of the explosion can be

decreased by shortening the ignition delay time. Higher cetane numbers result in shorter

ignition delay times, and therefore better engine operation. The ASTM D 975 minimum

acceptable cetane value is 40. Table 8.2 presents cetane numbers for common biodiesel

fuels and pure FAME. The iodine value is also shown for biodiesel fuels to indicate

the degree unsaturation. Biodiesel fuels have higher cetane numbers when compared to

standard diesel [8, 21, 22]. Similar to hydrocarbon compounds, the cetane number of

pure FAME decreases with increasing unsaturation and increases with increasing chain

length [16]. Soybean methyl ester and canola methyl ester have lower cetane numbers

than palm oil methyl ester because of the higher unsaturated fatty acid content in the

former [22]. Microalgal oils, which are rich in polyunsaturated fats with four or more

double bonds, have high iodine values and are likely to have depressed cetane numbers.

Current European biodiesel standards limit the iodine value and the concentration of

polyunsaturated FAME, so microalgal biodiesel must undergo hydrogenation3 to meet

current fuel standards [3].

8.3 Biodiesel Exhaust Emissions

There have been a number of studies on the use of biodiesel in CI engines. The goal of

these studies was to determine the effect of using biodiesel and diesel-biodiesel blends on

the emissions, fuel economy, and operation of the engine. Comprehensive review articles

on the use of biodiesel in CI engines and it effects on engine performance and exhaust

emissions have been published recently [8, 23].

The effect of biodiesel on chemical emissions depends on the specific pollutant of

concern, the type of engine used, the engine speed and load, and the biodiesel FAME

composition. The present study is mainly concerned with the role of FAME composition

because detailed chemical kinetic mechanisms can be used to elucidate any chemistry

related effects on combustion emissions. Therefore, this section briefly summarizes the

3Hydrogenations will saturate the double bonds and decrease the iodine value.


Table 8.2: Properties of common biodiesel fuels and pure FAMEa

Biodiesel feedstock or pure FAME Cetane number Iodine value

Beef Tallow 75 33-47

Palm Oil 61 49-55

Rapeseed/Canola Oil 55 110-126

Soybean Oil 49 118-139

Caprylic FAME (C8:0) 33.6 -

Lauric FAME (C12:0) 61.4 -

Myristic FAME (C14:0) 66.2 -

Palmitic FAME (C16:0) 74.5 -

Stearic FAME (C18:0) 86.9 -

Oleic FAME (C18:1) 55 -

Linoleic FAME (C18:2) 42.2 -

a Cetane numbers for biodiesel fuels are from [22, 21] and pure

FAME from [16]. Iodine values are from [24].

effects of biodiesel on diesel engine emissions with a particular focus on the role of FAME

composition4.

Table 8.3 presents the percentage of research publications that report increases, simi-

larities, or decreases in emissions when using biodiesel and diesel fuels [23]. The majority

of studies find that the use of biodiesel reduces the emissions of THC, CO, and PM, and

increases the emissions of NOx. The effect of biodiesel on the emissions of oxygenated

compounds is uncertain since research studies have shown both increases, decreases, and

insignificant differences compared to petroleum diesel.

8.3.1 CO, THC, and Oxygenate Emissions

The emissions of THC decreases when biodiesel is used [23]. Generally, the feedstock used

for biodiesel production does not affect THC emissions. However, research on pure FAME

indicates that THC emissions decrease with increasing fatty acid chain length [25, 26]

and decreasing unsaturation [25]. The most widely accepted reason for the decrease in

4The information presented is from a comprehensive review discussing the effect of biodiesel on dieselengine emissions by Lapuerta et al. [23]. The reader is directed to the original article for a more detaileddiscussion.


Table 8.3: Percent of publications that report changes in emissions for biodiesel [23].

Increase Same Decrease

NOx 85 10 5

PM 3 2 95

THC 1 4 95

CO 2 8 90

THC when compared to diesel fuel is that the oxygen content and higher cetane number

of biodiesel leads to a more complete and cleaner combustion. Rakapoulos has shown

that THC emissions decrease as the oxygen content in the cylinder increases, either by

enriching the oxygen content of the fuel or the air [27]. Increasing the cetane number

also helps reduce THC emissions, and this explains why longer chain and fully saturated

FAME have lower THC emissions.

CO emissions decrease when biodiesel is used [23]. For pure FAME, CO emissions

decrease with increasing chain length [16] and decreasing unsaturation [25, 28]. The de-

crease in emissions when compared to petroleum diesel is because of the higher oxygen

content and cetane number of biodiesel, which promote complete combustion. Cetane

numbers increase with increasing chain length and decreasing unsaturation, so this ex-

plains the decreases in CO emissions observed in longer chain and saturated FAME.

It is widely believed that biodiesel would lead to greater emissions of oxygenated

compounds, such as aldehydes and ketones, because of the fuel bound oxygen in FAME.

However, engine studies have shown varying results, and there is no conclusive evidence

on this matter [23]. One study that measured increased acrolein emissions in several

biodiesel fuels attributed this to the glycerol content of biodiesel [29]. The argument

appears valid since glycerol combustion is shown to produce high levels of acetaldehyde

and acrolein [30, 31].

8.3.2 PM and NOx emissions

It is nearly unanimous that PM emissions decrease when biodiesel is used [23]. The effects

of biodiesel feedstock and FAME molecular structure on PM emission is uncertain. The

EPA reported that PM emissions were lower for beef tallow methyl ester than soybean

methyl ester [28], which suggests that higher degrees of unsaturation may increase PM

emissions. However, studies on pure FAME have shown no correlation between FAME


molecular structure (i.e., chain length and unsaturation) and PM emissions [16, 25]. The

main factor affecting PM emissions is the oxygen content of the fuel, which is constant

for various biodiesel fuels and FAME [25]. There are a number of reasons explaining

the reductions in PM observed when using biodiesel. Biodiesel has no aromatic com-

pounds, so its use displaces the highly sooting aromatic compounds typically found in

petroleum diesel fuel. In additon, the oxygen atoms in the FAME bonds to carbon atoms,

and therefore prevents carbon atoms from participating in soot growth reactions. The

oxidation of FAME and other oxygenated intermediates also forms OH radicals, which

readily attack unsaturated hydrocarbons and prevent their participation in soot growth

reactions. The FAME combustion chemistry study presented in this dissertation offers

additional insights into the role of the ester moiety in reducing soot.

The use of biodiesel in diesel engines leads to a slight increase in NOx emissions

[23]. The EPA reported that NOx emissions were lower for beef tallow methyl ester

than soybean methyl ester [28], which suggests that higher degrees of unsaturation may

increase NOx emissions. For pure FAME, NOx emissions increase with decreasing chain

length and increasing unsaturation [16, 25].

Explanations for the observed increase in NOX in biodiesel are open for speculation.

The three prevailing mechanisms explaining NOx formation in combustion engines are: 1.

Fuel, 2. Thermal (i.e., Zeldovich mechanism), and 3. Prompt (i.e., Fenimore mechanism).

Fuel NOx is formed when nitrogen compounds fixated in the fuel are oxidized. This is

not a concern for biodiesel since the fuel does not contain any chemically bound nitrogen.

Thermal NOx is a result of high temperature dissociation and chain reaction of elemen-

tal nitrogen and oxygen in the post-combustion regime. The reactions in this mechanism

are shown in Equations 8.1, 8.2, and 8.3. The reactions are highly temperature depen-

dent, such that a decrease in combustion temperature will decrease NOx formation [32].

Tat and Van Gerpen [33] have shown that biodiesel has a higher isentropic bulk modulus

of compressibility than conventional diesel, which causes an inadvertent advance in fuel

injection time in a pump-line-nozzle fuel injector. The advance in fuel injection timing

increases the ignition delay, and therefore increases thermal NOx formation. Boehman

and coworkers [34] have shown that the bulk modulus of compressibility increases with

increasing iodine value, so this may explain the higher NOx emissions observed for un-

saturated FAME. This injection related phenomenon is the most widely cited reason for

biodiesel’s increased NOx formation [23]. The higher NOx emissions observed for shorter

chain and unsaturated FAME suggests that the lower cetane numbers of these compounds


leads to longer ignition delay times, and therefore more NOx formation.

Some experiments have shown that NOx emissions increase for biodiesel even if the

injection timing is matched exactly with petroleum diesel; and therefore, other mecha-

nisms than advanced injection time are proposed. Mueller et al. [35] recently conducted

experiments to assess the various proposed mechanisms. The results suggest that NOx

emissions increases because of advances in combustion phasing that lead to higher in-

cylinder temperatures and longer residence times, and lower radiative heat losses which

lead to higher flame temperatures. Therefore, the increased NOx emissions appear to be

largely attributed to the thermal NOx mechanism.

O ⋅+N2 ⇀↽ NO +N (8.1)

N ⋅+O2 ⇀↽ NO +O⋅ (8.2)

N ⋅+OH⋅⇀↽ NO +H⋅ (8.3)

Research studies have not eliminated the possibility of prompt NOx routes being the

cause of higher biodiesel NOx emissions. The prompt mechanism of NOx formation is

highly dependent on hydrocarbon radical intermediates formed during combustion, so it

is possible that chemical kinetic effects play a role [23, 35]. Garner et al. [36] postulate

that unsaturated FAME in biodiesel lead to higher acetylene levels that contribute to

prompt NOx formation. If such a mechanism is real, then detailed chemical kinetic models

for FAME can help to elucidate the role of molecular structure on NOx formation.

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Chapter 9

An Experimental and Kinetic

Modeling Study of Biodiesel

Combustion

9.1 Introduction

Real biodiesel is a complex mixture of FAME with differing chain lengths and degrees of

unsaturation, so it is much simpler to study the combustion chemistry of pure FAME.

However, the numerous possible reaction pathways for long chain FAME would result in

extremely large detailed chemical kinetic mechanisms. Such mechanisms are cumbersome

to develop and computationally expensive to solve in even the simplest physical reactor

models. Furthermore, conducting fundamental combustion experiments using long chain

(i.e., high molecular weight) FAME is challenging because vaporisation is difficult.

In order to avoid the difficulties associated with long chain FAME, surrogate fuels

with shorter chain lengths and known physical chemical properties are chosen for biodiesel

combustion chemistry studies. Using surrogate fuels simplifies the chemical kinetic mech-

anism by reducing the number of possible chemical reactions, while still representing the

role of the molecular structure in combustion (i.e., the role of the methyl ester moiety and

the role of carbon-carbon double bonds). In addition, surrogates fuels are more volatile,

and therefore easier to work with experimentally.

Figure 9.1 displays typical biodiesel FAME and several proposed surrogates. The

surrogate fuels are structurally similar to actual biodiesel FAME, and all but one contain

152

Chapter 9. Chemical Kinetic Modeling of Biodiesel Combustion 153

the ester moiety. The chain length and degree of unsaturation varies in the surrogate

fuels, so the individual effects of each can be understood. The remainder of this section

discusses the recent advances in the chemical kinetic modeling of FAME surrogate fuels.

Particular attention is placed on the chemistry related effects of the ester moiety, carbon

chain length, and carbon-carbon double bonds during combustion.

O

O

Methyl cis-9-octadecenoate

O

O

Methyl hexadecanoateMethyl octadecanoate

O

O

Biodiesel FAME

O

O

Methyl butanoate

O

O

Methyl trans-2-butenoate

O

O

n-Hexadecane Methyl decanoate

Surrogates for biodiesel

(Methyl oleate) (Methyl stearate) (Methyl palmitate)

(Methyl butyrate) (Methyl crotonate) (Methyl caprate)(Cetane)

Figure 9.1: Biodiesel FAME and their surrogates

9.2 Mechanisms for Short Chain Methyl Esters

Fisher and coworkers [1] were the first to develop a detailed chemical kinetic for a

biodiesel surrogate. The authors developed a chemical kinetic mechanism for methyl

butanoate (MB), a fully saturated short chain FAME. It was chosen as a modeling

surrogate of biodiesel because it was thought to be large enough to allow fast RO2 iso-

merization reactions, which are important for low-temperature chemical reactions that

control fuel auto-ignition in CI engines. While the mechanism was comprehensive, the


authors were unable to robustly validate the model due to limited experimental data on

MB combustion.

Gail et al. [2] were the first to extensively validate a slightly modified version of

the Fisher mechanism for MB using experimental data from a JSR, an opposed-flow

diffusion flame, and a flow reactor. The mechanism consisted of 295 chemical species and

1498 reactions. Recently, a number of studies have been conducted to further study the

combustion of MB and validate chemical kinetic mechanisms. Schwartz et al. [3] studied

MB combustion in co-flow flames of methane doped with MB. A number of experiments

in shock tubes and rapid compressions machines at various temperatures, pressures, and

equivalence ratios have been conducted to study the autoignition characteristics of MB

[4, 5, 6, 7, 8]. In addition, theoretical studies have performed ab initio calculations of

thermochemical properties [9, 10, 11, 12] and kinetic rate parameters [8, 13, 14] for MB.

The various studies on MB have revealed consistent conclusions. Firstly, quantum

calculations of thermochemical properties suggest that MB is a good surrogate fuel for

representing the thermochemistry of saturated long chain FAME. However, autoignition

and low temperature experimental data indicate that MB does not exhibit the cool flame

and negative temperature coefficient (NTC) behaviour, which are significant character-

istics of the longer chain FAME found in biodiesel. Figure 9.2 presents simulations of

methyl butanoate and methyl decanoate in a JSR at an initial fuel concentration of 1%, a

pressure of 1013.25 kPa, and a range of temperatures. Methyl decanoate displays typical

biodiesel NTC reactivity between 600 and 800 K, while methyl butanoate shows no reac-

tivity in this temperature range. Vaughan et al. [15] and Hadjiali et al. [16] also found

that the ignition delay time of MB did not match well with those of longer chain FAME.

Therefore, due to its short chain length, MB is not a suitable surrogate for understanding

the low temperature reactivity of biodiesel. However, it can serve as a starting point for

the development of mechanisms for larger molecules.

The studies also demonstrate the fate of the ester moiety during combustion. Figure

9.3 displays one high temperature combustion pathway leading to an important inter-

mediate during MB combustion, specifically the methoxycarbonyl radical (CH3OCO),

which is produced when the ester group breaks away from the fatty acid. Subsequently,

the methoxycarbonyl radical primarily decays to form methyl radical and CO2. From

a soot reduction standpoint, this decarboxylation of the ester is not an efficient use of

fuel-bound oxygen because two oxygen atoms are bonded to one carbon atom. It would

be more efficient if the ester moiety led to the production of CO since each oxygen atom


1.E-05

1.E-04

1.E-03

1.E-02

500 600 700 800 900 1000Temperature (K)

Mo

le F

ract

ion

MB MD

Figure 9.2: Computed profiles obtained from the oxidation of methyl decanoate and

methyl butanoate in a JSR at �=1.0, P=1013.25 kPa, �=1 s, 0.1% fuel mole fraction.

in the ester group would sequester one carbon atom from participating in the production

of soot. This conclusion is consistent with the findings of other studies on methyl esters

and biodiesel [17, 18, 19]. Furthermore, Pepiots-Desjardins and coworkers [20] studied

the sooting tendency of various oxygenated (e.g., alcohols, esters, aldehydes, etc.), and

it was concluded that ester moieties are less efficient at reducing soot than alcohol and

aldehydes moieties.

The aforementioned decarboxylation of the ester moiety also has implications on the

production of oxygenated hydrocarbon compounds. It is widely believed that FAME

would lead to higher emissions of oxygenated hydrocarbons, such as aldehydes and ke-

tones, because of the oxygen atoms present in the molecule. The chemical kinetic mech-

anism of MB indicates that at high temperatures in a flame about one-third of the fuel

bound oxygen goes to form CO2 directly and two-thirds forms oxygenated hydrocarbons.

The present study offers additional insights into the fate of the ester moiety during FAME

combustion.

Besides the ester moiety, the long chain FAME found in biodiesel are also distinguished

by their degree of unsaturation. To better understand the role of unsaturation, Sarathy

et al. [21] and Gail et al. [22] conducted combustion studies on methyl trans-2-butenoate

(MC), which is the monounsaturated counterpart of MB. The authors provided a chemical


C

OCH3

O

C

O

O

OC

- CH3

- CH3O

CH3

CH2

CH2

C

OCH3

O

CH3

CH

CH2

C

OCH3

O

- C3H

6

- H

Figure 9.3: One combustion pathway of methyl butanoate that depicts the fate of the

ester moiety.

kinetic mechanism for MC validated against experimental data in a JSR and an opposed-

flow diffusion flame. When compared to MB, the experimental data indicates that MC

combustion leads to higher levels of unsaturated hydrocarbon species (e.g., acetylene,

propyne, and propadiene), which have strong sooting tendencies. The chemical kinetic

mechanism for MC reveals that higher levels of unsaturated hydrocarbon species are

produced because the double bond is preserved during fuel decomposition, thus leading

to stable alkenes and alkynes, as shown in Figure 9.4. Such an analysis can explain why

some engine studies [23] saw the unsaturated soybean methyl esters forming more soot

than the saturated beef tallow methyl esters. Furthermore, if Garner et al.’s hypothesis

[24] that higher acetylene levels increase NOx formation via the prompt NOx mechanism,

then the analysis of MC may explain why unsaturated FAME exhibit higher NOx in

engine studies.

9.3 Mechanisms for Long Chain Methyl Esters

The studies on C4 FAME revealed that such short chain molecules are not suitable

surrogate fuels because they do not exhibit the low temperature autoignition properties

of long chain FAME. Therefore, chemical kinetic studies have shifted focus towards larger

molecules, in order to better represent the combustion properties of actual biodiesel. The

following is a discussion of the current progress in the modeling of larger surrogate fuels.

Dayma et al. [25] found that the shortest FAME to exhibit NTC behaviour in the


CH3

CH2

CH2

C

OCH3

O

CH2

C

CH2

CH3

CH

CH

C

OCH3

O

CH

CH

CH3

CH

CH2 CH

2

CH2

CH3

CH

CH

CH3

CH2

CH2

CH3

C

CH

Figure 9.4: A comparison of the combustion pathways for methyl trans-2-butenoate

(above) and methyl butanoate (below) which lead to unsaturated hydrocarbons.

JSR was methyl hexanoate. Similarly, Hadjali and coworkers [16] found that methyl

hexanoate exhibited high pressure autoignition delay times comparable to long chain

alkanes (i.e., n-heptane). Therefore, methyl hexanoate was proposed as a suitable sur-

rogate for long chain FAME. Dayma et al. developed a chemical kinetic mechanism for

methyl hexanoate consisting of 435 species and 1875 reversible reactions, and validated it

against data obtained in a JSR [25]. The model was developed by adding reactions to the

Fisher mechanism for MB, and therefore much of the same chemistry with respect to the

ester function was observed. The mechanism indicates that methyl hexanoate is mainly

consumed by H-atom abstraction reactions from the � carbon. More interestingly, the

study indicated the consumption of methyl hexanoate proceeds in much the same way

as a straight chain alkane.

In an effort to study the longer chain FAMEs found in real biodiesel, Dagaut and

coworkers [26] studied the oxidation of rape seed oil methyl ester (RME) in a JSR at

various temperatures and pressures. RME is a complex mixture of C14-C18 esters so

developing a chemical kinetic mechanism would be cumbersome. The authors proposed

that a long chain alkane would be a suitable surrogate for RME since experimental data

for n-hexadecane (C16H34) in the JSR at similar conditions indicated similar product

species concentration profiles. Therefore, a detailed chemical kinetic mechanism for n-

hexadecane was used to simulate the RME experiments. This gave a good description

of the RME experimental results, with a good agreement for RME reactivity and the


relative importance of C2-C6 alkenes. The authors stated that the mechanism could be

improved by including chemical kinetic pathways for the following: i. the ester moiety

to reproduce the early CO2 formation found in RME, and ii. carbon double bonds to

reproduce large alkene production in RME attributed to unsaturated FAMEs.

9.3.1 Mechanisms for Methyl Decanoate

Methyl decanoate has been proposed as a much better surrogate for biodiesel due to the

length of the alkyl chain. Vaughan et al. [15] found that the ignition time of methyl

decanoate (MD) fuel droplets in microgravity matched well with those of soybean oil

methyl esters. Szybist and coworkers [19] studied the autoignition of MD in a mo-

tored engine and compared it to n-heptane, a petroleum diesel surrogate. The authors

found that MD well reproduced the NTC behaviour that is characteristic of diesel and

biodiesel fuels. MD had a greater heat release during low temperature ignition, which

was attributed to the fully saturated aliphatic chain and not the ester group. Higher

levels of CO2 were observed at low temperatures, and it was hypothesized that this is

due to decarboxylation of the ester group, similar to pathways observed in kinetic studies

on MB. However, since actual biodiesel may be highly unsaturated, it is likely that using

MD as a surrogate will overpredict the low temperature heat release of actual biodiesel.

Herbinet et al. [27] have developed a detailed chemical kinetic mechanism for MD

consisting of 3012 species and 8820 reactions. Since there are no combustion studies of

MD in fundamental experimental configurations (e.g. shock tubes, laminar flames, stirred

and flow reactors), the mechanism was validated against MD data in motored engine

[19] and rapeseed oil methyl ester oxidation data in a JSR [26]. The MD mechanism

is capable of reproducing the early CO2 formation observed for RME in the JSR, a

behaviour that the n-hexadecane model by Dagaut et al. [26] could not reproduce.

The chemical kinetic mechanism reveals that low temperature formation of CO2 comes

directly from the presence of the ester group, and since CO is not formed directly, the

soot reducing efficiency of the fuel-bound oxygen is not maximized. The mechanism is

unable to reproduce large alkene production in RME because MD is too small, and it

does not contain the double bonds which lead to alkene formation. In addition, the large

size of this mechanism requires a robust numerical solver and enormous computing power

when attempting to model combustion in some configurations, such as laminar flames.

Zhang and coworkers recently studied the low temperature ignition chemistry of


three C9 FAME (i.e., methyl nonanoate, methyl trans-2-nonenoate, and methyl trans-

3-nonenoate), and used Herbinet’s [27] MD mechanism to interpret their results. It was

revealed that unsaturated FAME are less reactive at low temperatures because the C-

C double bonds inhibit the formation of six- or seven-membered transition state rings,

which are important in low temperature ignition chemistry. The reactivity decreases as

the double bond moves towards the center of the fatty acid chain. The study found that

low temperature ignition chemistry is dependent on the structure of the fatty acid chain

and not on the ester moiety; saturated FAME follow ignition pathways similar to straight

alkanes while unsaturated FAME follow similar pathways as straight alkenes.

The aforementioned MD chemical kinetic mechanism is limited in its applicability

due to the large number of species and reactions. In addition, the chemical stiffness,

which is characterized by dramatic differences between species and reaction time scales,

is significant for the large molecules in the detailed mechanism [28]. Using this mechanism

in a zero-dimensional simulation (i.e., JSR) is computationally expensive, and henceforth

a one-dimensional simulation (i.e., opposed-flow diffusion flame) is virtually impossible.

To overcome these problems, Seshadri and coworkers used the directed relation graph

(DRG) method to reduce the detailed mechanism into a skeletal mechanism consisting

of 713 elementary reactions and 125 species [29]. The skeletal mechanism was capable of

predicting experimental extinction and ignition of MD in an opposed-flow diffusion flame.

A large number of low temperature chemical reactions in the original mechanism were

discarded during the reduction, indicating that low temperature chemistry is of minor

importance in an opposed-flow diffusion flame.

9.4 Background Summary and Research Motivation

The knowledge of the high and low temperature chemical kinetic reactions responsible

for biodiesel consumption is necessary to simulate ignition, combustion, and emissions

in diesel engines. Developing validated chemical kinetic mechanisms for real biodiesel is

difficult for the following reasons:

∙ Biodiesel is a complex mixture of saturated and unsaturated fatty acid alkyl esters

that varies depending on the feedstock used for its preparation.

∙ Developing mechanisms for the large chain molecules is difficult since the num-

ber of reaction pathways and intermediate species increases drastically with each


additional carbon atom.

∙ Obtaining the vaporised fuel streams needed in most fundamental combustion se-

tups is challenging for large chain molecules due to their low vapour pressures and

high boiling points.

Therefore, surrogate fuels of well-characterized composition are needed to model

biodiesel combustion. Short chain methyl esters were originally proposed as surrogate

fuels, but detailed experimental and kinetic studies revealed that they do not reproduce

the important low temperature combustion properties of real biodiesel. However, mecha-

nisms for these smaller molecules have deepened our understanding of the ester function

during combustion. Intermediate chain length FAME, and possibly even straight chain

hydrocarbons, appear to be better biodiesel surrogates. Additional experimental and

modeling work is needed to develop validated chemical kinetic mechanisms for saturated

and unsaturated C8 to C10 FAME. The chemical kinetic mechanism for these surrogates

would reproduce the distinguishing features of biodiesel, namely the combined effects of

a large carbon chain, carbon double bonds, and the ester moiety.

The goal of this study is to develop an experimentally validated chemical kinetic

mechanism for MD. The existing detailed MD mechanism by Herbinet et al. [27] and

the skeletal mechanism by Seshadri et al. [29] have not been validated against funda-

mental combustion data for MD because experiments have not been performed. This

study presents new experimental temperature and species concentration profiles for an

MD opposed-flow diffusion flame, and uses this data to develop an improved skeletal

mechanism for MD combustion.

9.5 Experimental Methods

A detailed explanation of the experimental opposed-flow diffusion flame and correspond-

ing sampling setup was presented in Chapter 4. Briefly, the setup consists of two identical

flat flame burners with circular burner ports of diameter 25.4 mm, facing each other and

spaced 20 mm apart. A fuel mixture of 98.2% N2 and 1.8% fuel (99% pure MD) was

fed through the bottom port at a mass flux rate of 0.0142 g/cm2-sec, while an oxidizer

mixture of 42.25% O2 and 57.75% N2 was fed through the top port at a mass flux rate of

0.0137 g/cm2-sec. At these plug flow conditions, the Reynold’s Number is in the laminar

flow regime (i.e. Re < 400), the flame is on the fuel side of the stagnation plane, and the


fuel side strain rate is approximately 31 s−1. An ultrasonic atomizer was used to spray

the liquid fuel into a stream of N2 gas. The gaseous fuel mixture was delivered to the

burner via heated stainless steel tubing. The temperatures of the gases exiting the top

and bottom burner ports were 420 K and 400K , respectively. The gas sampling system

in these experiments consists of a quartz microprobe (250 m ID, 300 m OD) connected to

a dual-stage pump with heated heads (388 K) containing PTFE diaphragms. The suc-

tion side of the sampling system consisted of 1/4′′ tubing and a vacuum pressure gauge

connected to the quartz microprobe. An absolute pressure of 4-6 kPa was measured

downstream of the microprobe, and this was sufficient to quench most reactions and en-

sure accurate data on flame composition. The compression side of the pump delivered

the samples to the analytical instruments via 1/4′′ stainless steel tubing heated to 388

K.

Analytical techniques used to measure the species in the sample included: NDIR for

CO and CO2; GC/FID with an HP-Al/S PLOT column for C1 to C5 hydrocarbons;

and GC/FID equipped with a methanizer (i.e., Ni catalyst) and Poraplot-U column for

oxygenated hydrocarbons such as acetaldehyde/ethenol, formaldehyde, and acrolein. The

precision of species measurements is estimated to be ± 15%. Temperature measurements

were obtained using a 254 �m diameter wire R-type thermocouple (Pt-Pt/13% Rh) in

an apparatus similar to that used by McEnally et al. [30]. The measured temperatures

were corrected for radiation losses.

9.6 Computational Methods

The kinetic modeling for MD oxidation in the opposed-flow diffusion flame was performed

using the OPPDIF code within the CHEMKIN modeling package [31]. The inputs to

each simulation include a detailed chemical kinetic reaction mechanism, a dataset of

thermochemical properties, and a dataset of transport properties.

9.6.1 Chemical Kinetic Mechanism

The chemical kinetic mechanism developed here is an extension of previously published

detailed and skeletal mechanisms for MD. The large size of Herbinet et. al’s [27] de-

tailed mechanism makes it impractical for use in the one-dimensional flame code (i.e.,

OPPDIF), while the skeletal mechanism proposed by Seshadri et al. [29] does not contain


enough species and reactions to accurately predict species concentration profiles in the

opposed-flow diffusion flame. Therefore, the present study develops an intermediate size

mechanism, which balances computational performance and chemical fidelity. Initially,

several modifications were made to the detailed chemical kinetic mechanism to better

represent MD combustion, and then this modified mechanism was reduced using the

DRG method.

Modified Detailed Chemical Kinetic Mechanism

Herbinet et al.’s detailed chemical kinetic mechanism includes low temperature chem-

istry to simulate fuel ignition and NTC behaviour, as well as intermediate and high

temperature chemistry to simulate fuel combustion and product species formation. Low

temperature chemistry is not addressed in the present study because the consumption of

fuel in an opposed-flow diffusion flame is dominated by high temperature chemical reac-

tions. Therefore, the high temperature part of the detailed methyl decanoate mechanism

and the corresponding modifications are discussed here.

For the most part, the high temperature consumption of MD proceeds similarly to a

straight alkane. The decomposition is driven by unimolecular decomposition and H-atom

abstraction reactions leading to alkyl and alkyl-ester radicals. These radicals then react

via isomerization, decomposition (i.e., beta-scission) and bimolecular reactions with O2.

The unimolecular decomposition reactions were written in the reverse radical-radical

recombination direction and the rate for the decomposition direction was calculated from

thermochemistry via microscopic reversibility. The rates for unimolecular decomposition

reactions were based on a previous mechanism for MB by Fisher et al. [1]. H-atom

abstraction from MD and other hydrocarbon molecules were included for reactions with

radicals (e.g., H, CH3, O, OH, etc.), and the rates were determined based on typical

hydrocarbon C-H bond energies for primary, secondary, and tertiary H atoms. The

reaction rates for the two H atoms bonded to the carbon atoms adjacent to the carbonyl

group were based on the mechanism for MB by Fisher et al.

Herbinet et al. performed computational simulations of RME in a JSR using the

detailed MD mechanism and showed that unimolecular decomposition reactions are re-

sponsible for the consumption of fuel at 1040 K [27]. We also conducted opposed-flow

diffusion flame simulations using the skeletal mechanism by Seshadri et al. [29] and found

unimolecular decomposition significant at 1200 K. This predominance of unimolecular de-


composition is unexpected because chemical kinetic studies on MB [2] and MC [22] in a

JSR and opposed-flow diffusion flame and methyl hexanoate [25] in a JSR indicate that

H-atom abstraction reactions are predominant under these conditions. Additionally, ab

initio calculations by Huynh et al. [14] indicate that the energy barrier for unimolecular

decomposition of MB is higher than H-atom abstraction reactions, so H-atom abstrac-

tion reactions would dominate in combustion environments where reactive radicals are

abundant (e.g., flames, premixed reactors, etc.) We ran the JSR simulations again for

the same conditions and found that H abstraction reactions are indeed predominant at

1040 K, so Herbinet et al.’s statement that unimolecular decomposition dominates in

the JSR was incorrect. However, the unexpected predominance of unimolecular in the

opposed-flow diffusion flame simulations indicated that unimolecular decomposition rates

in original detailed mechanism needed attention.

A recent study on the autoignition of MB by Dooley et. al [4] proposed improved

rates for the unimolecular decomposition of MB into methyl ester plus alkyl radicals.

Additionally, Huynh et al. [14] and Dooley et al. [4] proposed new H-atom abstraction

rates for MB. The present study uses the study by Dooley et al. to develop improved re-

action rates for several MD unimolecular decomposition, H-atom abstraction, and radical

decomposition reactions. It should be noted that using reaction rates determined for MB

can be confidently applied to MD because theoretically calculated C-H, C-C, C-O bond

dissociation energies in long chain FAMEs [11, 10] are similar to those calculated for MB

[9]. The following list of modifications were made to better represent the combustion of

MD in the opposed-flow diffusion flame:


∙ The detailed mechanism by Herbinet et al. had an error in the activation energy for

H-atom abstractions reactions by OH from secondary C-H bonds. The value was

changed from its erroneous value (i.e., -3500 kcal/mol) to the correct value (i.e.,

-35 kcal/mol) in all relevant reactions. The original authors 1 discovered this error

after publishing the mechanism, but found that it did not alter their results much.

However, the present study shows that the error does have a significant affect on

the low temperature reactivity of MD.

∙ The recombination rate of 1-octene (C8H16) and the ME2J radical to form the

MD4J radical (i.e., reverse of the decomposition of the MD4J radical, as shown

Figure 9.5) was changed to

8.80x103 ⋅ T 2.48 exp

(−6130 cal

mol

RT

)cm3

mol ⋅ s

to make it consistent with rates of analogous reactions for the MB5J, MB6J, MB7J,

etc. radicals.

C8

C7

C6

C5

C

4

C3

C2

1

OC

O

C9

C10

C

OC

O

CC

CC

CCC

C+

MD4J

ME2JC8H16-1

Figure 9.5: Decomposition of the MD4J radical to 1-octene (C8H16) and the ME2J radical

∙ The recombination rate of methyl 2-propenoate (MP2D) and the 1-heptyl radical

(C7H15) to form the MD2J radical (i.e., reverse of the decomposition of the MD2J,

as shown Figure 9.6) was changed to

1.76x104 ⋅ T 2.48 exp

(−8130 cal

mol

RT

)cm3

mol ⋅ s

based on the rate expression given by Curran et al. [32] for the recombination of

propene (C3H6) and the methyl radical (CH3) to form the 2-butyl radical (sC4H9)

1Dr. William Pitz and Dr. Olivier Herbinet, Lawrence Livermore National Laboratory


(i.e., reverse of the decomposition of the sC4H9). Curran’s estimate was modified

by 2 kcal/mol to account for resonance stabilization effects of the carbonyl group

in MD2J [4].

C8

C7

C6

C5

C4

C3

C 2

1

OC

O

C9

C10

CC O

C

O

MD2J

+

C7H15COMethyl 2-propenoate

(MP2D)

CC

CC

C CC

Figure 9.6: Decomposition of the MD2J to methyl 2-propenoate (MP2D) and the C7H15

radical

∙ Hydrogen atoms bonded to the alpha carbon (see Figure 9.7) have BDEs similar

to tertiary C-H bonds in alkanes [4]. Therefore, H atom abstraction rates by the

radicals H, OH, CH3, CH3O, and HO2 were changed to analagous rates for tertiary

H atom abstraction in isobutane (iC4H10). The rates for isobutane from Healy et

al. [33] were multiplied by 2 to account for greater number of H atoms in the MD2J

radical.

C8

C7

C6

C5

C4

C3

C2

1

OC

O

C9

C10

MD

+ R

C8

C7

C6

C5

C4

C3

C

2

1

OC

O

C9

C10

+ RH

MD2J

Figure 9.7: Abstraction of H atoms from the alpha carbon atom by a reactive radical

species (R)

∙ Hydrogen atoms bonded to the methoxy carbon (see Figure 9.8 for MDMJ) have

BDEs similar to secondary C-H bonds in alkanes [4]. Therefore, H atom abstraction


rates by the radicals H, OH, CH3, CH3O, and HO2 from MDMJ amd MP2DMJ

were changed to analagous rates for secondary H atom abstraction in n-butane

(C4H10). The rates for n-butane from Healy et al. [33] were multiplied by 1.5 to

account for greater number of H atoms in the methyl ester radicals.

C8

C7

C6

C5

C4

C3

C2

1

OC

O

C9

C10

MD

+ R

+ RH

C8

C7

C6

C5

C4

C3

C2

1

OC

O

C9

C10

MDMJ

Figure 9.8: Abstraction of H atoms from the methoxy carbon atom by a reactive radical

species (R)

∙ The reaction rates for unimolecular decomposition involving C-C bonds in or around

the carbonyl group (see Figure 9.9) were changed to make them consistent with the

rate of analogous reaction for MB proposed by Dooley et al. [4]. These reactions

were written in the recombination direction in the original MD mechanism, while

Dooley et al. provided the rate constants for MB in the decomposition direction

and treated them for pressure dependency. We rewrote the MD reactions in the

decomposition direction and used Dooley et al.’s high pressure rate constant for

MB decomposition. The pressure dependency parameters were removed because

MD is a much larger molecule than MB, so the same pressure dependency would

not apply.

∙ The decomposition of the methoxy radical (CH3O) to formaldehyde (CH2O) and

the H radical was changed to

1.38x1021 ⋅ T−6.65 exp

(−33190 cal

mol

RT

)cm3

mol ⋅ s

based on the rate proposed by Tsang et al. [34]. This change was made to improve

the prediction of formaldehyde concentrations in the opposed-flow diffusion flame.


C8

C7

C6

C5

C4

C3

C2

1

OC

O

C9

C10

MD

C

OC

O

C8

C7

C6

C5

C4

C3

C2

1

O

C9

C10

C9H19CO

- CH3O

- C9H19

CH3OCO

C

2

1

OC

O

- C8H17

ME2J

- C7H15

C2

1

OC

O

C 3

MP3JC

8

C7

C6

C5

C4

C3

C2

1

O

O

C9

C10

- CH3

DAOJ

Figure 9.9: Unimolecular decomposition of MD via scission of C-C bonds in and around

the carbonyl group

Skeletal Chemical Kinetic Mechanism

The modified detailed chemical kinetic mechanism with 3012 species and 8820 reactions

is impractical for use in a one-dimensional flame code. However, mechanism reduction

methods are available to reduce the detailed mechanism’s size and complexity. The two

primary methods of reducing a mechanism are: i. elimination of unimportant species and

reactions and/or ii. lumping of species with similar structures (e.g., isomers) and reaction

pathways. When elimination is performed, the resulting mechanism is termed a “skeletal”

mechanism, and when lumping is performed the result is a “reduced” mechanism. In this

study, the DRG method is used to generate a skeletal version of the modified detailed

MD mechanism discussed previously.

Seshadri et al. [29] have already used the DRG method to generate an MD skele-

tal mechanism consisting of 125 species and 713 elementary reactions. The researcher

responsible for the development of the skeletal mechanism using the DRG methodology

was Dr. Tianfeng Lu2. The reduction of detailed chemical kinetic mechanisms is not

the focus of the present thesis study, so Dr. Lu’s assistance was sought in developing

2University of Connecticut, Department of Mechanical Engineering


a skeletal MD mechanism capable of predicting species and temperature profiles in the

opposed-flow diffusion flame. Below is a brief description of the DRG method, followed

by a discussion of the skeletal mechanism generated in this study.

The DRG is a systematic algorithm used for eliminating unimportant species and

reactions from a detailed mechanism. In this method a directed graph is used to detect all

the species in the mechanism that are strongly coupled to the fuel and oxidizer [28]. The

directed graph is generated using sampling points covering a wide range of temperatures,

pressures, fuel-oxygen-nitrogen mixture fractions, and fluid mixing conditions, such that

the couplings remain valid for combustion in various platforms (i.e., laminar flames, shock

tubes, jet-stirred reactors, etc.). The DRG methodology and its applicability to various

hydrocarbon mechanisms is available in the literature [35, 36].

The first step in the DRG method is to set up a sample space representing the various

combustion regimes of interest. The detailed mechanism is then run for all the points

in the sample space so that a directed graph of species coupling is generated. From the

directed graph, the user can select the desired error tolerance (e.g., 10%-30%), such that

unimportance species can be eliminated.

The previously proposed skeletal mechanism was capable of predicting experimental

extinction and ignition of MD in an opposed-flow diffusion flame with reasonably good

accuracy. However, its ability of predicting species profiles in an JSR and an opposed-flow

diffusion flame is poor. Figure 9.10 displays JSR simulations using various FAME and

alkane mechanisms at �=1.0, P=1013.25 kPa, �=1 s, 0.1% fuel mole fraction. n-Decane

simulations [37] and experimental data [38] match well, with both showing cool flame

reactivity in the range of 600-800 K. Simulations using Dooley’s MB mechanism [4] and

Seshadri et al.’s skeletal MD mechanism [29] indicate that these models lack cool flame

behaviour, and therefore they are not suitable for modeling real FAME low temperature

chemistry.

It is observed that Herbinet et al.’s detailed MD mechanism [27] displays cool flame

reactivity, but when compared to simulations for n-decane [37], the MD model appears

to overpredict the fuel’s reactivity. One would expect the reactivity of MD and n-decane

in the JSR to be similar since both contain C10 alkyl chains, and shock tube studies

also indicate that their reactivity is similar [27]. This study identified the problem as

incorrect activation energies for H-atom abstractions reactions by OH from secondary

C-H bonds, as mentioned previously. After correcting these values, the modified detailed

MD mechanism well predicts the reactivity of n-decane.


1.E-05

1.E-04

1.E-03

1.E-02

500 600 700 800 900 1000Temperature (K)

Mo

le F

ractio

n

MB (Dooley et al., 2008) MD (Seshadri et al., 2009)

MD (Herbinet et al., 2008) n-decane (Dagaut et al., 1995)

n-decane (Westbrook et al., 2008) MD (Sarathy et al., detailed)

MD (Sarathy et al., skelatal)

Figure 9.10: Experimental (symbols) and computed (lines with symbols) profiles obtained

from the oxidation of methyl decanoate, n-decane, and methyl butanoate in a JSR at

�=1.0, P=1013.25 kPa, �=1 s, 0.1% fuel mole fraction.

Figure 9.11 compares the skeletal MD mechanism of Seshadri et al., the detailed MD

mechanism by Herbinet et al. [27], and experimental data for RME in a JSR [26]. The

125 species mechanism by Seshadri et al. poorly predicts the concentrations of carbon

dioxide, carbon monoxide, and methane, especially at lower temperatures. This indicates

that the mechanism lacks chemical fidelity when compared to the detailed mechanism.

After discussion with Dr. Lu, it was decided that the sample space be modified to

produce an improved MD skeletal mechanism. The chosen sample space included the

following:

∙ Combustion in a closed homogeneous gas phase reactor at pressures of 101.325 kPa

and 1013.25 kPa, temperatures spanning 900-1800 K, equivalence ratios spanning

�=0.25-2.0, with mixtures of undiluted fuel plus air, and diluted fuel (i.e. 2% MD,

98% N2) plus air. These conditions were simulated using the SENKIN code in

CHEMKIN.


∙ Combustion in an open perfectly stirred reactor at pressures of 101.325 kPa and

1013.25 kPa, temperatures spanning 900-1500 K, equivalence spanning �=0.25-2.0,

residence times of 0.0001 s, 0.001 s , 0.01 s, and 1 s, and mixtures of undiluted fuel

plus air. These conditions were simulated using the PSR code in CHEMKIN.

The directed graph generated from the sampling points at the aforementioned con-

ditions was used to generate a skeletal mechanism consisting of 648 species and 2998

reactions. This skeletal mechanism accurately reproduces the low temperature reactiv-

ity predicted by the detailed mechanism, as shown in Figure 9.10. In addition, species

profiles for RME in the JSR, as shown in Figure 9.11, are also very similar for this

new skeletal mechanism and the detailed mechanism. This indicates that the proposed

skeletal mechanism is acceptable for modeling detailed chemical kinetic processes in the

JSR. Furthermore, it is suitable replacement to the detailed mechanism for predicting

the combustion properties of MD.

1.E-05

1.E-04

1.E-03

1.E-02

790 890 990 1090 1190

Temperature (K)

Mole

Fra

ction

carbon monoxidecarbon dioxidemethaneethylene

Herbinet et al., 2008

Seshadri et al, 2009

Sarathy et al, this study

Figure 9.11: Comparison of MD mechanisms (lines with symbols) and experimental data

(symbols) for RME in a JSR at �=1.0, P=101.325 kPa, �=1.0 s [26].


9.6.2 Thermochemical Data

The thermochemical data for MD published by Herbinet et al. [27] was used in this study.

The thermochemical properties for molecules and radicals were calculated using THERM

[39], which is a software based on the group and bond additivity methods proposed by

Benson [40]. THERM determines the thermochemical properties of a radical species by

applying a bond dissociation (BD) group increment to a stable molecule which reflects the

loss of an H atom from that species. The BD groups are based on specific bond energies

and difference in heat capacities and entropies for specific molecular classes [39]. For

more accurate calculations, the user can modify BD group values for specific molecular

classes based on ab initio thermochemical calculations, such as those by Sumathi and

Green [41].

With the assitance of Dr. Bill Pitz, a coauthor of the Herbinet et al. MD mechanism

[27], we found that the BD groups used in the calculation of thermochemical properties for

methyl ethanoate (ME), and it radicals ME2J and MEMJ, were incorrect in the original

work. In this study, the thermochemical properties were updated based on ME2J bond

energies calculated by El-Nahas et al. [9]. The new thermochemical parameters make

these molecules more stable and decrease their decomposition rates.

9.6.3 Transport Properties

The molecular transport parameters for species were obtained using a variety of methods.

This study builds upon the transport property database developed by Seshadri et al.

[29] for the 125 species contained within their MD skeletal mechanism. The transport

properties of species with no previously published data were determined as follows.

Most of the transport parameters that needed to be determined were for stable C2-C10

saturated and unsaturated methyl esters and their corresponding radicals. We assume

that the transport properties are similar for saturated and unsaturated methyl esters of

the same chain length, so we only performed calculations for saturated methyl esters and

used the same values for their unsaturated counterparts. For methyl ester radical species,

the transport properties of their stable counterpart were used.

For the stable saturated methyl ester species, this study used the correlations de-

veloped by Tee, Gotoh, and Stewart [42], as described by Wang and Frenklach [43], to

calculate the Lennard-Jones collision diameter and potential well depth using the Pc,

Tc, and Tb of the species. For C2-C6 methyl esters, these values were obtained from


the NIST Chemistry WebBook [44], but the Pc, Tc values were not available for C7-C10

methyl esters. However, a strong correlation exists between carbon chain length and Pc

and Tc for C3-C6 methyl esters, as shown in Figure 9.12, so we extrapolated the Pc and

Tc for larger methyl esters using a power law function.

100

200

300

400

500

600

700

0 2 4 6 8 10 12

Carbon Number

Cri

tical T

em

pera

ture

(K

)

20

25

30

35

40

45

50

Cri

tical P

ressu

re (

Pa)

Measured Tc

Extrapolated Tc

Measured Pc

Extrapolated Pc

Figure 9.12: Critical pressure (Pc) and critical temperature (Tc) for C2-C10 methyl esters.

Experimentally measured dipole moments were obtained from Mcclellan’s text [45].

This property was available for C3-C6 methyl esters but not for C7-C10 methyl esters.

The molecular dipole moment is a vector property that can be determined for an un-

known molecule using vector addition of known bond moments [46]. However, such a

method requires detailed information of the geometry of molecular bonds and their elec-

tronegativities (i.e., polarity). The problem is simplified in the present study because

experimentally measured dipole moments for saturated C3-C6 methyl esters fall in the

range of 1.61-1.76 Debyes. This indicates that the molecular dipole moment is created

by the ester moiety, which exhibits a strong polarity, and not the saturated alkyl chain,

which is nonpolar. Therefore, a dipole moment of 1.70 Debyes was used for the C7-C10

methyl esters.


Experimentally measured values for polarizability (�) in cubic Angstroms (A3) were

obtained from the CRC Handbook of Chemistry and Physics [47]. The polarizability

can also be determined using the empirical relation proposed by Bosque and Sales [48],

which allows estimation using the molecular formula (i.e., # of C, H, and O atoms), as

shown in Equation 9.1. Table 9.1 presents experimentally and empirically determined

polarizabilities for several FAME. The calculated values are within ±1% of the measured

values for C3-C6 methyl esters, so a high degree of confidence accompanies the calculated

polarizabilities for C7-C10 methyl esters.

� = 0.32 + 1.51 ∗#C + 0.17 ∗#H + 0.51 ∗#O (9.1)

Table 9.1: Experimentally and Empirically Determined Polarizabilities (A3) for FAME

Molecular Formula Experimental [47] Empirical [48]

Methyl ethanoate C3H6O2 6.94 6.89

Methyl propanoate C4H8O2 8.97 8.74

Methyl butanoate C5H10O2 10.41 10.59

Methyl pentanoate C6H12O2 - 12.44

Methyl hexanoate C6H14O2 - 14.29

Methyl heptanoate C7H16O2 - 16.14

Methyl octanoate C8H18O2 - 17.99

Methyl nonanoate C9H20O2 - 19.84

Methyl decanoate C10H22O2 - 21.69



The proposed skeletal MD mechanism was validated against experimental data obtained

in an MD opposed-flow diffusion flame. The opposed-flow diffusion flame allows the

study of fuel oxidation in a non-premixed laminar flame environment. Concentration

profiles for species were obtained by sampling the product gas at various points between

the two burner ports and then analyzing it using a variety of analytical techniques.


The measured species included methyl decanoate (MD), carbon monoxide (CO), car-

bon dioxide (CO2), formaldehyde (CH2O), methane (CH4), acetylene (C2H2), ethylene

(C2H4), ethane (C2H6), ketene (CH2CO), propane (C3H8), propene (C3H6), propyne

(pC3H4), 1-butene (1-C4H8), 1,3-butadiene (1,3-C4H6), 1-pentene (1-C5H10), 1-hexene

(1-C6H12), 1-heptene (C7H14), and 1-octene (1-C8H16). A species profile was identified

for C2H4O, but we are unable to determine if the compound is ethanal (i.e., acetaldehyde)

(CH3CHO) or ethenol (C2H3OH) since both have the same retention time on the GC

column. In addition, ethenol rapidly tautomerizes to acetaldehyde upon contact with

surfaces [49, 50], so we assume that the C2H4O measured in the GC is the combined

concentration of acetaldehyde and ethenol in the flame. Species below the experimental

limit of detection (LOD) (i.e., 5 ppm) included 1-butyne, n-butane, 2-butyne, trans-2-

butene, cis-2-butene, pentane, hexane, propanal, 2-propenal (i.e., acrolein), 2-propanone

(i.e., acetone), butanal, methyl 2-propenoate, methyl 3-butenoate, methyl 4-pentenoate,

and methyl 5-hexenoate.

9.7.2 Temperature, Fuel, and Hydrocarbon Species

Figure 9.13 displays the measured and predicted species and temperature profiles ob-

tained in the opposed-flow diffusion flame. The experimental results (solid symbols)

show that the MD concentration begins decreasing quickly at a distance of 5 mm from

the fuel port. As the fuel is consumed, the CO and CO2 concentrations begin rising.

All of the MD is consumed at a distance of approximately 8.25 mm from the fuel port,

which corresponds closely the visually observed flame front. Both the temperature and

CO2 concentrations reach their maximum at approximately 9.5 mm from the fuel port.

Just before the flame front, at around 7.75 mm from the fuel port, the concentrations

of hydrocarbon species reach their maximum. Besides CO and CO2, the most abundant

measured species are C2H4, C2H2, CH4, and C3H6.

It should be noted that these results do not display the shift in the measured species

profiles towards from the fuel port, which was observed previously in n-butanol experi-

ments (refer to Chapter 7). This shift was caused by a poor probe design which induced

flow field disturbances. The results for MD used a new probe design with did not disturb

the flame 3.

3The different probe designs are described in Chapter 4


This probe effect was resolved by moving to a new probe design 4.

The model’s prediction of temperature species profiles in the opposed-flow diffusion

is also shown in Figure 9.13 5. The model reproduces the experimentally measured

temperature profile very well. The reactivity of MD is also well predicted by the model.

The maximum concentration of CO2 is underpredicted by approximately 0.3%, while the

maximum concentration of CO is underpredicted by approximately 0.1%.

The model performs well qualitatively, in that it well reproduces the shape of the

experimental profiles and the points of maximum measured concentrations. In the fol-

lowing discussion, the model’s prediction is considered good if predicted maximum mole

fraction is within a factor 1.5 of the measured maximum mole fraction. The model per-

forms well at predicting the maximum concentrations of CH4, C2H6, C3H4, C3H6, 1-C4H8,

C8H16, C5H10, C6H12, C7H14, and 1,3-C4H6. The model moderately underpredicts (i.e.,

1.5-2 times) the maximum concentration of C2H4 and overpredicts the concentration of

C2H2. Both model and the experimental data indicate that the concentration of 1-alkenes

decreases with increasing carbon number (e.g., [C2H4]>[C3H6]>[1-C4H8]>[C5H10] etc.).

A reaction path analysis was performed for MD at 1040 K, the temperature at which

approximately where 50% of fuel is consumed. Approximately 98% of the fuel is consumed

via H-atom abstraction by H atoms (57%), OH radicals (5%), and CH3 radicals (30%).

Abstraction is favoured for H atoms bonded to the methoxy carbon (20%) and the �

carbon (16%), and then secondary (8% each) and tertiary H atoms (3%).

Figure 9.14 shows the fate of the MDMJ radical which is formed via H-atom abstrac-

tion from the methoxy carbon. Interestingly, �-scission is not favored for this radical

because it requires the breaking of a strong C-O bond. Instead, the MDMJ radical un-

dergoes isomerization to form the MD2J radical (61%) and the MD3J radical (36%).

The fate of the MD2J radical is shown in Figure 9.15, while that of the MD3J radical is

discussed later.

As shown in Figure 9.15, H-atom abstraction from the � carbon leads to the forma-

tion of the MD2J radical, which undergoes �-scission (99%) to form a 1-heptyl radical

and methyl 2-propenoate. The 1-heptyl radical eventually leads to the formation of

ethylene, 1-pentene, 1-butene, and the radicals C3H7 and C2H5, while the fate of methyl

2-propenoate is discussed in the next section. The measureable levels of ethylene, 1-

4The new probe design is described in Chapter 4 and improved results for methyl decanoate opposed-flow diffusion flames are shown in Chapter 9

5Appendix C contains larger versions of the graphs shown here


0

200

400

600

800

1000

1200

1400

1600

1800

2000

0 2 4 6 8 10 12 14 16 18 20


TE

MP

ER

AT

UR

E (

K)

measured

corrected

predicted

0%

2%

4%

6%

8%

10%

0 2 4 6 8 10 12 14 16 18 20


MO

LA

R C

ON

CE

NT

RA

TIO

N (

%) CO2

CO

MD

0

2000

4000

6000

8000

10000

12000

14000

0 2 4 6 8 10


MO

LA

R C

ON

CE

NT

RA

TIO

N (

PP

M) C2H4

C2H2

CH4

0

200

400

600

800

1000

1200

1400

1600

1800

2000

0 2 4 6 8 10


MO

LA

R C

ON

CE

NT

RA

TIO

N (

PP

M)

C2H6

1-C4H8

C3H6

C8H16

0

50

100

150

200

250

300

350

0 2 4 6 8 10


MO

LA

R C

ON

CE

NT

RA

TIO

N (

PP

M) C3H4

C5H10

C6H12

C7H14

1,3-C4H6

0

100

200

300

400

500

600

700

800

900

1000

0 2 4 6 8 10


MO

LA

R C

ON

CE

NT

RA

TIO

N (

PP

M) C2H4O

CH2O

CH2CO

Figure 9.13: Experimental and computed profiles obtained from the oxidation of MD in

an atmospheric opposed-flow flame (1.8% MD, 42% O2).


MDMJ

C8

C7

C6

C5

C4

C3

C2

1

OC

O

C9

C10

- 3%

+ C9H

19CO

OCH

2

MD2J

- 58%

- 39%

C8

C7

C6

C5

C4

C 3

C2

1

OC

O

C9

C10

MD3J

.....C

3

C

2

1

OC

O

Figure 9.14: Reaction pathway diagram for consumption of the MDMJ radical in the

opposed-flow diffusion flame at T=1040 K.

pentene, and 1-butene in the flame experiments agree with this reaction path analysis.

Approximately 56% of the fuel is consumed via abstraction of secondary H atoms from

the #4 to #9 carbon atoms. An example of the subsequent reaction pathways is shown

in Figure 9.16 for the MD4J radical. Approximately, 56% of the radical decomposes to

form 1-octene and the ME2J radical, while another 44% leads to the 1-pentyl radical

and methyl 4-pentenoate. The fate of the 1-pentyl radical is displayed in Figure 9.15,

which identifies ethylene as the final product species. The model indicates that methyl

4-pentenoate is consumed via unmimolecular decomposition to form an allyl radical (i.e.,

C3H5) and the ME2J radical. The ME2J either isomerizes (90%) to form the MEMJ

radical or decomposes (10%) to form ketene and a formaldehyde (i.e., via the methoxy

radical). The MEMJ radical goes to form formaldehyde and carbon monoxide (i.e., via

the acetyl radical). In this way, most of the MD4J radical goes to form formaldehyde,

carbon monoxide, ethylene, 1-octene, and ketene, all of which were measured in the

opposed-flow diffusion flame.

Other radicals formed via abstraction of secondary H atoms from other carbons in the

alkyl chain follow a similar path as MD4J. The radicals decompose via two routes, one

leading to an alkene and a methyl ester radical, and the other forming an unsaturated


MD2J

C8

C7

C6

C5

C4

C3

C

2

1

OC

O

C9

C10

- 99%

CC O

C

O

CC

CC

C

CC

+

MP2D

- 17% - 38%

CC

CC

C

CH2

CH2

CC

CC

CC

C

- 100% - C2H5

CC

CC C

- 77%

- C2H4

1-C5H

10

CC

C

CC

CC

- C3H7

- 44%

- 85%

CC

CC

1-C4H

8

- C3H7

C2H

4

Figure 9.15: Reaction pathway diagram for consumption of the MD2J radical in the


methyl ester and an alkyl radical. The alkyl radicals eventually result in the formation of

1-alkenes, and as shown previously the concentration of 1-alkenes decreases with increas-

ing carbon number. The model predicts that the unsaturated methyl esters are consumed

mainly by unimolecular decomposition to form an allyl radical and a saturated methyl

ester radical that is three carbon atoms shorter (e.g., methyl 6-heptenoate decomposes

to the radical methyl 4-butanoate, methyl 7-octenoate decomposes to the radical methyl

4-pentanoate, etc.). These methyl ester radicals with the radical site on the terminal car-

bon undergo �-scission to form ethylene and smaller methyl ester radicals. The process

continues until the radical site nears the carbonyl group and the radical decomposes to

a low molecular weight oxygenated species.

9.7.3 Oxygenated Species

Table 9.2 presents the maximum predicted and measured mole fractions of several unsat-

urated methyl esters, aldehydes, enals, ketones, ketenes, and enols. The measured and

predicted concentrations of oxygenated product species can add insight into the role of

the ester moiety during combustion. The following is a discussion of several important

oxygenated species and their chemistry in the flame.


MD4J

C8

C7

C6

C5

C

4

C3

C2

1

OC

O

C9

C10

- 90%

C

OC

OME2J

- 44%

- C5H11

CC

CC O

C

O

MF4D

- 73%

- C3H5

- 56%

C OC

OMEMJ

- 10%

O

CH3

CH2

C

O

+

- 100%

OCH

2+

CC

O

- 98%

- 99%- H

- CH3

OC

CC

CC

CCC

C+

1-C8H

16

Figure 9.16: Reaction pathway diagram for consumption of the MD4J radical in the


The model performs well at predicting the maximum concentrations of CH2CO, but

overpredicts the maximum concentrations of formaldehyde (CH2O) and underpredicts

acetaldehyde + ethenol (C2H4O). The measured concentration of CH2CO (i.e., ketene)

was 413 ppm, and the predicted concentration is 315 ppm. This is the first time ketene

concentrations have been measured in combustion studies of FAME. Figure 9.16 displays

the primary pathway which forms 83% of the ketene in the flame at 1040 K; the methyl

ester radical, ME2J, undergoes �-scission to form ketene and methoxy radical (CH3O).

Therefore, it is observed that the ester moiety contributes to the formation of ketene.

Approximately 86% of formaldehyde is formed via the decomposition of various

methyl ester radicals with a radical on the methoxy site, such as MDMJ, MEMJ, and

MP2DMJ. As shown in Figure 9.16 for MEMJ, these fuel radicals undergo �-scission to

form formaldehyde. Therefore, it is observed that the ester moiety contributes to the

formation of formaldehyde.

The maximum predicted concentration of formaldehyde is more than 2 times greater

than the measured concentration. This discrepancy can be attributed to either exper-

imental errors or modeling inaccuracies, so both are discussed here. The experimental

measurements for formaldehyde were performed using a GC/FID equipped with a meth-


Table 9.2: maximum Measured and Predicted Concentration of Oxygenated Species

Species Name Measured (ppm) Predicted (ppm)

in Mechanism

Formaldehyde CH2O 319 924

Ketene CH2CO 413 319

Ethanal+Ethenol CH3CHO+C2H3OH 100 15

Propanal C2H5CHO <LOD <1

2-propenal C2H3CHCO <LOD 44

2-propanone C3H6O <LOD 9

Methyl 2-propenoate MP2D <LOD 1149

Methyl 3-butenoate MB3D <LOD 120

Methyl 4-pentenoate MF4D <LOD 37

Methyl 5-hexenoate MH5D <LOD 29

anizer, which break the aldehyde’s C-O bond and replaces it with a C-H bond. This

method for detecting formaldehyde has yielded good results in JSR studies of FAME

[2, 22, 25, 51]. However, extractive sampling measurements in flames [2, 22, 52, 53] have

yielded similar discrepancies between measured and predicted formaldehyde, and one

group [53] suggests that formaldehyde may be lost due to polymerization in the sampling

lines.

Modeling overpredictions of formaldehyde concentrations have also been observed in

opposed-flow diffusion flames of MB [2, 4]. For MD at 1040 K, the present model predicts

that 86% of the formaldehyde is formed via decomposition of the metyl ester radical with

a radical on the methoxy site. A sensitivity analysis on formaldehyde at 1040 K revealed

that the predicted formaldehyde concentration is sensitive to the decomposition rates of

MDMJ, MEMJ, and MP2DMJ. The current rate estimate for the decomposition of these

radicals to formaldehyde are rough estimates, so detailed studies may reveal better rate

constants. Furthermore, the present model indicates that approximately 20% of CO in

the flame is formed directly from formaldehyde via the HCO radical. Since CO concen-

trations are underpredicted by approximately 1000 ppm, the 500 ppm overprediction of

formaldehyde by the model may be corrected by improving rate constants for reactions

linking CO and CH2O. Such modifications are beyond the scope of the present study,

and would require further investigation into fundamental experimental and theoretical


rate studies.

Both the model and experiments indicate that the C3 oxygenated species propanal,

2-propenal (i.e. acrolein), and 2-propanone (i.e. acetone) are not important product

species during the combustion of MD. However, a number of compounds are predicted

at appreciable levels in the flame, but were not detected in the experiments. It should

be noted that the flame sampling and analytical methods were capable of detecting

and quantifying these compounds. It is unlikely that these compounds were lost in the

sampling system since condensation and reactions in the sampling lines were minimized

by operating at low sampling pressures and using transfer lines heated to 200 ∘C.

The major discrepancy is for unsaturated methyl ester species. The experiments

did not measure detectable levels of any unsaturated methyl esters despite having the

appropriate sampling and analytical methods in place to measure them. Oxygenated

hydrocarbons typically create lower signal responses on an flame ionization detector, but

typically this affects detection levels of low molecular weight oxygenates (e.g., formalde-

hyde, acetaldehyde, etc.). Microliter injections of these unsaturated FAME verified that

the analytical instrument used in this study was suitable for their detection. Furthermore,

the accurate measurement of methyl decanoate in the flame samples indicates that the

sampling apparatus was adequate for high molecular weight FAME. It should be noted

that unsaturated methyl esters have been measured in experimental studies of methyl

hexanoate in a JSR at 10 atm[25], albeit at low concentrations.

Methyl 2-propenoate (i.e., MP2D) is the unsaturated FAME predicted in the high-

est concentration. Figure 9.17 displays the production and consumption pathways for

MP2D in the opposed-flow diffusion flame at 1040 K. 94% of the MP2D is formed via

�-scission of various methyl ester radicals with a radical site on the � carbon (e.g., MD2J,

etc.). The rest is formed via decomposition of the MP3J radical. Methyl 2-propenoate is

then consumed via H-atom abstraction reactions leading to the methyl ester radical with

a radical on the methoxy carbon (i.e., MP2DMJ). H abstraction is favoured from the

methoxy site because the H atoms bonded to the vinylic carbons have higher BDEs. The

MP2DMJ radical undergoes �-scission to ultimately form acetylene, carbon monoxide,

formaldehyde. High concentrations of methyl 2-propenoate are predicted because multi-

ple pathways lead to various methyl ester radicals with a radical site on the � carbon,

and these form methyl 2-propenoate faster than it can be consumed via H-atom abstrac-

tion reactions. The rate parameters for the methyl 2-propenoate consumption have been

determined based on analogies with saturated methyl ester molecules and unsaturated


hydrocarbons. Fundamental experimental and theoretical studies on H abstraction and

unimolecular decomposition reactions may improve the predicted concentration of methyl

2-propenoate.

Another explanation for the discrepancy between the model and predicted values is

possible decomposition of methyl 2-propenoate upon contact with hot surfaces in the

sampling line. Such a mechanism is plausible given the unstable nature of unsaturated

FAME, especially those of low molecular weight. It is possible that FAME are reacting

in the sampling line to form acetaldehyde+ethenol compounds which were measured

in appreciable quantities, but not predicted to be significant by the model. Further

investigation of the reactivity of methyl 2-propenoate at the conditions in the sampling

line (i.e., T=388 K, P=6-8 kPa) is required.

MP2D

CC O

C

O

.....C

3

C 2

1

OC

O

- CH2O

+ 94%

CC O

C

O

- H- 90 %

- 100 %

CC

C

O

MP2DMJ

- CxHy

- 100 %

- C2H3

OC

MX2J

Figure 9.17: Reaction pathways for the formation and consumption of methyl 2-

propenoate in the opposed-flow diffusion flame at T=1040 K.

The Fate of the Ester Moiety

Previous studies on methyl butanoate combustion discussed the fate of the ester moiety

at high temperatures. When applied to different experimental conditions (e.g., flames,

premixed reactors, etc.), the MB models predict that the methoxycarbonyl radical is an

important combustion intermediate. We ran the opposed-flow diffusion flame simulations


for MB using the experimental conditions of Gail et al. [2] and the mechanism of Dooley

et al. [4] to calculate how much of the fuel decomposes to form the methoxycarbonyl

radical at high temperatures. The results indicate that approximately 23% of the fuel

ends up in the methoxycarbonyl radical, as shown in Figure 9.18. In Figure 9.3, it

was shown that the methoxycarbonyl radical primarily decays to form a methyl radical

and CO2. From a soot reduction standpoint this decarboxylation of the ester is not an

efficient use of fuel-bound oxygen because two oxygen atoms are bonded to one carbon

atom. It would be more efficient if the ester moiety led to the production of CO since each

oxygen atom in the ester group would sequester one carbon atom from participating in

the production of soot. Many researchers have hypothesized that the long chain FAME in

biodiesel undergo similar reaction pathways as methyl butanoate, and therefore the ester

moiety in biodiesel is not as efficient at supressing soot compared to other oxygenated

moieties (e.g., aldehydes, ethers, alcohols, etc.). The present study on methyl decanoate

offers additional insights to the aforementioned hypothesis.

MB

C4

C3

C2

1

OC

O

- 27%

- H- 16%

- H

CC

C OC

OMB3J

- 84%

- C3H6

C

OC

O

CH3OCO

C4

C3

C

2

1

OC

O

CC O

C

O

MB2J

MP2D

- 84%

sum of all pathways

- 40%

- CH3

Figure 9.18: Reaction pathways leading to the formation of the methoxycarbonyl radical

in the opposed-flow diffusion flame at T=1030 K given the experimental and modeling

conditions of Gail et al. [2].

The above analysis on MB indicates that the radical sites on the � and � carbons can

lead to the methoxycarbonyl radical. At 1040 K, approximately 8% of MD is consumed

via H-atom abstraction from the � carbon leading to the MD3J radical. In addition, 8%

of MD leads to the MD3J radical via the MDMJ radical, as shown in Figure 9.14 . This


is 30% less than what was observed for methyl butanoate [2] under similar conditions, or

even for methyl hexanoate in a JSR at 950 K [25]. Similarly, 16% of MD is consumed via

H-atom abstraction from the � carbon whereas it is 27% for MB. These pathways in MD,

and for longer chain FAME too, becomes less important because the number of H-atom

abstraction sites increases with chain length. Furthermore, after absrtraction from the �

carbon, the resulting MD3J radical undergoes �-scission at equal rates to form either i.

1-nonene plus the methoxycarbonyl radical or ii. methyl 3-butenoate plus and a 1-hexyl

radical. However, the analogous radical in MB (i.e., MB3J) strongly favours the route

leading to the methoxycarbonyl radical, since �-scission forming methyl 3-butenoate is

thermodynamically unfavourable for the MB3J radical. The importance of fuel radical

isomerization reactions are become more important as the size of the FAME increases.

Similar to the previous studies on MB, the present MD mechanism predicts that the

methoxycarbonyl radical decomposes to form CO2. It is estimated that approximately

6% of the fuel ends up forming the CO2 via the methoxycarbonyl radical, which is much

lower than previous estimates based on MB (i.e., 23%). This number is expected to be

even lower in longer chain FAME because of the increased number of potential pathways

of fuel consumption.

It should be noted that the amount of fuel that ends up in the methoxycarbonyl

radical depends on the experimental conditions (i.e., temperature, pressure, mixture

fraction, etc.). The above analysis applies to high temperature oxidation where H-atom

abstraction is predominant. Simulations should be conducted for conditions in which uni-

molecular decomposition is predominant for additional insights. In any case, this study

suggests that decarboxylation of the ester group in long chain FAME is not significant.

In fact, much of the original fuel bound oxygen leads directly to oxygenated species such

as carbon monoxide, formaldehyde, and ketene, wherein one oxygen atom is bonded to

one carbon atom. Therefore, the soot reducing efficiency of long chain FAME may be

better than previously believed.


The proposed skeletal mechanism for MD was also validated against experimental JSR

data for RME at �=1.0, P=101.325 kPa, �=1.0 s [26]. Since MD is a smaller molecule

than RME, the inlet mole fraction of MD was proportionally increased to match the inlet

carbon flux of RME, as described by Herbinet et al. [27]. The comparison between the


model predictions and experimental data is shown in Figures 9.19 and 9.20.

The skeletal mechanism performs similarly to the detailed MD mechanism proposed

by Herbinet et al. [27]. The concentrations of CO2, CO, C2H4, O2, H2, CH4, and C3H6

are well predicted by the proposed skeletal mechanism. However, the concentrations of

1-C4H8, 1-C5H10, and 1-C6H12 are overpredicted by the model, which was also observed

by Herbinet et al. [27]. The prediction of alkenes is incorrect because RME consists of

longer chain FAME with various degrees of unsaturation, while MD is fully saturated

and has a small chain. Although there is no data available, RME is likely to have larger

carbon chains leading to the formation of larger 1-alkenes (e.g., >C8) than would MD;

therefore, MD over predicts the concentrations of the smaller 1-alkenes (e.g., C4-C6).

1.E-05

1.E-04

1.E-03

1.E-02

790 890 990 1090 1190

Temperature (K)

Mo

le F

ractio

n

O2 COCO2 H2CH4 C2H2co2 detailed o2 detailed

Figure 9.19: Comparison of proposed MD skeletal mechanism and experimental data for

RME in a JSR at �=1.0, P=101.325 kPa, �=1.0 s [26].

9.8 Conclusions and Recommendations

This study is the first to present experimental data for methyl decanoate combustion

that can be used for validating chemical kinetic mechanisms. The combustion of MD

in the opposed-flow diffusion flame generates typical hydrocarbon combustion products

(e.g., CO, CO2, CH4, C2H4, etc.). Of particular interest is the production of C5-C8 1-

alkenes which are formed after �-scission of fuel radicals. The production of low molecular


1.E-07

1.E-06

1.E-05

1.E-04

1.E-03

790 890 990 1090 1190

Temperature (K)

Mo

le F

ractio

n

C3H6

1-C4H8

1-C5H10

1-C6H12

Figure 9.20: Comparison of proposed MD skeletal mechanism (lines with symbols) and

experimental data (symbols) for RME in a JSR at �=1.0, P=101.325 kPa, �=1.0 s [26].

weight oxygenated compounds such as formaldehyde, ketene, and isomers of C2H4O is

also observed. The absence of acrolein and acetone in the measured data suggests that

FAME combustion does not produce these species.

The experimental data presented herein was used to validate an improved skeletal

mechanism for the high temperature oxidation of MD. Initially, modifications were made

to a previously proposed detailed mechanism for MD, and then an improved DRG al-

gorithm was performed to create a skeletal mechanism. This new skeletal mechanism

provides excellent qualitative prediction of experimentally measured species and temper-

ature profiles in the MD opposed-flow diffusion flame. This study highlights the effective-

ness of the DRG method in producing a mechanism that is computationally practical for

one-dimensional flame simulations yet also retains a high level of chemical fidelity. The

only major discrepancy between the proposed mechanism and the experimental data was

for methyl 2-propenoate, and it is suggested that the experimental sampling apparatus

be reassessed for accurate measurement of this compound.

The validated MD mechanism provides many new insights into the combustion chem-

istry of FAME. Firstly, the combustion of long chain FAME proceeds in much the same

way as a long chain alkane; however, the ester moiety does introduce unique combustion

pathways. It was shown that MD displays different combustion pathways than smaller


methyl esters, such as MB, due to the greater number of possible reaction pathways. This

study suggests that fuel bound oxygen in MD (i.e., the ester moiety) leads primarily to

the formation of carbon monoxide and low molecular weight oxygenated compounds, and

very little actually ends up in the form of CO2. Therefore, under the conditions of this

experimental and modeling study, the oxygen in the ester moiety does a good job of

sequestering carbon from participating in soot production. However, these findings need

to be verified across other fundamental combustion platforms with varying temperatures,

pressures, mixture fractions, and mixing conditions.

The proposed mechanism also indicates that unsaturated methyl esters are an im-

portant intermediate in the combustion of saturated FAME. The present mechanism

derives kinetic information for unsaturated methyl esters from analogous reaction path-

ways for alkenes. This approximation needs to be improved via fundamental studies on

the thermochemical properties of unsaturated FAME. Ab initio calculations for unsatu-

rated FAME will not only improve mechanisms for saturated FAME, but they will help

build comprehensive mechanisms for real biodiesel, which is a mixture of saturated and

unsaturated FAME.

Future experimental and modeling research should be directed towards unsaturated

FAME because current biodiesel fuels from soybean and canola oil, as well as next gen-

eration biodiesel from microalgal oil, have a high concentration of unsaturated FAME.

As shown in Figure 9.1 the unsaturated FAME typically found in biodiesel have double

bonds far away from the carbonyl group (e.g. methyl 9-octadecenoate, methyl 9,12-

octadecadienoate). However, as shown in this study, low molecular weight unsaturated

methyl esters with double bonds near the carbonyl group (i.e., methyl 2-propenoate,

methyl 3-butenoate, etc.) are important combustion intermediates. Therefore, this thesis

study recommends that future research be directed towards understanding the individual

effects of i. double bonds which are near the carbonyl group by using methyl 2-propenoate

and methyl 2-octenoate as surrogate compounds, and ii. double bonds which are far the

carbonyl group using methyl 9-nonenoate as as a surrogate compound6

6These surrogate compounds are selected because they represent the types of molecules encounteredin FAME combustion and they are readily available from chemical suppliers.

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CH3, C2H5, n-C3H7]: Implications for the gas-phase chemistry of enols,” Journal

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Part IV

Closing

195

Chapter 10

Scientific Contribution

This dissertation researched the combustion kinetics of biofuels used in transportation

applications. The chemical compounds (i.e., oxygenates) in biofuels are structurally

different than those in conventional hydrocarbon fuels, so this study determined the role

of molecular structure on biofuel combustion and emissions. Chemical kinetic mechanism

were used to describe the molecular level transformation of reactants into products via

a series of elementary steps. Fundamental combustion experiments were performed, and

the acquired data was used to validate chemical kinetic mechanisms for biobutanol (i.e.,

n-butanol) and biodiesel (i.e., FAME). In addition, since biobutanol is a new biofuel

that has not been critically assessed for sustainability, this dissertation also performed

an LCA of biobutanol.

Biofuel combustion was primarily studied using experimentally measured species and

temperature profiles in an opposed-flow diffusion flame. Additionally, JSR species profiles

and laminar flame speed data1 were used to study biofuel combustion. Comprehensive

chemical kinetic mechanisms for the combustion of n-butanol and FAME were developed,

and then validated using the experimental data. The effects of molecular structure on

combustion were elucidated using the chemical kinetic mechanism to identify the domi-

nant routes of reactant consumption and product formation.

The experimental results indicate that the combustion of n-butanol and FAME pro-

duced appreciable quantities of carbon dioxide, carbon monoxide, water, methane, and

ethylene. Therefore, the emissions profiles are similar to those of conventional hydro-

carbon fuels. The combustion of biofuels produces higher levels of oxygenated product

1These experiments were performed by collaborating researchers.

196

Chapter 10. Scientific Contribution 197

species, such as aldehydes and ketones. These species sequester carbon in carbon-oxygen

bonds, and therefore prevent carbon from forming soot precursors (e.g., acetylene). How-

ever, these oxygenated species may have adverse environmental and health impacts that

warrants further investigation.

The chemical kinetic mechanisms developed in this study correlates well with the

experimental data presented herein. The modeling results indicated that the oxygenated

molecules in biofuels follow similar combustion pathways to the hydrocarbons in petroleum

fuels. The mechanisms suggest that oxygenated product species are formed directly from

the oxygenated moieties in the parent biofuel molecules (i.e., the original carbon-oxygen

bonds are preserved through the combustion process).

The detailed chemical kinetic mechanisms developed in this thesis have been vali-

dated against a range of experimental data; however, more data is required to test the

mechanisms across a wider range of temperatures, pressures, mixing conditions, and

mixture fractions. Ultimately, these mechanisms will be an important tool for studying

combustion in practical applications, such as internal combustion engines. Additional

work is needed to reduce the complexity of the mechanisms in order to model multi-

dimensional systems; however, the detailed mechanisms herein provide the fundamental

scientific basis for such reduced mechanisms.

Appendix A

Schematic of the opposed-flow diffusion flame setup. Copyright c⃝ 2009 by Tim Chan.

N2

O2

Com

pre

sse

d

nitro

gen

sup

ply

Com

pre

sse

d

oxyge

n

sup

ply

Flo

w m

ete

r

Com

pre

sse

d a

ir

(fro

m b

uild

ing)

Fu

el

(Ma

ste

rFle

x)

Pum

p d

rive

Ato

miz

er

Bu

rne

r

(Te

led

yn

e

Ha

stin

gs)

Ma

ss f

low

m

ete

r &

con

tro

ller

Hea

tin

g t

ap

e

Ma

ss f

low

me

ter

&

con

tro

ller

Eva

po

rize

rOp

po

sed

-Flo

w D

iffu

sio

n F

lam

e S

etu

p

Co

pyri

gh

t ©

20

09

by T

im C

ha

n

198

Appendix B

This Appendix contains full-sized figures and complete datasets for the n-butanol com-

bustion study

199


The following figures are comparisons of the experimental and predicted concentration

profiles obtained from the oxidation of n-butanol in a JSR at �=1, P=101.3 kPa, �=0.07s

1E-05

1E-04

1E-03

1E-02

750 850 950 1050 1150 1250Temperature (K)

Mo

le F

rac

tio

n

CO

CO2

CH4


1E-05

1E-04

1E-03

750 850 950 1050 1150 1250

Temperature (K)

Mo

le F

rac

tio

n

C4H9OH

C3H7CHO

C3H6

1-C4H8


1E-05

1E-04

1E-03

750 850 950 1050 1150 1250Temperature (K)

Mo

le F

rac

tio

n

C2H2

C2H4

CH3CHO

C2H6



profiles obtained from the oxidation of n-butanol in a JSR at �=2, P=101.3 kPa, �=0.07

s

1E-05

1E-04

1E-03

1E-02

750 850 950 1050 1150 1250Temperature (K)

Mo

le F

rac

tio

n

CO

CO2

CH4


1E-05

1E-04

1E-03

750 850 950 1050 1150 1250

Temperature (K)

Mo

le F

rac

tio

n

C4H9OH

C3H7CHO

C3H6

1-C4H8


1E-05

1E-04

1E-03

750 850 950 1050 1150 1250

Temperature (K)

Mo

le F

rac

tio

n

C2H2

C2H4

CH3CHO

C2H6


1E-05

1E-04

1E-03

1E-02

750 850 950 1050 1150 1250

Temperature (K)

Mo

le F

rac

tio

n

H2O

H2

CH2O

O2



profiles obtained from the oxidation of n-butanol in a JSR at �=0.5, P=101.3 kPa, �=0.07

s

1E-05

1E-04

1E-03

1E-02

750 850 950 1050 1150 1250

Temperature (K)

Mo

le F

rac

tio

n

CO

CO2

CH4


1E-05

1E-04

1E-03

750 850 950 1050 1150 1250

Temperature (K)

Mo

le F

rac

tio

n

C4H9OH

C3H7CHO

C3H6

1-C4H8


1E-05

1E-04

1E-03

750 850 950 1050 1150 1250Temperature (K)

Mo

le F

rac

tio

n

C2H2

C2H4

CH3CHO

C2H6


1E-05

1E-04

1E-03

1E-02

1E-01

1E+00

750 850 950 1050 1150 1250

Temperature (K)

Mo

le F

rac

tio

n

H2O

H2

CH2O

O2



profiles obtained from the oxidation of n-butanol in a JSR at �=0.25, P=101.3 kPa,

�=0.07 s

1E-05

1E-04

1E-03

1E-02

750 850 950 1050 1150 1250

Temperature (K)

Mo

le F

rac

tio

n (

PP

M)

CO

CO2

CH4


1E-05

1E-04

1E-03

750 850 950 1050 1150 1250

Temperature (K)

Mo

le F

rac

tio

n

C4H9OH

C3H7CHO

C3H6

1-C4H8


1E-05

1E-04

1E-03

750 850 950 1050 1150 1250

Temperature (K)

Mo

le F

rac

tio

n

C2H2

C2H4

CH3CHO

C2H6


1E-05

1E-04

1E-03

1E-02

1E-01

1E+00

750 850 950 1050 1150 1250

Temperature (K)

Mo

le F

rac

tio

n

H2O

H2

CH2O

O2



profiles obtained from the oxidation of n-butanol in a JSR at �=1.0, P=1013 kPa, �=0.7

s

1E-06

1E-05

1E-04

1E-03

1E-02

750 850 950 1050 1150

Temperature (K)

Mo

le F

rac

tio

n

CO old

CO new

CO2 old

CO2 ew

CH4 old

CH4 new


1E-06

1E-05

1E-04

1E-03

750 850 950 1050 1150

Temperature (K)

Mo

le F

rac

tio

n

C4H8 oldC4H8 new

C4H9OH oldC4H9OH new

C3H7CHO oldC3H7CHO new

C3H6 oldC3H6 new


1E-06

1E-05

1E-04

1E-03

750 850 950 1050 1150

Temperature (K)

Mo

le F

rac

tio

n

C2H2 old C2H2 new

C2H4 old C2H4 new

CH3CHO old CH3CHO new

C2H6 old C2H6 new


1E-06

1E-05

1E-04

1E-03

1E-02

750 850 950 1050 1150

Temperature (K)

Mo

le F

rac

tio

n

H2O oldH2O new

H2 oldH2 new

CH2O oldCH2O new




s

1E-06

1E-05

1E-04

1E-03

1E-02

750 850 950 1050 1150

Temperature (K)

Mo

le F

racti

on

CO old

CO new

CO2

CH4

CO2 new

CH4 new


1E-06

1E-05

1E-04

1E-03

750 850 950 1050 1150

Temperature (K)

Mo

le F

rac

tio

n

C4H8 oldC4H8 new



C3H6 oldC3H6 new


1E-06

1E-05

1E-04

1E-03

750 850 950 1050 1150Temperature (K)

Mo

le F

rac

tio

n

C2H2 old C2H2 new

C2H4 old C2H4 new


C2H6 old C2H6 new


1E-06

1E-05

1E-04

1E-03

1E-02

750 850 950 1050 1150

Temperature (K)

Mo

le F

rac

tio

n

H2O oldH2O new

H2 oldH2 new

CH2O oldCH2O new




s

1E-06

1E-05

1E-04

1E-03

750 850 950 1050 1150

Temperature (K)

Mo

le F

rac

tio

n

C4H8 oldC4H8 new



C3H6 oldC3H6 new


1E-06

1E-05

1E-04

1E-03

1E-02

750 850 950 1050 1150

Temperature (K)

Mo

le F

rac

tio

n

CO oldCO new

CO2 oldCO2 new

CH4 oldCH4 new


1E-06

1E-05

1E-04

1E-03

750 850 950 1050 1150

Temperature (K)

Mo

le F

rac

tio

n

C2H2 old C2H2 new

C2H4 old C2H4 new


C2H6 old C2H6 new


1E-06

1E-05

1E-04

1E-03

1E-02

750 850 950 1050 1150

Temperature (K)

Mo

le F

rac

tio

n

H2O oldH2O new

H2 oldH2 new

CH2O oldCH2O new



profiles obtained from the oxidation of n-butanol in an atmospheric opposed-flow flame

(5.89% C4H9OH, 42% O2).

0

200

400

600

800

1000

1200

1400

1600

1800

2000

0 5 10 15 20


TE

MP

ER

AT

UR

E (

K)

measured

corrected

predicted


0%

2%

4%

6%

8%

10%

12%

0 2 4 6 8 10 12 14 16 18 20


MO

LA

R C

ON

CE

NT

RA

TIO

N (

%)

CO2

CO

C4H9OH


0

2000

4000

6000

8000

10000

12000

0 2 4 6 8 10 12


MO

LA

R C

ON

CE

NT

RA

TIO

N (

pp

m) C2H4

C2H2

CH4


0

500

1000

1500

2000

2500

3000

3500

4000

4500

0 2 4 6 8 10 12


MO

LA

R C

ON

CE

NT

RA

TIO

N (

pp

m)

C2H6

C4H8

C3H6


0

50

100

150

200

250

300

350

400

0 2 4 6 8 10 12


MO

LA

R C

ON

CE

NT

RA

TIO

N (

pp

m) C3H7CHO

C3H8

C3H4

1,3-C4H6


0

200

400

600

800

1000

1200

1400

0 2 4 6 8 10 12


MO

LA

R C

ON

CE

NT

RA

TIO

N (

pp

m) CH2O

CH3CHO


The following figures are the experimental concentration profiles obtained from the

oxidation of n-butane in an atmospheric opposed-flow flame (5.89% C4H9OH, 42% O2).

0%

2%

4%

6%

8%

10%

12%

0 2 4 6 8 10 12 14 16


MO

LA

R C

ON

CE

NT

RA

TIO

N (

%) CO2

CO

C4H10


0

1000

2000

3000

4000

5000

6000

7000

8000

9000

10000

0 2 4 6 8 10 12


MO

LA

R C

ON

CE

NT

RA

TIO

N (

PP

M) C2H4

C2H2

CH4


0

200

400

600

800

1000

1200

1400

1600

0 2 4 6 8 10 12


MO

LA

R C

ON

CE

NT

RA

TIO

N (

PP

M)

C2H6

C4H8

C3H6


0

50

100

150

200

250

300

350

400

0 2 4 6 8 10 12


MO

LA

R C

ON

CE

NT

RA

TIO

N (

PP

M)

CH2O

CH3CHO


0

10

20

30

40

50

60

70

80

0 2 4 6 8 10 12


MO

LA

R C

ON

CE

NT

RA

TIO

N (

PP

M)

C3H7CHO

C3H8

Appendix C

This Appendix contains full-sized figures and complete datasets for the methyl decanoate

combustion study.

239


The following figures are comparisons of experimental and computed profiles obtained

from the oxidation of MD in an atmospheric opposed-flow flame (1.8% MD, 42% O2).

0

200

400

600

800

1000

1200

1400

1600

1800

2000

0 2 4 6 8 10 12 14 16 18 20


TE

MP

ER

AT

UR

E (

K)

measured

corrected

predicted


0%

2%

4%

6%

8%

10%

0 2 4 6 8 10 12 14 16 18 20


MO

LA

R C

ON

CE

NT

RA

TIO

N (

%) CO2

CO

MD


0

2000

4000

6000

8000

10000

12000

14000

0 2 4 6 8 10


MO

LA

R C

ON

CE

NT

RA

TIO

N (

PP

M) C2H4

C2H2

CH4


0

200

400

600

800

1000

1200

1400

1600

1800

2000

0 2 4 6 8 10


MO

LA

R C

ON

CE

NT

RA

TIO

N (

PP

M)

C2H6

1-C4H8

C3H6

C8H16


0

100

200

300

400

500

600

700

800

900

1000

0 2 4 6 8 10


MO

LA

R C

ON

CE

NT

RA

TIO

N (

PP

M) C2H4O

CH2O

CH2CO


0

50

100

150

200

250

300

350

0 2 4 6 8 10


MO

LA

R C

ON

CE

NT

RA

TIO

N (

PP

M)

C3H4

C5H10

C6H12

C7H14

1,3-C4H6

chemical kinetic modeling of biofuel combustion · chemical kinetic modeling of biofuel combustion...

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