EXPERIMENTAL AND STATISTICAL INVESTIGATION OF AUSTRALIAN NATIVE PLANTS FOR SECOND-GENERATION BIODIESEL PRODUCTION Md Jahirul Islam B.Sc., M.Sc. Supervisors: Dr Wijitha Senadeera, A/Prof Richard Brown, Prof Zoran Ristovski, A/Prof Ian O’Hara A thesis submitted in fulfilment of the requirements for the degree of Doctor of Philosophy (PhD) School of Chemistry, Physics and Mechanical Engineering Faculty of Science and Engineering Queensland University of Technology 2015
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EXPERIMENTAL AND STATISTICAL
INVESTIGATION OF AUSTRALIAN NATIVE
PLANTS FOR SECOND-GENERATION
BIODIESEL PRODUCTION
Md Jahirul Islam
B.Sc., M.Sc.
Supervisors: Dr Wijitha Senadeera, A/Prof Richard Brown, Prof Zoran Ristovski, A/Prof Ian O’Hara
A thesis submitted in fulfilment of the requirements for the degree of
Doctor of Philosophy (PhD)
School of Chemistry, Physics and Mechanical Engineering
Faculty of Science and Engineering
Queensland University of Technology
2015
To my parents for their love, support and encouragement
Keywords
Alternative energy, Artificial neural networks (ANN), Australian native plants, Biofuel,
Biodiesel, First-generation biodiesel, Multi criteria decision analysis (MCDA),
efficiency, cylinder peak pressure and specific nitrogen oxide (NOx) particle mass (PM)
emissions. At the same time, brake specific fuel consumption and particle number emissions
were found to be higher from Beauty leaf biodiesel compared with that of conventional diesel.
However, this variation is not unusual and is commonly found in conventional biodiesels,
mainly due to variations in physico-chemical properties between biodiesel and conventional
diesel.
This thesis advances knowledge in the field biofuel technology, by delivering an extensive
database of the properties of second-generation biodiesel and its application in a modern
diesel engine. The research methodology and numerical model developed in this study can be
used for a broad range of biodiesel feedstock and will facilitate further biodiesel research in
the future. The experimental study on modern automobile engines using Beauty leaf biodiesel
indicated the suitability of Australian native plants for use as fuel for modern automobile
diesel engines without engine modification. Therefore, the findings of this study are expected
to serve as the basis for further developments in the use of Beauty leaf as a feedstock for
industrial scale biodiesel production.
i
Table of Contents
Table of Contents ..................................................................................................................................... i
List of Figures ......................................................................................................................................... v
List of Tables ...................................................................................................................................... viii
List of Abbreviations ............................................................................................................................... x
List of Publication ................................................................................................................................ xii
Published journal papers: ..................................................................................................................... xii
Conference papers: ............................................................................................................................... xii
Statement of Original Authorship ........................................................................................................ xiv
Acknowledgements ............................................................................................................................... xv
2.1 Biodiesel .................................................................................................................................... 19 2.1.1 Biodiesel feedstock ......................................................................................................... 20 2.1.2 First and second-generation biodiesel ............................................................................. 21 2.1.3 Potential second-generation biodiesel feedstock ............................................................ 23 2.1.4 Production of biodiesel ................................................................................................... 26 2.1.5 Chemical composition of biodiesel ................................................................................. 29 2.1.6 Biodiesel standards ......................................................................................................... 31 2.1.7 Fuel properties ................................................................................................................ 32 2.1.7.1 Kinematic viscosity ........................................................................................................ 33 2.1.7.2 Density ............................................................................................................................ 35 2.1.7.3 Cetane number (CN) ....................................................................................................... 36 2.1.7.4 Heating (calorific) Value ................................................................................................ 37 2.1.7.5 Flash point ...................................................................................................................... 37 2.1.7.6 Oxidation stability .......................................................................................................... 38 2.1.7.7 Cold temperature properties ........................................................................................... 38 2.1.7.8 Lubricity ......................................................................................................................... 40 2.1.7.9 Iodine value .................................................................................................................... 41
2.2 Biodiesel as a diesel Engine Fuel ............................................................................................... 41 2.2.1 Engine performance ........................................................................................................ 42 2.2.2 Exhaust emissions ........................................................................................................... 44
2.3 Artificial neural networks .......................................................................................................... 47 2.3.1 ANN in predicting engine emission and performance .................................................... 51 2.3.2 ANN in predicting fuel properties .................................................................................. 53
2.4 ANN modeling of second-generation biodiesel ......................................................................... 57
3.2 Data Collection .......................................................................................................................... 65
3.3 Results and discussion ............................................................................................................... 71 3.3.1 Chemical composition .................................................................................................... 71 3.3.2 Fuel properties ................................................................................................................ 74 3.3.3 Correlation of chemical composition and fuel properties ............................................... 78 3.3.4 Principle component analysis ......................................................................................... 82 3.3.5 ANN model development ............................................................................................... 84 3.3.6 Evaluation of ANN model performance ......................................................................... 88
CHAPTER 7: DIESEL ENGINE TESTING WITH BIODIESEL OF CONTROLLED CHEMICAL COMPOSITION ........................................................................................................ 177
7.2 Materials and methods ............................................................................................................. 181 7.2.1 Engine and fuel specification ........................................................................................ 181 7.2.2 Exhaust sampling and measurement system ................................................................. 183
7.3 Results and Discussion ............................................................................................................ 186 7.3.1 Specific PM emissions .................................................................................................. 186 7.3.2 Specific PN emissions .................................................................................................. 187 7.3.3 Particle number size distribution .................................................................................. 189 7.3.4 Particle median size ...................................................................................................... 189 7.3.5 NOx emissions .............................................................................................................. 190 7.3.6 Influence of fuel physical properties and chemical composition on particle
emissions ...................................................................................................................... 192 7.3.7 Comparison of engine performance and particle emissions among used
APPENDICES ................................................................................................................................... 266 APPENDIX A: MatLab code for ANN models training......................................................... 266 APPENDIX B: The eigenvalue for each of the PCs ............................................................. 270
v
List of Figures
Figure 1-1: Outline on the thesis ................................................................................ 11
Figure 2-1. Biodiesel feedstocks around the world .................................................... 21
Figure 2-9: Multi-layer ANN model .......................................................................... 49
Figure 2-10: Working principle of ANN ................................................................... 49
Figure 2-11: Comparison of the performance of between ANN and various linear and non-linear prediction techniques ................................................ 50
Figure 2-12: Proposed structure of ANN model ........................................................ 58
Figure 3-1: Number and average weight in percentages of fatty acid methyl esters found in the samples .......................................................................... 74
Figure 3-2: Correlation of (a) C18:2 with oxidation stability; (b) H2 with CN ......... 77
Figure 3-3: Correlation of (a) ANDB with CN; (b) ANBD with IV ......................... 79
Figure 3-4: Effect of ACL on biodiesel (a) kinematic viscosity (KV) and (b) higher heating value (HHV) ......................................................................... 81
Figure 3-5: Principle component analysis and correlation of biodiesel properties with chemical composition: ........................................................ 84
Figure 3-6: Proposed flow chart of ANN prediction model development ................. 85
Figure 3-7: Structure of ANN .................................................................................... 86
Figure 3-8: Analysis of influence between chemical composition and fuel properties ...................................................................................................... 87
Figure 3-9: Biodiesel properties estimation accuracy of developed ANN models .......................................................................................................... 91
Figure 4-1: Beauty leaf tree growing along a beach front, and in a park and its distribution in Australia ............................................................................... 95
Figure 4-2: The tree and kernels of Candle nut ......................................................... 96
Figure 4-3: Blue berry lily plant and seeds. ............................................................... 97
Figure 4-4: Tree and kernels of Queen palm ............................................................. 98
Figure 4-5: Shrub and seeds of Castor ....................................................................... 99
vi
Figure 4-6: Bidwilli plant and seeds ......................................................................... 100
Figure 4-7: Karanja fruit and seeds ......................................................................... 101
Figure 4-8: Tree and fruit of Whitewood .................................................................. 101
Figure 4-9: The tree and fruit of Cordyline .............................................................. 102
Figure 4-10: Flame tree, fruit and seeds .................................................................. 103
Figure 4-11: Chinese rain tree and fruits ................................................................. 104
Figure 4-17: Extracted bio-oil sample from native plants ........................................ 109
Figure 4-18: (a) Graphical Analysis for Interactive Assistance (GAIA) plot for eight biodiesel showing 11 criteria and decision vector. (b) Corresponding complete ranking and Phi value of biodiesel from native plants ............................................................................................... 118
Figure 5-5-2: Mechanical oil extraction through a screw press ............................... 130
Figure 5-5-3: Chemical oil extraction ...................................................................... 131
Figure 5-5-4: ASE oil extraction (a) Dionex™ ASE 350® (b) solvent removal with flow of nitrogen .................................................................................. 132
Figure 5-5-5: Beauty leaf oil yield from three different extraction methods. .......... 133
Figure 5-5-6: Soap formation in oils contains high FFA ......................................... 137
Figure 5-5-10: (a) Graphical Analysis for Interactive Assistance (GAIA) plot for eight biodiesel showing 10 criteria and decision vector. (b) Corresponding ranking of biodiesel on their outranking flow. .................. 151
Figure 6-6-1: (a) Esterification and transesterification reactor; (b) Layer of Methanol-Water (top) and oil (bottom) after acid-catalysed pre-esterification; (c) Layer of Beauty leaf oil methyl ester (top) and glycerol (bottom) after base-catalysed Trans-esterification. ...................... 165
Figure 6-6-2: Scatter diagram of experimental FFA (%) and predicted FFA (%) of a linear model. ....................................................................................... 170
Figure 6-6-3: Response surface of FFA content against .......................................... 171
Figure 6-6-4: Scatter diagram of experimental and calculated FAME (%) of full quadratic model. .................................................................................. 174
vii
Figure 6-6-5: Response surface ester content against catalyst concentration vs. (a) methanol to oil molar ratio at 55 °C; (b) reaction temperature at 7.5:1 methanol to oil molar ratio. ............................................................... 175
Figure 7-7-1: Schematic diagram of used engine exhaust measurement system ..... 185
Figure 7-7-2: Brake specific PM emission at ........................................................... 187
Figure 7-7-3: Brake specific PN emissions at 1500 rpm 100% load (a) and 2000 rpm 100% load (b). ........................................................................... 188
Figure 7-7-4: Variations in particle median size among used fuels at 1500 rpm 100% load (a) and 2000 rpm 100% load (b), while (c) shows particle median size variation with total number concentration. ............................ 190
Figure 7-7-5: Brake specific NOx emission at (a) 1500 rpm 100% load and (b) 2000 rpm 100% load. ................................................................................. 192
Figure 7-7-6: Variation in specific PM and PN emissions with used fuel surface tension, viscosity and oxygen content ........................................... 193
Figure 7-7-7: Comparison of engine performance (power, BSFC) and particle emissions (PM, PN) among biodiesels and their blends where petroleum diesel was used as a reference fuel. .......................................... 195
Figure 8-8-2: Variation of power output for neat diesel and biodiesel blends (a) brake power; (b) indicated power .............................................................. 209
Figure 8-8-3: (a) Brake thermal efficiency (BTE) and (b) Brake specific fuel consumption (BSFC) for neat diesel and biodiesel blends ........................ 210
Figure 8-8-4: Engine cylinder pressure for diesel and Beauty leaf biodiesel blends, (a) full load; (b) 75% load; (c) 50% load; and (d) 25% load ......... 211
Figure 8-8-5: NOX emission for diesel and BOME blend for different engine load conditions ........................................................................................... 213
Figure 8-8-6: Particle emission for diesel and BOME blend in different engine load condition (a) Brake specific particle mass (PM); (b) brake specific particle number ............................................................................. 214
viii
List of Tables
Table 2-1. Advances in biodiesel technology ............................................................ 22
Table 2-3: Reported optimum conditions for transesterification of oils for biodiesel production. .................................................................................... 28
Table 2-4: Chemical structure of common fatty acid in biodiesels ............................ 30
Table 2-5: International biodiesel standards .............................................................. 32
Table 2-6: Performance and emission of diesel engines with biodiesel .................... 43
Table 2-7: ANN used in automobile engine application ............................................ 55
Table 2-8: ANN in predicting fuel properties ............................................................ 56
Table 3-1 Biodiesel property test standard ................................................................. 66
Table 3-2: Biodiesel datasets investigated in this study ............................................. 68
Table 3-3: Structural formulae for fatty acids methyl ester found in biodiesel samples ......................................................................................................... 71
Table 3-4: Chemical composition of tested biodiesel ................................................ 72
Table 3-5: BREF experimental results of biodiesel properties .................................. 73
Table 3-6: Summary of the secondary data for biodiesel properties .......................... 76
Table 3-7: Number of input variables and optimised number of neuron in ANN model ............................................................................................................ 88
Table 4-1: Fatty acid profile and chemical composition of bio-oil produced from native plants ....................................................................................... 112
Table 4-3: Variables and preference used in PROMETHEE-GAIA analysis .......... 117
Table 4-4: Comparative rank shift with different weighting for bio-oil yield ......... 119
Table 4-5: Comparative rank shift with different oxidation stability of biodiesel ... 119
Table 4-6: Comparative Rank shift with different cold filter plugging point temperature ................................................................................................. 120
Table 5-1: Advantages and disadvantages of the three extraction methods ............ 134
Table 5-2: Physical properties of Beauty leaf oil ..................................................... 136
Table 5-3: The fatty acid distributions of Beauty leaf and commercial biodiesels .................................................................................................... 142
Table 5-4: Fuel properties of Beauty leaf oil biodiesel and commercial biodiesels .................................................................................................... 149
Table 5-5: Variables and preference used in PROMETHEE-GAIA analysis .......... 150
Table 5-6: Comparative rank shift with different OS and CFPP weighting ............ 152
ix
Table 6-1: Experimental range and levels of independent variables ....................... 165
Table 6-4: Properties of Beauty leaf oil. .................................................................. 167
Table 6-5: Experimental conditions and results for acid-catalysed pre-esterification ............................................................................................... 169
Table 6-6: Regression coefficients for %FFA prediction ........................................ 170
Table 6-7: Experimental data for base-catalysed trans-esterification. ..................... 172
Table 6-8: Regression coefficients for FAME (%) prediction ................................. 173
Table 7-1: Test engine specification ........................................................................ 181
Table 7-2: Fatty acid profile of used biodiesels ....................................................... 183
Table 7-3: Important physicochemical properties of tested fuels ............................ 185
Table 8-1: Properties of Beauty leaf fatty acid methyl ester (BOME) and petroleum diesel ......................................................................................... 206
Table 8-2: Test engine specification ........................................................................ 208
x
List of Abbreviations
AFR air-fuel ratio ANN artificial neural network ANOVA analysis of variance BMEP brake mean effective pressure BSFC brake-specific fuel consumption BTE brake thermal efficiency C cylinder CB cylinder bore CFPP cold-filter plugging point CI compression ignition CME coconut methyl ester CN cetane number CNG compressed natural gas CO carbon monoxide CO2 carbon dioxide CP cloud point DU degree of unsaturation ECP Engine cylinder pressure CPO crude palm oil CR compression ratio EGR exhaust gas recirculation ES engine stock ET engine temperature FAME fatty acid methyl ester FAEE fatty acid ethyle ester FDR fuels blend ratio FFA free fatty acid FFR fuel flow rate GHG greenhouse gas H2 hydrogen HC hydrocarbon HV heating value HHV higher heating value ICE internal combustion engines IP injection pressure IT injection timing
IV iodine value KV kinematic viscosity L load Lb lubricity LHV lower heating value LPG liquid petroleum gas MLR multiple linear regression MRE mean relative error MSE mean square error N2 nitrogen NA naturally aspirated NOx nitrogen oxides O2 oxygen OS oxidation stability P power PCA principle component analysis PCR principle component regression PLS partial least square regression PM particulate matter PME palm oil methyl ester PP pour point R2 regression coefficient RME rapeseed methyl ester RPM rotation per minute S sulphur SI spark ignition SME soybean methyl ester SOx sulphur oxides T torque TC turbocharged Texh exhaust gas temperature TP throttle position UHC unburned hydrocarbons VT valve timing WCO waste cooking oil
xii
List of Publications
Published journal papers:
1. M. I. Jahirul, K. Wenyong, L Moghaddam, R. J. Brown, I. O'Hara, W. Senadeera, N. Ashwath. Biodiesel production from non-edible Beauty Leaf (Calophyllum inophyllum) oil: process optimization using response surface methodology (RSM), Energies 7(8), 5317-5331, 2014. I.F. 1.884.
2. M. M. Rahman, A. M. Pourkhesalian, M. I. Jahirul, S. Stevanovic, P. X. Pham, H. Wang, A.R. Masri, R. J. Brown and Z. D. Ristovski. Particle emissions from biodiesels with different physicochemical properties, Fuel 134, 201-208, 2014. IF. 3.357
3. M. I. Jahirul, R. J. Brown, W. Senadeera, Z Ristovski, I O'Hara. Artificial neural network approach in identifying sustainable future generation biofuel feedstock, Energies, Special issue: Alternative Fuels for the Internal Combustion Engines (ICE), 6, 3764-3806, 2013. I.F. 1.884.
4. M. I. Jahirul, J. R. Brown, W. Senadeera, N. Ashwath, C. Laing, J. Leski-Taylor, and M. G. Rasul. Optimisation of Bio-Oil Extraction Process from Beauty Leaf (Calophyllum Inophyllum) Oil Seed as a Second-Generation Biodiesel Source, Procedia Engineering, 56, 619-24, 2013.
Submitted Paper: 1. M. I. Jahirul, W. Senadeera, J. R. Brown, , N. Ashwath, M. G. Rasul, M. M.
Rahman, Muhammad Aminul Islam, and I. M. O’Hara. Physico-chemical Assessment of Beauty Leaf (Calophyllum Inophyllum) as Second-Generation Biodiesel Feedstock. Submitted to the journal of Energy Conversion and Management.
Conference papers:
1. M. I. Jahirul, W. Senadeera, R. J. Brown, L. Moghaddam. Estimation of Biodiesel Properties from Its Chemical Composition – An Artificial Neural Network (ANN) Approach. International Conference on Environment and Renewable Energy, Cité Internationale Universitaire de Paris, 17 Boulevard Jourdan, 75014 Paris – France, 7-8 May 2014.
2. M. I. Jahirul, W. Senadeera, P. Brooks, R.J. Browna, R. Situ, P.X. Pham and A.R. Masri. An Artificial Neutral Network (ANN) Model for Predicting Biodiesel Kinematic Viscosity as a Function of Temperature and Chemical Compositions. 20th International Congress on Modelling and Simulation, Adelaide, Australia, 1–6 December 2013, Paper no. 1021
xiii
3. Jahirul M. I., Brown R. J, Senadeera W, Z Ristovski. Influence of the physical properties of fractionated methyl ester on the ultra-fine particle emission of internal combustion engine. 8th Australia and New Zealand Aerosol Workshop, 26-27 November 2012. Canberra, Australia.
xiv
Statement of Original Authorship
The work contained in this thesis has not been previously submitted to meet
requirements for an award at this or any other higher education institution. To the
best of my knowledge and belief, the thesis contains no material previously
published or written by another person except where due reference is made.
Signature: QUT Verified Signature
Date: 17/04/2014
xv
Acknowledgements
First of all, I would like to express my praise to Almighty Allah Subhanawatala for
giving me the opportunity to finish the thesis successfully. I would like to express my
gratitude and profound respect to my supervisors Dr Wijitha Senadeera, A/Prof
Richard Brown, Prof Zoran Ristovski and A/Prof Ian O’Hara. Their supervision,
guidance and comments helped me finish the research work. I am grateful for their
generosity and amicable nature. I found them to be not only great supervisors and
scientists, but also caring and supportive men. Further, I should also express my
particular gratitude to A/Prof Richard Brown for his excellent support,
encouragement, guidance and care during my research journey.
My sincere appreciation goes to Queensland University of Technology for providing
me scholarships and access to various research instruments/facilities. Further, I
should thank the Science and Engineering Faculty and the School of Chemistry,
Physics and Mechanical Engineering at QUT for their continued administrative
support and financial assistance, particularly for conference and workshop
attendance. I would like to express thanks to the Biofuel Engine Research Facility
(BERF), QUT, for providing engine testing facilities. I would also like to extend my
thanks to all researchers and staff of this facility. Special thanks go to Mr Noel
Hartnett, Mr Scott Abbett, Mr. Tony Morris, Mr. Nathaniel Raup, Mr Shane Russel
and other technical staff of QUT, who also contributed to this work. I would also like
to extend my gratitude to Dr. Lalehvash Moghaddam, Centre for Tropical Crops and
Biocommodities (CTCB), QUT, for her significant contribution in this project.
I would like to thank my friends and colleagues at QUT for their continuous support
and encouragement, without which my PhD journey might be more difficult. Special
thanks to Chaminda Prasad Karunasena Helambage, M. Aminul Islam, Meisam
Babaie, Md Mostafizur Rahman, Prof. Nurun Nabi, Dr. Timothy Bodisco, Kabir
Suara, Farhad Hossain and many others, whom I always found beside me during this
project. I wish to commend the efforts of all undergraduate and postgraduate students
involved in this project for their assistance, especially, Wenyong Koh, Jakub L.
xvi
Taylor, Cameron Laing, Eerond Parez, Luke Asgill, Fadil Darmanto, Jagaddhita
Pandya Atmaja, Bodhimula Satyajati and Mohammad Hariz Bin Mokhtar.
I would like to thank A/Prof Nanajapa Ashwath and A/Prof Muhammad Rasul, as
well as all technical staff from the Centre for Plant and Water Sciences (CPWS),
CQU, for their sincere support during my Rockhampton bio-oil extraction visit. I
would like to acknowledge my gratitude to Professor Henning Bockhorn and
Michael Stroebele from the Karlsruhe Institute of Technology (KIT), Germany, for
their immeasurable support and guidance during my visit to Germany for biodiesel
property testing. Further, I am very thankful to A/Prof Bo Feng and George Thomas
from the University of Queensland (UQ), for their excellent support during the
engine testing campaign with Australian native plants. I would also like to express
my thank to Dr. Peter Brooks, University of the Sunshine Coast (USC) for extending
his support to my project and helping to determine the chemical composition of
biodiesel using his lab facilities.
Further, I should be very thankful to my wife Mst. Halima Akhter for her unique
love, passion, encouragement and commitment. She has been so close to me all the
time, along with our little daughter Arisha Islam, who has tried her best to cheer her
father with lovely smiles or cheerful activities. I also would like to thank my
relatives, friends and their families who helped me throughout the journey in many
ways.
Last but not least, I would like to acknowledge with gratitude, the support and love
of my parents, Abdul Mannan Mozumder and Mst. Nojumer Nesa. Although their
life is full of up-and-down situations and financial hardship, they always try to keep
me on the right track and consistently encourage me for moving forward with my
study. I strongly believe that this thesis would not be possible without their sincere
effort and wishes.
Chapter 1: Introduction 1
Chapter 1: Introduction
1.1 BACKGROUND
The current global energy supply is based on petroleum fuels (oil, natural gas, coal)
of which the reserves are finite. Given the growing world population, the increasing
energy consumption per capita and global warming due to greenhouse gas emissions,
the necessity of identifying long-term alternative energy sources is well recognised.
In order to counter greenhouse gas emissions, the European Union ratified the Kyoto
Protocol in 2002 and emphasised the potential for scientific innovation, which
unfortunately has not yet been fully achieved. Atmospheric CO2 concentration has
already exceeded the allowable level 10 years earlier than had previously been
predicted (Stern 2008).
Although the transport sector occupies third place (after industry and the building
sector) when considering total global energy consumption and greenhouse gas
emissions, it is the fastest growing sector. By 2030, the energy consumption and CO2
emissions of this sector are predicted to be 80% above the levels seen today (Miller,
Schmidt and Shindell 2006). Besides, it is also the sector that most heavily depends
on petroleum fuel (through the oil-derived liquid products gasoline and diesel) and
currently consumes 30% of global petroleum oil, which is predicted to increase to
60% by 2030 (Luque et al. 2008). Furthermore, the availability of petroleum oil is
geographically restricted and the era of cheap and secure oil is almost over. These
facts have forced automobile researchers to look for alternative carbon neutral
transport fuels which promise an harmonious amalgamation of sustainable
development, energy conversion, energy efficiency and environmental preservation
(Jahirul et al. 2010). As yet, no such option for fuel has been fully developed for the
transportation sector. Moreover, cars which emit no greenhouse gases (electric, solar,
hydrogen etc.) are a long way from becoming mainstream vehicles. Therefore, the
development of a sustainable, long-term alternative fuel has become essential, with
2 Chapter 1: Introduction
biodiesel receiving much attention and presenting as a promising alternative to
conventional fossil fuel (Luque et al. 2008).
In recent years, biodiesels have received much attention as a sustainable alternative
for petroleum diesel. It is liquid fuel made from various oil seeds crops, as well as
animal fat. The socio-economic advantages of using biodiesel are many, including
the fact that it is renewable, bio-degradable, non-toxic and eco-friendly compared
with petroleum fuels. Biodiesels are now produced on an industrial scale around the
world, using edible oil feedstocks such as soybean oil, palm oil, sunflower oil, corn
Figure 2-5: Fatty acid profile of various biodiesel fuels
(Hoekman et al. 2012; Kapdan and Kargi 2006; Singh and Singh 2010; Taravus,
Temur and Yartasi 2009; Chuck et al. 2009; Kinoshita et al. 2007; Koçak, Ileri and
Utlu 2007)
2.1.6 Biodiesel standards
Quality standards are crucial for the commercial use of any fuel product. They serve
as guidelines for production, assure customers that they are buying high-quality
fuels, and to provide authorities with approved tools for a common approach to
transport, storage and handling. Modern diesel engines using common rail fuel
injection systems are more sensitive to fuel quality. Therefore, engine and
automotive manufacturers rely on fuel standards in determining consumer
warranties. However, the chemical compositions of biodiesel and petroleum diesel
are very different, and these differences result in varying physico-chemical
properties. In order to improve the viability of biodiesel for as a commercial fuel for
direct replacement of petroleum diesel, the properties of biodiesel need to reflect
functional equivalence with diesel.
Biodiesel can be used as a pure fuel (B100) or blended with petroleum diesel in
varying concentrations. For B100, the most internationally recognised standards are
EN14214 (Europe) and ASTM D-6751 (USA). Both standards are similar in content,
32 Chapter 2: Literature Review
with only minor differences in some parameters (Hoekman et al. 2012). Many other
countries have defined their own standards, which are frequently derived from either
EN14214 or ASTM D-6751 (Hoekman et al. 2012). As a part of the Fuel Quality
Standards Act 2000, the Australian government released a biodiesel fuel standard,
“Fuel Standard (Biodiesel) Determination 2003”. A summary of the major fuel
quality parameters in these standards is detailed in Table 2-5.
Table 2-5: International biodiesel standards
(Singh and Singh 2010; Canakci and Sanli 2008)
Properties Units USA ASTM D-
6751 Europe EN
14214 Australia
Viscosity, 40 °C mm2/sec 1.9–6.0 3.5–5.0 3.5–5.0 Density gm/m3 n/a 0.860–0.900 0.860–0.900
Cetane number - 47 min 51 min 51 min Flash point °C 130 min 120 min 120 min Cloud point °C report report report
Acid number mg KOH/g 0.80 max 0.5 max 0.8 max Free glycerine wt.% 0.02 max 0.02 max 0.02 max Total glycerine wt.% 0.24 max 0.25 max 0.25 max Iodine number - - 120 max n/a
Oxidation stability h - 6 min n/a Monoglyceride Mass (%) - 0.8 max n/a
Diglyceride Mass (%) - 0.2max n/a Triglyceride Mass (%) - 0.2 max n/a
CFPP °C - - −4
2.1.7 Fuel properties
Biodiesel fuel properties vary significantly between feedstocks due to their differing
chemical compositions. Figure 2-6 summarises the key fuel properties of various
biodiesels reported in the more recent literature. The factors that influence biodiesel
fuel properties are discussed below.
Chapter 2: Literature Review 33
Figure 2-6: Variation in fuel properties of various biodiesel
(Ramos et al. 2009; Taravus, Temur and Yartasi 2009; Koçak, Ileri and Utlu 2007;
Benjumea, Agudelo and Agudelo 2010; Sanford et al. 2009; Canakci and Sanli 2008;
Canakci 2005b; Barnwal and Sharma 2005; Alptekin and Canakci 2008; Kinast 2003a)
2.1.7.1 Kinematic viscosity
Viscosity is defined as the resistance to shear or flow; it is highly dependent on
temperature and it describes the behaviour of a liquid in motion near a solid
boundary such as the walls of a pipe. The presence of strong or weak interactions at
the molecular level can greatly affect the way the molecules of an oil or fat interact,
therefore affecting their resistance to flow. Viscosity is one of the most critical
features of a fuel. It plays a dominant role in fuel spray, fuel-air mixture formation
and the combustion process. In a diesel engine, the liquid fuel is sprayed into
compressed air and atomised into small droplets near the nozzle exit. In the
combustion chamber, a fuel form a cone-shaped spray at the nozzle exit which
affects the viscosity affects the atomisation quality, penetration and size of the fuel
droplet (Alptekin and Canakci 2008). Higher viscosities result in higher drag in the
fuel line and injection pump, higher engine deposits, higher fuel pump duties and
increased wear in the fuel pump elements and injectors. Moreover, the mean
diameter of the fuel droplets from the injector and their penetration increases with an
34 Chapter 2: Literature Review
increase in fuel viscosity (Choi and Reitz 1999). Higher pressure in the fuel line can
cause early injection, moving the combustion of the fuel closer to top dead centre,
increasing the maximum pressure and temperature in the combustion chamber (Choi
and Reitz 1999; Lee et al. 2002; Tat and Van Gerpen 2003a). Therefore, fuel
viscosity significantly influences engine combustion, performance and emissions,
especially carbon monoxide (CO) and unburnt hydrocarbon (UHC) (Knothe and
Steidley 2005).
To estimate the influence of biodiesel viscosity on diesel engine exhaust emission,
Ng et al. (Ng, Ng and Gan 2012) conducted experiments on a light-duty diesel
References Use of ANN model (Boudy and Seers 2009) Density prediction in different temperatures for palm oil biodiesel (Ramadhas et al. 2006b) Cetane number prediction based on the fatty acid profile of biodiesel (Agarwal, Singh and Chaurasia 2010)
Density, kinetic viscosity, water and methanol content prediction for various biodiesels
(Kumar, Bansal and Jha 2007)
Flash point, fire point, density and viscosity prediction based on diesel-biodiesel blend ratio
(Liu et al. 2007) Density, flash point, freezing point, aniline point and net heat of combustion prediction for various jet fuels based on their chemical composition
(Korres et al. 2002) Lubricity prediction from physical properties of diesel (Marinović et al. 2012) Prediction of diesel cold temperature properties based on density,
kinetic viscosity, conductivity, sulphur content and 90% distillation point
(Pasadakis, Gaganis and Foteinopoulos 2006)
Octane number prediction from chemical composition of gasoline
(Pasadakis, Sourligas and Foteinopoulos 2006)
Cold temperature properties and distillation curve prediction from the chemical composition of diesel
(Wu et al. 2006) Prediction of cold filter plugging point of diesel from physical properties
(Yang et al. 2002) Cetane number and density prediction using chemical composition of diesel
(Cheenkachorn 2004) Viscosity, cetane number and heat of combustion prediction from fatty acid composition of various biodiesel feedstocks
Satyanarayana and Muraleedharan (Satyanarayana and Muraleedharan 2010)
developed the ANN model to analyse the relation of esterification methods with fuel
properties for biodiesel produced from rubber seed. They used transesterification
reaction parameters such as the methanol-oil ratio, catalyst concentration, reaction
time, and reaction temperature in the input layer and acid value of biodiesel in output
layer while constricting the ANN model. Although a good prediction was obtained,
this study did not consider the initial acid value of the vegetable oil, which may have
an impact on the final acid value of the biodiesel. This issue has been addressed by
Rajendra et al. (Rajendra, Jena and Raheman 2009) while using ANN techniques to
predict the acid value of sunflower oil biodiesel, including its initial acid value as
well as transesterification reaction parameters. Liu et al. (Liu et al. 2007) compared
the reputability of standard fuel property testing methods with ANN while
developing a model to predict density, flash point, freezing point, aniline point and
net heat of combustion of 80 different jet fuels. This study found that the
Chapter 2: Literature Review 57
repeatability of neural network models for measuring density and flash point were
lower than the ASTM test methods. However, for the freezing point, aniline point
and net heat of combustion, the repeatability of the ANN methods are equal to the
ASTM methods. Therefore it can be said that not only the prediction accuracy but
also the ANN approaches are comparable to the repeatability values of the standard
ASTM methods, which are used for the experimental determination of the properties.
Similar conclusions have been made by Pasadakis et al. (Pasadakis, Gaganis and
Foteinopoulos 2006; Pasadakis, Sourligas and Foteinopoulos 2006) while predicting
pour point (CP), cloud point (CP) of diesel and octane number of gasoline based on
the chemical composition of the respective fuels. Several other studies which were
also successful in utilising ANN to estimate the properties of various fuels, which are
summarised in Table 2-8. The success of those studies proved that ANN has the
ability to accurately estimate the fuel properties instead of having costly and time
consuming experimental measurements.
2.4 ANN MODELING OF SECOND-GENERATION BIODIESEL
Although numerous feedstocks including oilseed crops and algae species have been
identified as being suitable for producing second-generation biodiesel, these types of
biodiesel have not yet been established, due to the unavailability of feedstock supply,
high production costs and a lack of knowledge about the fuel’s quality. Moreover, by
producing fuels from new feedstock, optimising production procesess, ensuring fuel
quality through measuring a number of physical and chemical properties, and
evaluating the end-use performance in automobile engines is costly, time-consuming
and requires a wide variety of specialised equipment and skilled workers. These
concerns have restricted the progress of second-generation biodiesel technology,
making it still unacceptable to both automobile engine manufacturers and customers,
which is yet to begin industrial-scale production. In order to address this issue, the
ANN modelling technique could be a very useful tool in predicting fuel quality and
engine combustion-related parameters when considering the chemical composition of
new biodiesel feedstocks. It would require laboratory scale biodiesel production and
basic chemical testing equipment, which will significantly reduce the research cost,
58 Chapter 2: Literature Review
and hence accelerate the investigation of future generation biodiesel. However,
researchers should move to develop a universal ANN prediction model for second-
generation biodiesel, and this will enable the instigation of a wide range of biodiesel
feedstock and automobile engine systems. While training the network, they need to
consider all possible parameters in feedstock that affect the production process, the
quality and the combustion performance of biodiesel in automobile engine
applications. A two-stage artificial neural network (ANN) prediction model can be
proposed for this purpose. At the 1st stage of the model, chemical composition of
biodiesel in terms of fatty acid profile can be used as input parameters and fuel
properties can be used as output or target variable at. In the 2nd stage of the model,
fuel properties alone with engine specification and operating condition can be used
as input layer, whereas, engine performance, emission and combustion parameters
can be used as the target vector of output layer. The structure of such a two-stage
ANN model as proposed is shown in Figure 12. It can be expected that such an
approach will generate new knowledge, based upon which, second-generation
biodiesel will be more sustainable, commercially available and a key contributor to
the mainstream global energy system.
Figure 2-12: Proposed structure of ANN model
Chapter 2: Literature Review 59
2.5 CONCLUSIONS
Biodiesel, produced from renewable feedstocks represents a more sustainable source
of energy and will therefore play a significant role in providing the energy
requirements for transportation in the near future. However, first-generation
biodiesels used around the World today are unlikely to be sustainable in the long
term as a result of being produced from edible oil feedstock. Second-generation
biodiesels produced from non-edible feedstocks have the potential to overcome this
challenge, and to serve as a more sustainable energy source in the near future.
Chemically, all biodiesels are fatty acid methyl esters (FAME), produced from raw
vegetable oil. Numerous fatty acids, ranging in chain length from 6 to 24, have been
found in various biodiesels, which are identical to their respective feedstock.
However, clear differences in chemical structure are apparent from one feedstock to
the next in terms of chain length, degree of unsaturation, number of double bonds
and double bond configuration-which all determine the important fuel properties of
biodiesel. This includes kinetic viscosity, density, cetane number, calorific value,
flash point, oxidation stability, cold temperature properties and iodine value.
Therefore, different levels of combustion performance and emission levels have been
observed in the literature when using different types of biodiesel as diesel engine
fuel. While considering production optimisation and engine durability issues, similar
trends have been observed. The literature reviewed in this study has assured that the
suitability of any biodiesel as automobile engine fuel can be explained largely
through the chemical composition of its respective feedstock.
ANN is a powerful computational modelling tool which has the ability to identify
complex relationships from input-output data. It can result in a higher level of
accuracy in its prediction ability when compared with other statistical methods.
Therefore, ANN has emerged and has found extensive acceptance in many
60 Chapter 2: Literature Review
disciplines for modelling complex real world situations. However, most of the
literatures compared the prediction accuracy of ANNs and statistical methods based
on MSE or RMS which may not much appropriate. It is recommended to consider
some other error measurement technique including residual plots, the maximum error
percentage, minimum error percentage etc.
Recent literature shows that the complex relationship between biodiesel chemical
composition, fuel properties and diesel engine combustion performance can be
established at different operating condition conation by using ANNs. Several ANN
models have been developed to estimate the combustion-related performance of
various fuels in automobile engine applications with a high prediction accuracy, as
shown in Table 6. However, applicability of these models is limited to a specific
engine and to fuel types that have been used to collect the experimental data upon
which the network has been trained. These models also have serious limitations,
considering the limited number of engine operating parameters used in the
experiments. The automobile engine is a complex system, with a large number of
parameters directly influencing its combustion performance. Moreover, no study has
considered the physical and chemical properties of fuel while developing the ANN
model for predicting combustion performance, in spite of there being a strong
correlation between these parameters. Therefore, it would be worthwhile for
researchers to develop a universal ANN model which will be able to predict the
combustion performance of versatile automobile engines and fuel types. To ensure
the most robust ANN model, data should be used which cover as much a range as
possible. This model will able to access the sustainability of the wide ranges of
biodiesel feedstock collecting from different origin.
Chapter 3: Artificial neural network (ANN) model development 61
Chapter 3: Artificial neural network (ANN) model development
Correlation between chemical composition and properties
of biodiesel – a principal component analysis (PCA) and
artificial neural network (ANN) approach
Abstract
Biodiesel, produced from renewable feedstocks, represents a more sustainable source
of energy and will, therefore, play a significant role in providing the energy
requirements for transportation in the near future. Chemically, all biodiesels are fatty
acid methyl esters (FAME), produced from raw vegetable oil and animal fat.
However, clear differences in chemical structure are apparent from one feedstock to
the next, in terms of chain length, degree of unsaturation, number of double bonds
and double bond configuration – all of which determine the fuel properties of
biodiesel. In the present study, the sensitivity of biodiesel fuel properties was
compared against its chemical composition using experimental data. The effective
fuel properties include kinematic viscosity, density, higher heating value, oxidation
stability, cold filter plugging point temperature, flash point temperature and iodine
value. Principal component analysis (PCA) was used to understand the relationship
between important properties of biodiesel and its chemical composition. Finally,
several artificial intelligence-based models were developed to predict specific
biodiesel properties based on its chemical composition. As the relationship between
biodiesel properties and its chemical composition is complex, and there is a lack of
available knowledge to develop traditional mathematical models, a data driven
modelling technique, namely an artificial neural network (ANN), was used in this
study. The experimental study was conducted in order to generate training data for
the ANN. Available (experimental) data from the literature was also employed for
this modelling strategy. The analytical part of this study found a complex multi-
dimensional correlation between chemical composition and biodiesel properties.
62 Chapter 3: Artificial neural network (ANN) model development
Average numbers of double bonds in the chemical structure (representing the
unsaturated component in biodiesel) and the poly-unsaturated component in biodiesel
had a great impact on biodiesel properties. The simulation study demonstrated that
ANN was able to predict the relationship between biodiesel chemical composition
and fuel properties. Therefore, the ANN model developed in this study could be a
useful tool in estimating biodiesel fuel properties, instead of undertaking costly and
time consuming experimental tests.
3.1 INTRODUCTION
Vegetable oil methyl or ethyl esters, commonly referred to as biodiesel, are a
renewable liquid fuel alternative to petroleum diesel. In technical terms, biodiesel is
diesel engine fuel comprised of mono-alkyl esters of long chain fatty acids derived
from vegetable oil or animal fats (Demirbas 2008b). These mono-alkyl esters are the
main chemical species that give biodiesel similar or better fuel properties compared
with petroleum diesel (Fernando et al. 2007). It is also safer to handle, store and
transport than petroleum diesel because it is biodegradable and non-toxic. It has a
higher flash point than diesel. One of the major advantages of biodiesel is that it has
the potential to reduce dependency on imported petroleum through the use of
domestic feedstocks for fuel production (Demirbas 2008b; Fernando et al. 2007).
Biodiesels are usually made from vegetable oils and animal fat feedstocks, through a
chemical reaction called trans-esterification. In this process, the pure oil and fat is
converted from natural oil (three long chain carbon molecules struck together by
glycerine) into three mono-alkyl esters (three separated long chain carbon
molecules). Triglycerides are allowed to react with alcohol (normally methanol)
under acidic or basic catalyst conditions, producing fatty acid esters of the respective
alcohol and free glycerol. After the complete reaction, glycerol is removed as a by-
product and esters remain, which are known as biodiesel (Jahirul 2013).
Chapter 3: Artificial neural network (ANN) model development 63
Quality standards are crucial for the commercial use of any fuel product, which serve
as guidelines for the production process, to assure customers are buying fuels at the
appropriate quality, and provide authorities with approved tools for the assessment of
safety risks and environmental pollution. The fuel quality, which eventually affects
fuel combustion performance, exhaust emissions and engine durability, is more
sensitive in modern diesel engines, as the use of high pressure (about 75,000 bar) in
common rail fuel injection systems has increased (Haseeb et al. 2011a). Cetane
number (CN), widely used as a diesel fuel quality parameter, is a measure of the
combustion quality of diesel fuels during compression ignition. It is related to the
ignition delay (ID) time (i.e. the time that passes between injection of the fuel into
the cylinder and onset of ignition). The higher the CN the lower the ignition delay.
An adequate CN is required for good engine performance. A high CN helps ensure
good cold start properties and minimises the formation of white smoke. On the other
hand, lower CNs may result in diesel knocking and increased exhaust emissions
(Meher, Vidya Sagar and Naik 2006a). Kinematic viscosity (KV) is one of the engine
fuel properties that play a dominant role in the fuel spray, fuel-air mixture formation
and combustion process in diesel engine applications. It effects engine combustion,
performance and emission, especially carbon monoxide (CO) and unburnt
hydrocarbon (UHC) (Knothe and Steidley 2007). In a light-duty diesel engine, the
CO and UHC could increase by 0.02% (by volume) and 1 ppm (by volume),
respectively, by increasing 1 cSt. fuel viscosity (Ng, Ng and Gan 2012). Moreover,
high viscosity is more of a problem in cold weather, as viscosity increases with
decreasing temperature (Joshi and Pegg 2007b). On the other hand, low fuel
viscosity is not desirable, because fuel with low viscosity does not provide sufficient
lubrication for the precision fit of the fuel injection pumps, resulting in leakage or
increased wear. Therefore, all biodiesel standards defined the upper and lower limit
of fuel viscosity. Heating value is another fuel property indicating the energy content
in the fuel. Depending on the amount of oxygen contained in the biodiesel, it is
generally accepted that biodiesel from all sources has about 10% less energy content
compared with petroleum diesel. Similarly, other important fuel properties like
density, oxidation stability (OS), cold filter plugging point temperature (CFPP), flash
point temperature (FP) and iodine value (IV) also effect the combustion performance
of diesel engines and have been discussed by Jahirul et al. (2013). However, those
properties are largely determined by the complex chemical structure of biodiesel. For
64 Chapter 3: Artificial neural network (ANN) model development
instance, the KV of biodiesel increases with increasing carbon chain length in FAME
(Knothe and Steidley 2007). As the lengths of the acid and alcohol segments in the
ester molecules increase, so does the degree of random intermolecular interactions
and consequently, the kinematic viscosity. As reported in the literature (Refaat
2009a), shorter fatty acid chains with longer alcohol content in biodiesel display
lower viscosity than esters with longer fatty acid chains and shorter alcohol. Other
factors that influence biodiesel properties include number and position of the double
bonds, degree of saturation, molecular weight, branching hydroxyl groups and the
level of impurities, such as free fatty acid and unreacted glycerides or glycerol etc.
However, understanding the relationship between chemical composition and the
properties of biodiesel is not a trivial task, since the chemical structure of biodiesel is
so complex. The available mathematical models are still limited in their ability to
describe biodiesel fluid properties, in terms of their corresponding chemical
structure.
In recent years, ANN modelling techniques have increased in popularity due to their
excellent capability to learn and model complex non-linear relationships. The most
important feature of artificial neural networks is their ability to solve problems
through learning by example, without having the process knowledge. More precisely,
ANNs work like a ‘black box’ model, and they can map any relationship based on
system input and output data without knowing the detailed or complete information
about the problem. Therefore, ANNs have been successfully applied in various
disciplines, including neuroscience (Alkım, Gürbüz and Kılıç 2012), mathematical
and computational analysis (Costa, Braga and De Menezes 2012), learning systems
(Carrillo et al. 2012) and engineering design and application (Samura and Hayashi
2012; Gao et al. 2012; Minnett et al. 2011a). The application of ANN has also been
used to predict the fuel properties of biodiesel. Ramadas et. al. (Ramadhas et al.
2006a) used ANN to predict the CN of biodiesel based on fatty acid profile. ANNs
have also been used to predict viscosity, flash point and fire point, based on diesel-
biodiesel blend ratio (Kumar et al. 2007). However, those prediction models were
limited to a specific biodiesel and/or experimental conditions. No investigation has
been reported in the literature to develop an ANN model to predict biodiesel
properties for a wide range of feedstocks. In addition, the available literature have
Chapter 3: Artificial neural network (ANN) model development 65
not considered the impurities generally contained in biodiesel. Therefore, this study
aimed to develop a robust ANN model to estimate the important fuel properties of
biodiesel from its chemical composition. During the model development process, this
study also aimed to make an in-depth investigation of chemical composition and
important fuel properties of biodiesel, and to analyse the relationship between them.
3.2 DATA COLLECTION
Two types of data were used in this study: BERF and literature data. BERF data was
obtained from the experimental study of nine biodiesel samples, using the biofuel
engine research facility (BERF) testing facility. Among the samples, biodiesel
soybean oil (SOME) and waste cooking oil methyl ester (WCO) are commercially
available. The other biodiesel samples, named C810, C1214, C1618 and C1822,
were produced by the fractionating of palm oil biodiesel produced by Proctor &
Gambel. The chemical composition, fatty acid profile and glyceride content of nine
biodiesel samples were analysed using gas chromatography-mass spectrometry (GC-
MS). Biodiesel samples were diluted 1:100 with n-hexane and 1uL samples were
injected into a PerkinElmer Clarus 580 GC-MS fitted with an Elite - 5MS, 30m x
0.25mm x 0.25um column. The split ratio was 30:1, with a column flow of 1mL/min
He. The temperature program was as follows: 120 °C initial, holding 0.5min,
ramping 10 °C/min until 310 °C, and holding for 2 min. Masses were analysed over
the range 40-350m/z. The total amount of carbon (C), oxygen (O) and hydrogen (H)
content in biodiesel was obtained through elemental analysis. In addition, acid
number and six fuel properties of biodiesel, including cetane number (CN) kinematic
viscosity (KV), density, higher heating value (HHV), were obtained through
experimental study, following recognised international standards, as shown in Table
3-1.
66 Chapter 3: Artificial neural network (ANN) model development
Table 3-1 Biodiesel property test standard
Fuel properties Unit Test Method
Element analysis (C, O, H) wt.% DIN EN 15104 Cetane value (CN) - DIN 51773 Kinematic Viscosity (KV) cSt ASTM D445
Density Kg/l ASTM D4052
Higher heating value (HHV) Mj/kg ASTM D4868
Acid number (AN) - ASTM D664
Literature data were collected from papers published in recognised international
journals, conferences and reports of renowned research centres around the world.
Scientific and electronic databases, including Elsevier, Taylor and Francis,
DieselNet, Scopus, Springer, Wiley International, American Chemical Society,
IEEE, SAGA Publication, MDPI etc., were searched for relevant papers for this
study. More than 120 papers were collected, most of which were published in the last
decade, containing experimental results of the chemical composition of biodiesel
along with corresponding fuel properties. During secondary data collection, special
care was taken to ensure the quality of the data and eliminate duplication. Data was
only taken from the literature when the experiments were conducted by the authors
themselves, following recognised international standards. Some extreme data was
excluded from database, due to the unexpected nature of the results. Data was also
eliminated from the database if it was too dissimilar compared to the fuel properties
recorded in the primary data collection results. Furthermore, the experimental results
for density and kinematic viscosity of biodiesel are highly dependent on temperature
(Joshi and Pegg 2007b; Yuan, Hansen and Zhang 2009). Although 15 ºC and 40 °C
temperatures are recommended for density and kinematic viscosity respectively,
some researchers did not mention the test temperature. Therefore, those data were
excluded from the database. Since the properties of a particular biodiesel can be
varied depending on the type of alcohol (methyl, ethyl etc.) used in the production
process, this study only considered methyl esters for inclusion in the database. The
list of papers, including feedstock use and country, are tabulated in Table 3-2. A
large number of feedstocks were investigated worldwide for biodiesel production;
including edible and non-edible vegetable oils, waste cooking oils, beef tallow,
chicken fats, fish oils, algae etc. It is also interesting to note that many investigations
Chapter 3: Artificial neural network (ANN) model development 67
used pure methyl esters in order to represent actual biodiesel, which are mostly
produced by artificial chemical processes. The most popular edible feedstock for
biodiesel investigated worldwide was soybean, followed by palm, sunflower, canola
and rapeseed oil. Among non-edible oils, the most-investigated feedstock was
Jatropha, as shown in Table 3-2.
Chapter 3: Artificial neural network (ANN) model development 68
Table 3-2: Biodiesel datasets investigated in this study
Feedstock Authors’ affiliation References
Algae USA (Do et al. 2011) Almond Iran, Nigeria (Atapour and Kariminia 2011; Giwa and Ogunbona 2014) Apricot Turkey (Gumus and Kasifoglu 2010) Babassu USA, India, Brazil (Sanford et al. 2009; Rodrigues Jr et al. 2006; Barnwal and Sharma 2005; Nogueira Jr et al. 2010) Brassica Austria (Dorado et al. 2004) Camelina USA, China, Ireland (Sanford et al. 2009; Chung 2010; Wu and Leung 2011; Fröhlich and Rice 2005; Moser and
Vaughn 2010; Soriano Jr and Narani 2012) Canola USA, Turkey, China,
Canada (Sanford et al. 2009; Albuquerque et al. 2009; Koçak, Ileri and Utlu 2007; Davis et al. 2009; Hu et al. 2005; Haagenson et al. 2010; Chhetri and Watts 2012a; Moser 2008; Chhetri and Watts 2012b; Kinast 2003b; Do et al. 2011; Duncan et al. 2010; Cecrle et al. 2012)
Coconut Philippine, USA, India, Thailand, Brazil
(Sanford et al. 2009; Alleman and McCormick 2006; Rodrigues Jr et al. 2006; Tan, Culaba and Purvis 2004; Kumar et al. 2010; Nakpong and Wootthikanokkhan 2010; Duncan et al. 2010; Cecrle et al. 2012; Feitosa et al. 2010)
Coffee Greece, Japan (Deligiannis et al. 2011; Todaka et al. 2013) Corn Brazil, USA, Romania (Lin, Huang and Huang 2009; Rodrigues Jr et al. 2006; Dantas et al. 2011; Serdari et al. 1998;
Cursaru, Neagu and Bogatu 2013) Cottonseeds Brazil, Greek, USA (Albuquerque et al. 2009; Royon et al. 2007; Rashid, Anwar and Knothe 2009; Demirbaş 2002;
Tang, Salley and Simon Ng 2008; Nogueira Jr et al. 2010) Fish oil Taiwan, Chile, Turkey (Lin and Li 2009a; Reyes and Sepulveda 2006; Behçet 2011) Golden cress Egypt (Ali 2013) Grape Spain, Romania (Ramos et al. 2009; Cursaru, Neagu and Bogatu 2013) Hazelnut Turkey (Koçak, Ileri and Utlu 2007; Moser 2012; Demirbaş 2002) Hepar USA (Sanford et al. 2009) Jathropa USA, India, Canada,
South Africa, China, Japan
(Sanford et al. 2009; Sarin et al. 2007; Choudhury and Bose 2008; Jain and Sharma 2012; Chhetri et al. 2008; Singh and Padhi 2009; Aransiola et al. 2012; WANG et al. 2012; Chhetri and Watts 2012a, 2012b; Kumar Tiwari, Kumar and Raheman 2007; Todaka et al. 2013)
Chapter 3: Artificial neural network (ANN) model development 69
Lard Portugal, Korea (South), Spain, USA
(Lee, Foglia and Chang 2002; Dias, Alvim-Ferraz and Almeida 2009; Berrios et al. 2009; Kinast 2003b; Wyatt et al. 2005)
Linseed Brazil, Lithuania, India, Egypt
(Rodrigues Jr et al. 2006; Guzatto, De Martini and Samios 2011; Samios et al. 2009a; Lebedevas et al. 2006; Puhan et al. 2009; El Diwani and El Rafie 2008; Radha and Manikandan 2011)
Mahua India, Turkey (Ghadge and Raheman 2005; Godiganur, Suryanarayana Murthy and Reddy 2009; Kapilan and Reddy 2008; Demirbas 2009a)
Mustard Turkey, Brazil, USA (Bannikov 2011; Jham et al. 2009) Neem India, South Africa,
Pakistan (Ragit et al. 2011; Aransiola et al. 2012; Sivalakshmi and Balusamy 2012; Sardar et al. 2011; Radha and Manikandan 2011)
Olive Spain, Greece, USA, Romania
(Ramos et al. 2009; Dorado et al. 2003; Kalligeros et al. 2003; Cecrle et al. 2012; Cursaru, Neagu and Bogatu 2013)
Palm Malaysia, Indonesia, Greece, Colombia, Japan, Spain, India, USA, Canada, Romania
(Karavalakis, Stournas and Bakeas 2009; Sarin et al. 2007; Kousoulidou et al. 2010; Ramos et al. 2009; Benjumea, Agudelo and Agudelo 2008; Kalam and Masjuki 2002a; Ng and Gan 2010; Loh, Chew and Choo 2006; Crabbe et al. 2001; Kalam and Masjuki 2002b; Pérez et al. 2010; Barnwal and Sharma 2005; Moser 2008; Do et al. 2011; Vedaraman et al. 2011; Cecrle et al. 2012; Park et al. 2008; Cursaru, Neagu and Bogatu 2013)
Peanut USA, Spain, Turkey, India, China, Romania
(Lin, Huang and Huang 2009; Ramos et al. 2009; Moser 2012; Pérez et al. 2010; Davis et al. 2009; Kaya et al. 2009; Barnwal and Sharma 2005; SUN et al. 2008; Cursaru, Neagu and Bogatu 2013)
Popyseed Turkey (Demirbaş 2002) Rapeseed USA, Greece,
Lithuania, Turkey, Spain
(Karavalakis, Stournas and Bakeas 2009; Wu 2008; Lin, Huang and Huang 2009; Senatore et al. 2000; Rashid and Anwar 2008a; Sahoo et al. 2007; Ramos et al. 2009; Fröhlich and Rice 2005; Lebedevas et al. 2006; Demirbaş 2002; Pérez et al. 2010; Cecrle et al. 2012; Park et al. 2008; Todaka et al. 2013)
Rice barn India (Sinha, Agarwal and Garg 2008) Rubberseed India (Ramadhas, Jayaraj and Muraleedharan 2005; Ikwuagwu, Ononogbu and Njoku 2000) Safflower USA (Rashid and Anwar 2008a; Demirbaş 2002) Sesame Nigeria, Pakistan (Betiku and Adepoju 2013; Ahmad, Ullah, et al. 2011) Soybean USA, Brazil, Spain, (Ali, Hanna and Cuppett 1995b; Wu 2008; Armas, Yehliu and Boehman 2010; Albuquerque et al.
70 Chapter 3: Artificial neural network (ANN) model development
India, Turkey, China 2009; Sarin et al. 2007; Lin, Huang and Huang 2009; Ali, Hanna and Cuppett 1995a; Rodrigues Jr et al. 2006; Ramos et al. 2009; Guzatto, De Martini and Samios 2011; Canakci and Van Gerpen 2003; Alcantara et al. 2000; Schwab, Bagby and Freedman 1987; Pérez et al. 2010; Davis et al. 2009; Barnwal and Sharma 2005; Moser 2008; Kinast 2003b; Duncan et al. 2010; Candeia et al. 2009; Cecrle et al. 2012; Pereira et al. 2007; Tang, Salley and Simon Ng 2008; Wyatt et al. 2005; Canakci 2005a; Feitosa et al. 2010; Qi et al. 2009; Moraes et al. 2008; Nogueira Jr et al. 2010; Park et al. 2008; Shah et al. 2013)
(Sarin et al. 2007; Lin, Huang and Huang 2009; Royon et al. 2007; Serdari et al. 1998; Ramos et al. 2009; El Diwani and El Rafie 2008; Demirbaş 2002; Pérez et al. 2010; Barnwal and Sharma 2005; Kalligeros et al. 2003; Antolın et al. 2002; Moser 2008; Rashid et al. 2008; Cursaru, Neagu and Bogatu 2013)
Tall Turkey (Altıparmak et al. 2007) Tallow USA, Lithuania, Spain,
India, Turkey, Brazil, Japan
(Ali, Hanna and Cuppett 1995b; Sanford et al. 2009; Ali, Hanna and Cuppett 1995a; Lebedevas et al. 2006; Alcantara et al. 2000; Barnwal and Sharma 2005; Öner and Altun 2009; Ramalho et al. 2012; Kinast 2003b; Tang, Salley and Simon Ng 2008; Wyatt et al. 2005; Moraes et al. 2008)
Terebinth Turkey (Özcanlı, Keskin and Aydın 2011) Terminalia Brazil (Dos Santos et al. 2008) Turnip USA (Shah et al. 2013) Walnut USA (Moser 2012) Waste cooking Vietnam, Spain,
Taiwan, Turkey, Brazil, Spain, USA
(Lin, Huang and Huang 2009; Phan and Phan 2008; Encinar, Gonzalez and Rodríguez-Reinares 2005; Lin and Li 2009a; Koçak, Ileri and Utlu 2007; Guzatto, De Martini and Samios 2011; Lapuerta et al. 2008; Alcantara et al. 2000; Demirbas 2009b; Chhetri, Watts and Islam 2008; Cecrle et al. 2012)
Yellow grease USA (Canakci and Van Gerpen 2003; Kinast 2003b) Pure methyl ester
USA (Rodrigues Jr et al. 2006; Knothe 2005; Knothe and Steidley 2005; Knothe 2008; Moser 2011)
Chapter 3: Artificial neural network (ANN) model development 71
Table 3-3: Structural formulae for fatty acids methyl ester found in biodiesel samples
A systematic analysis of chemical composition and comparative fuel properties is
very important for selecting appropriate feedstock for biodiesel production. As
discussed in Chapter 3, chemical composition is a key factor in determining the
quality of biodiesel and the chemical composition of the extracted oil samples was
determined in terms of fatty acid profile and the percentage free fatty acid content
BAC D E F G H I J K
110 Chapter 4: Biodiesel from Australian native plants
(FFA). The fatty acid profiles were analysed by gas chromatography and flame
ionisation detection (GC-FID), in accordance with EN 14103 standards. The gas
chromatograph (GC) was a Hewlett-Packard 6890 System fitted with Varian Select TM 30 m × 0.32 mm × 0.25 µm column. The chemical composition of the tested bio-
oils are presented in Table 4-1, where it can be seen that the chemical compositions
of bio-oil obtained from native oil seeds were similar to those for conventional edible
oil and with the exception of Queen palm and Castor, they were mostly rich in
triglycerides of Oleic (C18:1), followed by Stearic (C18:0), Linoleic (C18:2),
Palmitic (C16:0) and Linolenic (C18:3) fatty acids. Queen palm bio-oil was mostly
comprised of shorts chain fatty acids, including 42.05 wt.% Luatic acid (C12:0) and
10.45 wt.% Myristic acid (C14:0), as well as small amounts of Caprilic (C8:0) and
Capric (C10:0) acids, which were not found in any other tested bio-oil. These fatty
acids not only consisted of a short carbon chain but they were also saturated fatty
acids, which explains why Queen palm showed the lowest average chain length
(ACL) account and highest percentages of saturated fatty acids among the tested bio-
oil samples. Likewise, Bidwilli bio-oil was rich in saturated fatty acids, containing
42.39% C16:0 and 14.37% C18:0 fatty acids. On the other hand, the highest ACL
and lowest saturated fatty acid content was found for Castor bio-oil, comprising 95
wt.% long chain length fatty acids and mono-unsaturated Gondonic (C20:1) acid,
with small amounts of C18:2 and C18:1 fatty acids. The only other oil to contain a
significant amount of Gondonic (C20:1) acid was Whitewood, which accounted for
25.04% by weight. Due to its high Linolenic (C18:3) fatty acid content, Candle nut
oil showed the highest level of poly-unsaturated fatty acids (PUFA), followed by
Cordilyne, Chinese rain and Blue berry lily oils. In contrast, very small amounts of
PUFA’s were found with Whitewood, Castor and Queen palm bio-oil. As reported in
Chapter 3, the higher the unsaturation of biodiesel, the greater the tendency for the
biodiesel to oxidise. On the other hand, unsaturated fatty acids had a positive
influence on other fuel properties, such as cold filter plugging point temperature.
Therefore, both saturated and unsaturated FAMEs have a role to play in finding the
optimal balance for high quality biodiesel.
FFA’s have a significant effect on biodiesel processing from vegetable oil, as
discussed in Chapter 2. In this study, FFA content of the native plant oil was
analysed using a D5555-95 (2011) standard test method and the results are shows in
Table 4-1. Although the literature (Dorado et al. 2002; Lam, Lee and Mohamed
2010; Kumar Tiwari, Kumar and Raheman 2007; Ramadhas, Jayaraj and
Muraleedharan 2005) suggested that the FFA content of bio-oil should be below 5%
for alkali-trans-esterification, most of the bio-oil tested in this study contained much
Chapter 4: Biodiesel from Australian native plants 111
higher FFA’s. These results indicate that FFA can be one of the issues impeding the
success of biodiesel production from native species. In particular, Flame tree oil
contained 36.7% FFA’s, which was the highest among the native bio-oils, followed
by Beauty leaf (22%), Queen palm (15%) and Blue berry lily (13.1%). The lowest
FFA content was found in Chinese rain oil, which consisted of 1.8%. In addition,
Queen palm contained an exceptionally higher amount of oxygen, which accounted
for 14.19% on a per weight basis. The oxygen content of the other methyl esters
range d from 10.25 to 11.82%, as shown in Table 4-1.
Chapter 4: Biodiesel from Australian native plants 112
Table 4-1: Fatty acid profile and chemical composition of bio-oil produced from native plants
Chapter 4: Biodiesel from Australian native plants 116
4.7 EVALUATION OF NATIVE PLANT METHYLE ESTER
In order to constitute an ideal source of sustainable biodiesel, the feedstock should
contain a sufficient amount of bio-oil, with a suitable chemical composition, in order
to elicit good fuel quality properties. However, selection of the most suitable
feedstock for industrial production is a multi-criteria problem, as it involves multiple
quality indicators. In this study, a multi-criteria decision method (MCDM) software
PORMETHEE-GAIA was used for the selection of biodiesel for large scale
production. The most suitable native plant species were selected from the eleven
native species investigated in this study, based on the parameters listed in Table 4-3.
The GIGA plan displays how the alternatives perform in terms of the different
criteria, as shown in Figure 4-18. The length of the criteria vectors and their
directions indicate the influence these criteria have on the decision vector (red line in
Figure 4-18) and preference of the species. The preference functions of criteria were
modelled as Min (i.e. lower values are preferred for good biodiesel) or Max (higher
values are preferred for good biodiesel), as per Table 4-3. When the criteria are
oriented in an opposite direction they are in conflict and when they are oriented in a
similar direction they express the same preference (Espinasse, Picolet and Chouraqui
1997). For example, maximum values for oil yield (OY), IV and OS were preferable
for good biodiesel and therefore, those criteria lines are in same direction of decision
vector. The preference functions were obtained by principle component analysis
(PCA) techniques (Brans 2002), however preference function selection also
influenced the orientation of criteria (which was also suggested in (Islam et al.
2013)). For example, IV and OS were inversely related but still showing in the same
direction within ±45○. This is because OS was preferred to maximum, but iodine
number was preferred to minimum, as shown in Table 4-4. Therefore, criteria which
are in the same preference (min/max) and lie close to ±45° are correlated. The
decision vector, which is marked as red line in Figure 4-18, is the direction in which
the decision maker is invited to decide. The direction and length of criteria are
indicative of their influence on the decision vector (Islam et al. 2013), such that the
very short length of some criteria (i.e. difficulty level (DL) and HHV) indicate the
little effect they had on the decision vector. However, the freedom of decision vector
Chapter 4: Biodiesel from Australian native plants 117
is modelled by the preference weight of individual criteria and therefore, if the
weights are modified, the decision maker is invited to decide in another direction
(when the position of the criteria and alternative remain unchanged) (Brans 2002).
The Phi value is the net flow score, which could be negative or positive depending
upon the angular distance from the decision vector and the distance from the centre.
Figure 4-18 shows the raking results of eleven native species biodiesels with its
corresponding phi value for the equally weighted criteria and preferred function
listed in Table 4-3. Results showed that Beauty leaf was most aligned with the
decision vector and in the farthest relative position from the centre, giving it the
highest of ranking followed, by Queen plam, Castor and Karenja. On the other hand,
biodiesel from the Flame tree was at the bottom of the ranking.
Table 4-3: Variables and preference used in PROMETHEE-GAIA analysis
No Variables Preference for PROMETHEE-GAIA
1 Seeds production per year per hector Max 2 Difficulty level of seed processing Min 3 Oil yield Max 4 Free fatty acid content in oil Min 5 Kinematic viscosity Min 6 Density Min 7 Higher heating value Max 8 Oxidation stability Max 9 Iodine value Min 10 Cetane number Max 11 Flash point temperature Max 12 Cold filter plug point Max
118 Chapter 4: Biodiesel from Australian native plants
Figure 4-18: (a) Graphical Analysis for Interactive Assistance (GAIA) plot for eight
biodiesel showing 11 criteria and decision vector. (b) Corresponding complete
ranking and Phi value of biodiesel from native plants
The quality ranking analyses of biodiesel shown in the previous section was
conducted with an equal weighting of all parameters. However, for industrial and
economical biodiesel production, the oil content of the feedstock may be a more
important criterion than others. Moreover, the importance of some fuel properties
depends on the country and place where it will be used and stored. For an example,
in tropical/sub-tropical regions, CFPP was not considered to be of importance,
however elevated temperatures in these regions are likely to affect the OS of
biodiesel. On the other hand, in colder climate conditions, CFPP are more important
than OS. In this study, ranking sensitivity analysis was conducted for the criteria OY,
CFPP and OS, by increasing the weighting from 1 (equal to other parameters) to 10,
and the results are shown in the Table 4-4 to 4-6. As shown in Table 4-4, Beauty leaf
was always ranked number 1 with the increasing OY weighting, whereas the rank of
Candle nut and Flame tree gradually improved from 7 to 2 and 11 to 6, respectively.
Rank Biodiesel Phi
1 Beauty leaf 0.1917
2 Queen palm 0.1500
3 Castor 0.1333
4 Karanja 0.0917
5 Whitewood 0.0583
6 Chinese rain
0.0083
7 Candle nut -0.0083
8 Cordyline -0.0500
9 Bidwilli -0.1500
10 Blue berry lily
-0.2000
11 Flame tree -0.2250
(a) (b)
Chapter 4: Biodiesel from Australian native plants 119
With a weighting of 10 for OY, Queen palm biodiesel dropped down from a ranking
of 2 to 3, however it increased to first position when the weighting of OS was
increased to 2 and it remained there as the weighting increased (see Table 4-5). The
largest improvement in ranking was observed for Bidwilli biodiesel, which increased
in rank from 9 to 4 when the weighting of OS was increased to 6. Whitewood also
showed a significant improvement in rank (from 5 to 2) for a heavier weighting of
OS. The rank of Flame tree also improved from 11 to 8 following the same increase
in weighting.
Table 4-4: Comparative rank shift with different weighting for bio-oil yield
Flash point °C 120, min 93, min 120, min 145.64 143.06 143.65 160.87 162.00 176 170 177
CFPP °C Report Report Report 2.45 4.11 3.92 -5.76 -2.94 10 -10 -3 *Experimental; **Literature (Ramos et al. 2009; Singh and Singh 2010; Hoekman et al. 2012)
Chapter 5: Pilot scale Beauty leaf (Calophyllum Inophyllum) biodiesel production 150
5.10 VALIDATION OF BEAUTY LEAF BIODIESEL
To be an ideal source of biodiesel, Beauty leaf biodiesel should have suitable
chemical composition to ensure compliance with standard biodiesel properties. The
fuel properties of Beauty leaf biodiesel from three different extraction methods were
analysed and compared with five other commercially available biodiesels. To
determine the suitability of Beauty leaf biodiesels compared to other biodiesels based
on 14 criteria (fuel properties): CN, IV,OX, AN, HHV, KV, Density, FP, CFPP,
Linolenic acid, ACL, MUFA and PUFA, a multi-criteria decision method (MCDM)
software was used. In this study, the Preference Ranking Organisation Method for
Enrichment Evaluation (PROMETHEE) and Graphical Analysis for Interactive
Assistance (GAIA) were used because of their rational decision vector which
stretches towards the preferred solution compared to other MCDM (Brans and
Mareschal 1994).
Table 5-5: Variables and preference used in PROMETHEE-GAIA analysis
Variables Preference For PROMETHEE‐GAIA
Kinematic viscosity (KV) Min Density Min Higher heating value (HHV) Max Acid number (AN) Min Oxidation stability (OX) Max Iodine value (IV) Min Cetane Number (CN) Max Linolenic acid (LA) Min Flash point (FP) Max Cold filter plug point (CFPP) Min
In GAIA plane, the criteria which lie close to (±45°) are correlated, while those lying
in opposite directions (135–225°) are anti-correlated, and those in a roughly
orthogonal direction have no or less influence (Espinasse, Picolet and Chouraqui
1997). The preference function criteria (fuel property) were modelled as minimum
(i.e. lower values are preferred for good biodiesel) or maximum (higher values are
preferred for good biodiesel). The selection of preference function also influences the
Chapter 5: Pilot scale Beauty leaf (Calophyllum Inophyllum) biodiesel production 151
direction of criteria. For example, IV and CN were inversely related but still showed
the same direction within ±45○. This is because the Cetane number were preferred to
maximum, but iodine number was preferred to minimum, as shown in Table 5-5,
which was suggested by Islam et al. (2013). Therefore, criteria which are in the same
preference (min/max) and lie close to ±45° are correlated. The direction and length
of criteria are indicative to their influence on decision vector (marked as red line in
Figure 5-10) (Islam et al. 2013), such that the very short length of some criteria, in
particular ‘Density’ and ‘HHV’, indicate the little effect on the decision vector.
The decision vector indicates the most preferable samples (i.e. those that align with
the direction of this vector) and the outermost criteria in the direction of the decision
vector are the most preferable (Figueira, Greco and Ehrgott 2005). In this study,
equally weighted criteria showed (Figure 5-10a) that POME was most aligned with
the decision vector and its farthest position from the centre gave it the highest
ranking.
Rank Biodiesel Phi
1 POME 0.16
2 ROME 0.05
3 BLOME_OP 0.02
4 BLOME_nHX 0.01
5 BLOME_ASE -0.01
6 COME -0.06
7 SFOME -0.08
8 SOME -0.10
(a) (b)
Figure 5-5-10: (a) Graphical Analysis for Interactive Assistance (GAIA) plot for
eight biodiesel showing 10 criteria and decision vector. (b) Corresponding ranking of
biodiesel on their outranking flow.
152 Chapter 5: Pilot scale Beauty leaf (Calophyllum Inophyllum) biodiesel production
Figure 5-10(b) shows the overall ranking of the different biodiesel and the three
biodiesel from Beauty leaf, BLOME_OP, BLOME_nHX and BLOME_ASE, were
placed third, fourth and fifth, respectively, in the middle of the overall rankings. The
Phi value is the net flow score which could be negative or positive depending upon
the angular distance from the decision vector and the distance from the canter.
Biodiesel from soybean oil was at the bottom of the ranking compared with other
biodiesel. In can be seen from Figure 10 that the quality of Beauty leaf biodiesel in
terms of fuel properties did not depends on oil extraction methods. The results of this
analysis indicate the ability of Beauty leaf biodiesel to compete with commercially
available first- generation biodiesels.
Table 5-6: Comparative rank shift with different OS and CFPP weighting
OS CFPP Weighting 1‐3 4‐6 6‐10 1‐2 3‐4 5‐6 7‐10
POME 1 1 ‐ 1 ‐ 1 5 7 8
ROME 2 5 6 2 1 1 ‐ 1 ‐ BLOME_OP 3 2 2 3 6 6 ‐ 6 ‐
BLOME_nHX 4 3 3 4 7 8 7
BLOME_ASE 5 4 4 5 8 5 5 ‐
COME 6 6 ‐ 5 6 3 3 ‐ 3 ‐
SFOME 7 7 ‐ 7 ‐ 7 4 4 ‐ 4 ‐
SOME 8 8 ‐ 8 ‐ 8 2 2 ‐ 2 ‐
Black arrows upward: rank increase; Black arrows downward reduce rank; Hyphen: no ranking change
The quality ranking analyses of biodiesel shown in the previous section was
conducted with an equal weighting of all parameters. However, the importance of
some fuel properties depends on the country and place where it will be used and
stored. In tropical/sub-tropical regions, CFPP was not considered to be of importance
here. Elevated temperatures of these regions are, however, likely to affect oxidative
stability of the biodiesel. On the other hand, in the winter climate condition CFPP are
more important than oxidation stability. Therefore, ranking sensitivity analysis was
conducted for the fuel properties CFPP and OS by increasing the weighting from 1
(equal to other parameters) to 10, and the results are shown in the Table 5-6. A
significant change in ranking was found for both OS and CFPP. POME always
Chapter 5: Pilot scale Beauty leaf (Calophyllum Inophyllum) biodiesel production 153
ranked 1 with the increasing of OS weighting. At the same time, the rank of Beauty
leaf biodiesels improved and was ranked just after POME. In contrast, the rank of
ROME dropped dramatically with the weighting increase of OS. On the other hand,
an opposite trend was observed when the weighting was increased for CFPP. Both
POME and Beauty leaf biodiesels dropped in rank and were placed at the bottom in
the ranking table. Therefore, as for palm oil biodiesel, Beauty leaf oil biodiesels are
unlikely to be suitable for cold climate conditions, especially in winter. These results
indicate that Beauty leaf biodiesels are a better choice for tropical/sub-tropical
regions than colder climate conditions.
5.11 CONCLUSION
Second-generation biodiesel is gaining more interest in the market as a sustainable
alternative of diesel fuel. However, to produce biodiesel from new sources and
continue to develop these in the market, various aspects must be examined. In this
study, the potential of Beauty leaf plant was evaluated as a source of second-
generation biodiesel. Oil was extracted from dry seed kernels using 3 different oil
extraction methods and oil properties have been analysed. Oil has been esterified to
produce biodiesel using a two-step esterification technique and the physico-chemical
properties were assessed. From the results obtained in this study the following
conclusion can be made.
By flowering two seasons in a year, Beauty leaf plant is able to produce a large
amount of seeds that contain non-edible oil. Due to the variability in size of the
seeds, having relatively soft and high moisture containing (about 32% by weight) oil
bearing kernels, special care need to be taken during the seed cracking process. This
will prevent damage to the kernels and reduce oil loss. The conventional seed
cracking methods using mallets and a stomper were able to produce good quality
kernels but those processes were found to be slow, labour-intensive and might be not
suitable for large scale production, processing approximately 2–3 kg of seeds per
operator per hour. Therefore, an automated seeds cracking device needs to be
designed for industrial scale production using Beauty leaf oil seeds.
154 Chapter 5: Pilot scale Beauty leaf (Calophyllum Inophyllum) biodiesel production
This study also found that all oil extraction methods had several advantages and
disadvantages in terms of oil production from Beauty leaf oil seeds, which are
summarised in Table 1. The performance of Beauty leaf oil extraction using an oil
press resulted in a low oil yield. This drawback was overcome using chemical oil
extraction using n-hexane as oil solvent. Furthermore, the oil yield further increased
by 3-4% with high pressure and temperature extraction. The highest oil yield was
found on average 51.5% of dry kernels in ASE extraction method, which suggested
that Beauty leaf plant is able to produce about 1.56 tons of oil per year per hectare.
When comparing quality with edible vegetable oils, conventionally used as biodiesel
feedstock, in terms of acid value, density, kinematic viscosity, surface tension and
higher heating value, Beauty leaf oil showed much higher acid values resulting from
high free fatty acid contents. Chemical oil extraction under atmospheric conditions
produced oil containing relatively low levels of free fatty acids. However, those
results have illustrated that raw Beauty leaf oil may not suitable for direct use in
diesel engines. Another drawback of Beauty leaf oil is that conventional base-
catalysed transesterification cannot be used directly for biodiesel production.
Therefore, a two-step esterification process, involving acid-catalysed pre-
esterification and base-catalysed trans-esterification, was used in this study. during
the first stage of this process, the acid value was significantly reduced to the
acceptable limit for base-catalysed trans-esterification. The highest biodiesel
conversion efficiency was found to be 93.05% for the oil produced by chemical oil
extraction in atmospheric condition, whereas oil obtained from screw press and ASE
methods showed 75.74% and 83.76%, respectively, under similar reaction
conditions, which is due to variations in the acid value of the respective oils.
Table 6-8: Regression coefficients for FAME (%) prediction
Predictor Linear Full quadratic
Coefficient p-value Coefficient p-value
Constant 40.9817 0.008 −129.28 0.141
MeOH:Oil (M') 0.3825 0.727 2.256 0.809
NaOCH3 (C') 47.6688 0.001 339 0.021
Temp (T') 0.00742 0.959 1.817 0.239
MeOH:Oil × NaOCH3 (M'C') 9.392 0.133
MeOH:Oil × Temp (M'T') 0.0014 0.985
NaOCH3 × Temp (C'T') −1.2203 0.141
MeOH:Oil × MeOH:Oil (M'2) −0.7892 0.0207
NaOCH3 × NaOCH3 (C'2) −171.57 0.025
Temp × Temp (T'2) −0.007011 0.502
The regression coefficient (R2) and the adjusted regression coefficient (R2 (adj)) of
linear model were 0.6465 and 0.55 demonstrated that the linear model may not be
suitable for estimate FAME in given reaction condition. Whereas full quadratic
model with 0.9224 of regression coefficient (R2) and 0.8413 of adjusted regression
174 Chapter 6: Production process optimisation of biodiesel
coefficient (R2 (adj)) shows better model for FAME estimation. Figure 6-4 shows the
accuracy of the prediction model in a scattered plot between experimental and
predicted ester contents. All points are close to straight line demonstrate a good
agreement between experimental results and those ones calculated by the model.
Figure 6-6-4: Scatter diagram of experimental and calculated FAME (%) of full
quadratic model.
More detailed analysis of the effect of base-catalysed transesterification reaction
parameters on Beauty leaf FAME ester content are shown in Figures 6-5a and 6-5b.
These figures predict that an optimal methanol to oil molar ratio would be 7.5:1,
however, further increase in methanol would not have a positive effect on ester
content. On the other hand, NaOCH3 concentration has a strong effect on ester
content of the FAME with corresponding increment with agreement in terms of
linear and quadratic effects. Temperature had a less significant effect than methanol
to oil molar ratio and catalyst concentration. Figure 6-5 shows that the optimum
temperature of transesterification was 65 °C. These figures illustrated that although
all parameters are not statistically significant at 95% confident level but the
relationship still contains useful information for some biodiesel production purposes.
Chapter 6: Production process optimisation of biodiesel 175
(a)
(b)
Figure 6-6-5: Response surface ester content against catalyst concentration vs. (a)
methanol to oil molar ratio at 55 °C; (b) reaction temperature at 7.5:1 methanol to oil
molar ratio.
176 Chapter 6: Production process optimisation of biodiesel
6.4 CONCLUSIONS
A response surface method based a Box-Behnken design was employed to determine
a feasible experimental plan to optimise the Beauty leaf oil to biodiesel conversion
procedure. Due to the high FFA content of Beauty leaf oil (12 wt%), a two-step
process was employed utilising sulphuric acid catalysed pre-esterification followed
by sodium methoxide catalysed trans-esterification. Effects of reaction parameters
such as methanol to oil molar ratio, catalyst loading and reaction temperature were
statistically investigated on the reduction of FFA content in pre-esterification and
ester content in trans-esterification. The optimal conditions for pre-esterification
were 30:1 methanol to oil molar ratio, 10 wt% sulphuric acid catalyst and 75 °C
reaction temperature which reduced the FFA content to 1.8 wt%. With the aid of
statistical modelling, the predicted optimal conditions for transesterification
methanol to oil molar ratio, catalyst concentration and reaction temperature were
7.5:1, 1% and 55 °C respectively. Based on these conditions, the highest achievable
ester content of FAME predicted by the model was found to be approximately 93%.
However a higher result may be achievable by future lowering FFA content of
Beauty leaf oil. In terms of a linear effect on FFA reduction for the first step,
methanol to oil molar ratio was found to be highly significant and reaction
temperature moderately significant. For trans-esterification, catalyst concentration
was found be the most dominant variable in achieving high ester contents. The
limitation of the developed response surface model is that all the p-values are greater
than 0.05. Therefore, the developed models might be over-specified and some that
terms can be omitted. However, the information contained in the model and
experiment in this study is very significant in industrial biodiesel production.
Chapter 7: Diesel engine testing with biodiesel of controlled chemical composition 177
Chapter 7: Diesel engine testing with biodiesel of controlled chemical composition
Particle emissions from biodiesels with different
physicochemical properties
M. M. Rahman, A. M. Pourkhesalian , M. I. Jahirul , S. Stevanovic , P. X. Pham,
H. Wang , A.R. Masri , R. J. Brown and Z. D. Ristovski
Publication: Journal of Fuel, Vol. 134, pp. 201-208, 2014
http://dx.doi.org/10.1016/j.fuel.2014.05.053 0016-2361/2014 Elsevier Ltd. All rights reserved
Author Contribution
Contributor Statement of Contribution
M. M. Rahman Conducted the experiments, performed the data analysis and drafted the manuscript
A. M. Pourkhesalian Assisted with conducting the experiment and data analysis
M. I. Jahirul Conducted the experiments, performed the data analysis and drafted the manuscript Signature
S. Stevanovic Assisted with conducting the experiment
P. X. Pham Assisted with conducting the experiment
H. Wang Assisted with conducting the experiment
A.R. Masri Supervised the project and revised the manuscript
R. J. Brown Supervised the project and revised the manuscript
Z. D. Ristovski Supervised the project and revised the manuscript
Principal Supervisor Confirmation
I have sighted email or other correspondence from all co-authors confirming
their certifying authorship.
Name
Dr Wijitha Senadeera
Signature
Date
178 Chapter 7: Diesel engine testing with biodiesel of controlled chemical composition
Abstract
Biodiesels produced from different feedstocks usually have wide variations in their
fatty acid methyl ester (FAME) so that their physical properties and chemical
composition are also different. The aim of this study is to investigate the effect of the
physico-chemical properties of biodiesels on engine exhaust particle emissions.
Alongside with neat diesel, four biodiesels with variations in carbon chain and
degree of unsaturation have been used at three blending ratio (B100, B50, B20) in a
common rail engine. It is found that particle emission increased with the increase of
carbon chain length and degree of unsaturation in FAME. However, for similar
carbon chain length, particle emissions from totally unsaturated biodiesel is found to
be slightly less than that of partially (about 50%) unsaturated biodiesel. Particle size
is also found to be dependent on fuel type. The fuel or fuel mix responsible for
higher PM and PN emissions is also found responsible for lager particle median size.
Particle emissions reduced consistently with fuel oxygen content regardless of the
proportion of biodiesel in the blends, whereas it increased with fuel viscosity and
surface tension only for higher diesel-biodiesel blend percentages (B100, B50).
However, since fuel oxygen content increases with the decreasing carbon chain
length, it is not clear which of these factors drives the lower particle emission.
Rather, overall, it is evident from the results presented here that chemical
composition of biodiesel is more important than its physical properties in controlling
exhaust particle emissions.
Keywords: Biodiesel, particle emissions, fuel physical properties, fuel chemical
composition
Highlights
Four biodiesels were used to investigate their influence on particle emissions.
Particle emission increased with the increase of biodiesel carbon chain length.
Particle emissions reduced consistently with fuel oxygen content.
Particle median size found dependent on the type of fuel used.
Biodiesel chemical composition found more important than physical properties.
Chapter 7: Diesel engine testing with biodiesel of controlled chemical composition 179
7.1 INTRODUCTION:
Compression Ignition (CI) engines are increasing in popularity due to their higher
thermal efficiency. They power a wide range of land and sea transport as well as
provide electrical power, used in farming, construction and industrial applications.
Tail pipe emissions of diesel engines, especially particulate matter (PM) are still a
matter of concern due to its harmful effects both on human health and the
environment(Brito et al. 2010; Jacobson 2001). Exposure to diesel particulate matter
(DPM) can cause pulmonary diseases such as asthma, bronchitis and lung
cancer(Brito et al. 2010) and because of these adverse effects, the International
Agency for Research on Cancer (IARC) included DPM as carcinogenic to human
health.
The harmful effects caused by DPM are related to both the physical properties and
chemical composition of the particles. The physical properties that influence
respiratory health include particle mass, surface area, mixing status of particles,
number and size distribution (Ristovski et al. 2012). The particles deposit in different
parts of the lung depending on their size. The smaller the particles the higher the
deposition efficiency (Broday and Rosenzweig 2011) and the greater the chance of
them penetrating deep into the lung. The smaller particles stay suspended in the
atmosphere for longer thus have a higher probability of being inhaled and
consequently deposited deep in the alveolar region of the lung. Particle number
governs the ability of particles to grow larger in size by coagulation while particle
surface area determines the ability of the particles to carry toxic substances. Recent
studies reveal that DPM surface area and organic compounds play a significant role
in initiating various cellular and chemical processes responsible for respiratory
disease (Giechaskiel, Alfoldy and Drossinos 2009; Ristovski et al. 2012). In addition
to this, a large fraction of DPM is black carbon, which is considered the second most
potential greenhouse warming agent after carbon dioxide (Jacobson 2001). After
treatment devices (ATD) like diesel particulate filters (DPF) and diesel oxidation
catalysts (DOC) aid in reducing DPM (Herner et al. 2011). Alternative fuels are
another potential emission reducing source (Bakeas, Karavalakis and Stournas 2011).
Of these fuels, biodiesel is considered one of the more promising for diesel engines
180 Chapter 7: Diesel engine testing with biodiesel of controlled chemical composition
as it (Varuvel et al. 2012; Xue, Grift and Hansen 2011) produces less PM and other
gaseous emissions (Lapuerta, Armas and Rodríguez-Fernández 2008b; Xue, Grift
and Hansen 2011; Surawski, Miljevic, Ayoko, Roberts, et al. 2011). Biodiesel in
diesel engines has the potential to greatly reduce carbon emissions and is a renewable
source of energy.
Biodiesel is a mixture of fatty acid esters with physicochemical properties that
mostly depend on the structure of this molecule. Biodiesel can be produced from a
variety of feedstock sources such as vegetable oil, animal fat, municipal and
industrial waste and some from insects (Salvi and Panwar 2012; Sharma, Singh and
Upadhyay 2008; Morshed et al. 2011; Alptekin, Canakci and Sanli 2012). An
extensive range of fatty acid profiles exist among these feedstocks (Moser 2014),
with some being within the same feedstock; which can be controlled. Physical
properties and chemical composition of biodiesel varies among different feedstocks,
which can have a noticeable influence on engine performance and emissions
(Hoekman et al. 2012; Mccormick, Graboski, Alleman and Harrin 2001).
McCormick et al. (McCormick, Graboski, Alleman, Herring, et al. 2001) reported
constant PM emissions from different biodiesel feedstocks when the density was less
than 0.89 g/cm3 or cetane number was greater than about 45, but increase of NOx
emissions with the increase of biodiesel density and iodine number. In contradiction
to these findings, a difference in particle emissions from biodiesel from different
feedstocks has also been reported (Surawski, Miljevic, et al. 2011a; Allan, Williams
and Rogerson 2008). Lapuerta et al.(2008b) reported a 10% increase of NOx and
20% decrease of particle emissions by unsaturated biodiesel. Benjumea et al.(2011)
found that the degree of unsaturation in biodiesel doesn’t significantly affect the
engine performance but increases smoke opacity and THC emissions. Kravalkis et al.
(2011) reported noticeable influence of biodiesel origin on particle emissions,
especially particles associated with PAH and carbonyl emissions. Very recently
Salamanaca et al.(2012) reported increased PM and HC emissions from biodiesel
that contains more unsaturated compounds that favour soot precursor formation.
There is no distinction however, that exists in the literature, which indicates whether
chemical composition of biodiesel, physical properties or a combination of these is
responsible for this variation in engine performance and emissions. This study
Chapter 7: Diesel engine testing with biodiesel of controlled chemical composition 181
therefore, aims to investigate the effect of biodiesel physical properties and chemical
composition on engine exhaust particle emissions. It is an extension of the previous
study (Pham et al. 2013) where results from the same experiments were presented for
the engine performance characteristics and emission of pollutants including some
preliminary results for the particle emission, particularly for pure biodiesel. It should
be noted that the results for B100 are reproduced here for comparison purposes.
Furthermore, the paper elaborates on these findings and presents new analysis in
terms of the physico-chemical properties of the fuels and their blends.
Table 7-1: Test engine specification
Model Cummins ISBe220 31
Cylinders 6 in-line
Capacity (L) 5.9
Bore x Stroke (mm) 102 x 120
Maximum power (kW/rpm) 162/2500
Maximum torque (Nm/rpm) 820/1500
Compression ratio 17.3
Aspiration Turbocharged & after cooled
Fuel injection Common rail
After treatment systems None
Emissions certification Euro III
7.2 MATERIALS AND METHODS
7.2.1 Engine and fuel specification
This experimental study was performed in a heavy duty 6 litres, six cylinders,
turbocharged after cooled, common rail diesel engine typically used in medium size
trucks. Test engine is the same as used in Pham at el. (2013). Table 7-1 shows
specification of the test engine. Engine was coupled to a water brake dynamometer,
and both of them are connected to an electronic control unit (ECU). Engine was
operated at 1500 rpm (maximum torque speed) and at 2000 rpm (intermediate
speed), and four different loads 25%, 50%, 75%, & 100% for each engine speed.
182 Chapter 7: Diesel engine testing with biodiesel of controlled chemical composition
Maximum load at any particular engine speed depends upon the type of fuel used,
therefore for each fuel at first maximum load was measured when engine was in full
throttle for a particular speed. This measured load is then considered as 100% load
for that speed and other loads were determined based upon measured 100% load.
An ultra-low sulphur diesel (sulphur content < 6ppm) and four biodiesels with
different physicochemical properties were used to run the engine. All four biodiesels
were used at three blending ratio i.e. 100% biodiesel (B100), blends of 50% diesel
and 50% biodiesel (B50), and blends of 80% diesel and 20% biodiesel (B20). 7-2
shows the fatty acid profile of used biodiesels as found using gas chromatography
mass spectrometry (GCMS) analysis. Biodiesel samples were analysed using Perkin
Elmer clarus 580GC-MS equipped with Elite 5MS 30m x 0.25mm x 0.25um column
with a flow rate of 1mL/min. Before analysing, each biodiesel was diluted with n-
hexane (1:100 v/v). Initial temperature was 120 0C for 0.5 minutes, then raised to 310 0C for 2 minutes at 10 0C/min and kept at 310 0C for 2 minutes. The mass selective
detector was optimised using calibrating standards with reference masses at m/z (35-
40). Among four biodiesels, C810 is fully saturated and composed of 52% and 46%
caprylic acid and capric acid ester respectively. C1214 is also dominated by saturated
compounds but has comparatively longer carbon chain length fatty acid ester i.e.
48% lauric, 19% % myristic, 10% palmitic and 18% oleic acid ester. On the other
hand both C1618 and C1822 are dominated by long chain unsaturated fatty acid
esters. C1618 is composed of 21% palmitic, 9% stearic, 58% cis-oleic and 10%
linoleic acid ester where C1822 has 10% more oleic and linoleic acid ester. C1822
also has small amount (4%) of trans- oleic acid ester.
Some important properties of all used fuels related to combustion and emissions are
shown in Table 7-3. Among the used biodiesels physical properties varied with the
variation in chemical composition i.e. carbon chain length and degree of
saturation/unsaturation. Viscosity, heating value, iodine value and oxygen content
increased with the increase of carbon chain length and degree of unsaturation, where
saponification value decreased. Density of all four biodiesels was found higher than
diesel, and no trend observed among biodiesels either with the carbon chain length or
degree of unsaturation. Surface tension also increased with the carbon chain length in
Chapter 7: Diesel engine testing with biodiesel of controlled chemical composition 183
biodiesels, although no significant change was observed with the degree of
unsaturation. For example, there is almost no difference in surface tension between
C1618 & C1822, although C1822 contains much higher percentage of unsaturated
compounds compared to C1618. Surface tension and cetane value of diesel were
found to be lower than all four used biodiesels where calorific value was higher.
Viscosity of diesel was higher than C810 but lower than the rest of the three
The Dekati ejector diluter was used to partly sample raw exhaust from the engine
exhaust pipe and then dilute it with particle free compressed air. A second Dekati
diluter was connected in series with the first one to further increase the dilution ratio
in order to further decrease concentration. A HEPA filter was used to provide
particle free compressed air for the diluters. The purpose of the dilution was to bring
down the temperature as well as the concentration of gases and PM within the
measuring range of the instruments. Diluted exhaust was then sent to different
gaseous and particle measuring instruments. A CAI 600 series CO2 analyser was
184 Chapter 7: Diesel engine testing with biodiesel of controlled chemical composition
used to measure the CO2 concentration directly from the raw exhaust. A second CO2
meter (SABLE, CA-10) connected via a three way valve between the two diluters
was used to record the CO2 concentration from the diluted exhaust. Background
corrected CO2 was used as tracer gas to calculate the dilution ratio for each stage.
After first stage dilution, CAI 600 series CLD NOx analyser was used to measure the
NOx. PM2.5 emissions were measured by a TSI DustTrak (Model 8530). DustTrak
readings were converted into a gravimetric measurement by using the tapered
element oscillating microbalance to DustTrak correlation for diesel particles
published by Jamriska et al.(2004). It is worth noting this conversion can introduce
significant uncertainties if the optical properties of particles significantly changes.
The particle number size distribution for C810, C1214, C1618 and their blends was
measured by a scanning mobility particle sizer (SMPS). This SMPS consisted of a
TSI 3080 electrostatic classifier (EC) and a TSI 3025 butanol based condensation
particle counter (CPC). Due to technical problems a new SMPS had to be used for
the reference diesel and C1822. This SMPS system consisted of a 3085 classifier
with a nano-DMA (differential mobility analyser). As the measurement range of the
two SMPS’s used was different, we have used a fitting procedure (see Section 7.3.2)
to recalculate the total PN and make the measurements comparable. A TSI 3089 nm
aerosol sampler (NAS) was used in conjunction with a Tandem Differential Mobility
Analyser (TDMA) to collect preselected particles on Transmission Electron
Microscopic (TEM) grids for morphological analysis. The EC in the TDMA
preselected the size of the particles, which deposited on the TEM grid in the NAS.
An Aethalometer (Magee Scientific) was also connected after second stage dilution
for black carbon (BC) measurement. Results from TEM analysis and BC data will be
published in a separate paper.
Chapter 7: Diesel engine testing with biodiesel of controlled chemical composition 185
Table 7-3: Important physicochemical properties of tested fuels
Relevant properties Fuels
C810 C1214 C1618 C1822 Diesel
Average formula C9.5H19.7O2 C9.5H19.7O2 C9.5H19.7O2 C9.5H19.7O2 CxHya
Average unsaturation (AU) 0 0.22 0.7892 1.11 ‐ Oxygen content (wt%) 18.72 13.25 10.74 10.83 0 a
Stoichiometric air fuel ratio 11.12 12.05 12.50 12.48 14.5 Relative density(kg/l) 0.877 0.871 0.873 0.879 0.8482 Viscosity (mm2/sec) 1.95 4.37 4.95 5.29 3.148 Surface tension (mN/m) 26.184 28.41 29.9 29.966 26 Cetane value 62.96 65.57 61.06 53.65 48.5 Iodine number 1 max 8 65 105 Saponication value 330 233 195 185 Acid value 0.9 0.4 0.8 0.4 <0.05 Boiling point (0C) 190 >150 165.6 >150 >190 a
Gross Calorific value(MJ/kg)
35.335 38.409 37.585 39.825 44.365
Sulphur content(mg/kg) 0 0 0 0 2.5 a Values with superscripts have been taken from literature(Surawski, Miljevic, et al.
2011c) and (Lapuerta, Armas and Rodriguez-Fernandez 2008).
Figure 7-7-1: Schematic diagram of used engine exhaust measurement system
186 Chapter 7: Diesel engine testing with biodiesel of controlled chemical composition
7.3 RESULTS AND DISCUSSION
7.3.1 Specific PM emissions
All four biodiesels that were used, disregarding the variations in physical properties
and chemical composition, reduced PM emissions in comparison to petroleum diesel.
Figure 7-2a and b shows brake specific PM emissions at engine operating speeds of
1500 rpm and 2000 rpm, respectively. It was found that as the biodiesel percentage
in the diesel-biodiesel blends increased, PM emissions decreased consistently. The
maximum reduction in PM was observed for 100% biodiesel blends, an observation
common in the literature (Lapuerta, Armas and Rodríguez-Fernández 2008a;
Surawski, Miljevic, et al. 2011b; Xue, Grift and Hansen 2011). Noticeable variations
in PM emissions were also observed among the four biodiesels and their blends. In
the case of using 100% biodiesel, a massive 98% reduction in PM was observed for
biodiesel C810, where C1214, C1618 and C1822 reduced PM 83%, 70% and 76%
respectively. Similar trends in PM emissions were also found for B50 and B20
blends although there was a difference of PM reduction proportion in these. For the
B50 blend, PM reduction among biodiesels C810, C1214, C1618 and C1822 was
88%, 75%, 70% and 76% respectively. B20 was slightly lower, measuring 66%,
57%, 42% and 48% respectively. PM emissions from other tested engine loads are
shown in the appendix (Figure A7-1). Similar trends in PM emissions were also
observed for these loads at 2000 rpm engine speed, although at 1500 rpm, PM
emissions from B20 (C1618) were found to be slightly higher than for the diesel.
These variations in PM emissions among biodiesels could be due to either their
chemical composition or their physical properties. Among the biodiesels, PM
emissions increased consistently with biodiesel carbon chain length with the
exception of C1822. This blends carbon chain length was similar to C1618 but its
degree of unsaturation was higher and its PM emissions were less. Pinzi et al.(2013)
also reported reduction of PM emission with the increase of degree of unsaturation
but same carbon chain length in FAME. Opposing observations were also reported in
the literature, which suggests that unsaturated compounds have a tendency to act as
soot precursor (Salamanca et al. 2012; Benjumea, Agudelo and Agudelo 2011). In
addition, important physical properties of C1822 in regards to particle emissions i.e.
viscosity and surface tension were also higher. This slight reduction in PM emissions
Chapter 7: Diesel engine testing with biodiesel of controlled chemical composition 187
from C1822 might be attributed to its high iodine or low cetane value. Fuels with low
cetane value undergo prolonged premixed combustion phases that are responsible for
less soot formation. In addition, NOx emissions from C1822 were highest among the
fuels that were favourable for soot oxidation. This could also be responsible for
comparatively low PM emissions from C1822.
Figure 7-7-2: Brake specific PM emission at
(a) 1500 rpm 100% load and (b) 2000 rpm 100% load. The PM emissions shown are
calculated based on DustTrak measurement.
7.3.2 Specific PN emissions
Variations that were observed in PN emissions were similar to the fuels used. There
were however, slight differences in proportion compared to PM emissions. All PN
emissions were calculated for the size range from 10.2-514 nm. As the measurements
for neat diesel and C1822 were done using the nano-DMA, in the size range from
4.6nm-156nm, a fitting procedure was used to recalculate the PN concentration to the
same size range as used in the other measurements (Heintzenberg 1994). As shown
in Figure 7-3, PN emissions from B100 were found to be lower than diesel for all
biodiesels. Among the biodiesels used, C810 reduced PN most and C1618 reduced
188 Chapter 7: Diesel engine testing with biodiesel of controlled chemical composition
PN the least compared to neat diesel, at 90% and 20% respectively. Reductions from
C1214 and C1822 were measured at 60% and 35% respectively. For B50, in the case
of C810, C1214 and C1822, the PN emissions remained lower than diesel although
C1618 increased approximately 10%. Similar to B100, the lowest PN emissions were
observed with C810 for B50 with C1214 and C1822 following the trend for B100.
PN emissions from C1214 increased 15% with a large standard error at 2000 rpm,
while at 1500 rpm it remained almost same to C1822. Apart from B100 and B50, PN
emissions from B20 were found to be slightly less than diesel and almost the same
among the biodiesels with the exception of around 15% increase from diesel at 1500
rpm. Brake specific PN emissions from other engine loads at both rpm are shown in
appendix (Figure A7-2). PN emissions from all other lodes and engine speeds
showed a similar trend with the exception of 1500 rpm 50% load where PN emission
from B20 appeared to have a different trend as compared to the rest of the results.
Figure 7-7-3: Brake specific PN emissions at 1500 rpm 100% load (a) and 2000 rpm
100% load (b).
Chapter 7: Diesel engine testing with biodiesel of controlled chemical composition 189
7.3.3 Particle number size distribution
Particle size distribution (PSD) was always found to be unimodal with a single peak
in the accumulation mode despite the variations in the fuel and the condition of
engine operation (appendix: Figures A7-3 to A7-5). Variations in PSD among the
biodiesels were more prevalent for B100, followed by B50. Comparatively, PSD
from B20 was found to be similar to petroleum diesel regardless of the variations of
biodiesel. Another important feature is that biodiesel reduced a higher proportion of
large particles (mobility diameter >100 nm) compared to nanoparticles (mobility
diameter <50 nm). Nanoparticle emissions from biodiesel however, did not exceed
that of diesels, which have been reported in few studies (Shi et al. 2010; Surawski,
Miljevic, et al. 2011a). The presence of second peak (nucleation mode) in PSD is
responsible for increased nanoparticle emission which we didn’t observe in this
study. Presence of excessive volatiles and semi volatiles in exhaust which partitioned
into particles upon cooling down are the primary contributor to nucleation mode
peak. In addition, impurities in biodiesel especially glycerol doesn’t undergo
complete combustion due to their high viscosity, poor atomisation and mixing
property. They form partially oxidised volatiles and semi volatiles which can be a
major contributor to nucleation mode peak. Biodiesels used in this study was free
from glycerol and other impurities, which might facilitate the absence of nucleation
mode peak in PSD.
7.3.4 Particle median size
Particle size also varied among used fuels in a similar way to PM and PN. In case of
diesel, the median size of the particles in the SMPS size distribution was 61 nm and
56 nm at 1500 and 2000 rpm respectively. For 100% biodiesel, particle median size
was always found to be smaller than for neat diesel and diesel-biodiesel blends
(Figure 7-4a and 7-4b). Among four used biodiesels, C810 produced the smallest
particle median size i.e. 40 nm and 43 nm at 1500 and 2000 rpm respectively,
followed by C1822 which was 53 nm and 44 nm. C1214 and C1618 gave almost the
same particle size as neat diesel with slight difference between two engines operating
speeds. Particle size from B50 was found to be larger than B100 but smaller than
B20 blends. Interestingly, particle emitted from all B20 blends were found to be
larger than diesel with the largest particle median size observed for C1618 B20
190 Chapter 7: Diesel engine testing with biodiesel of controlled chemical composition
blend. Similar trends in particle size were also observed at other engine loads which
are shown in appendix (Figures A7-6 and A7-7). To gain some insight of the
variation in particle sizes among different biodiesels and its blends, particle median
size from all measurement was plotted against the particle number concentrations. As
can be seen on Figure 7-4c a moderate positive correlation, with a Pearson
correlation coefficient of 0.61 was found between particle median size and total
number concentration. This indicates that total PN number concentration through
coagulation could be one of the key parameters influencing the overall particle size.
Higher the particle number emissions, larger the particle size can be. The other
factors may be the biodiesel viscosity, surface tension and especially oxygen
contents which ensure the presence of more oxygen functional groups on the surface
of particles responsible for enhance particle oxidation and subsequent size reduction
(Wang et al. 2009; Zhu, Cheung and Huang 2011).
Figure 7-7-4: Variations in particle median size among used fuels at 1500 rpm 100%
load (a) and 2000 rpm 100% load (b), while (c) shows particle median size variation
with total number concentration.
7.3.5 NOx emissions
NOx emissions were also found to be dependent on biodiesel carbon chain length
and degree of unsaturation (see Figure. 7-5). Biodiesels with higher degree of
Chapter 7: Diesel engine testing with biodiesel of controlled chemical composition 191
saturation and shorter carbon chain length emitted less NOx than biodiesels with
relatively longer carbon chain and higher degree of unsaturation. Interestingly NOx
emissions from C810 and C1214 were found to be less than for diesel especially at
higher blend percentages. An interesting trend in NOx emissions was also observed
among the different blends used. The usual trend, as reported in most of the
literature, is to observe the increase in NOx emissions with the increase of biodiesel
blend percentage (Bakeas, Karavalakis and Stournas 2011; Hoekman and Robbins
2012). While we have observed a similar trend for long chained biodiesels with
higher degree of unsaturation i.e. C1618 and C1822, however for saturated and short
chained biodiesels i.e. C810 the opposite trend was observed. NOx formation mostly
depends on the duration of premixed combustion phase and in cylinder temperature.
Biodiesels with higher degree of unsaturation have low cetane number, which leads
to prolonged premixed combustion favourable for thermal NOx formation. So the
higher NOx emissions from C1618 and C1875 are expected and are due to their
higher unsaturation as well as their higher heating value. On the other hand higher
degree of saturation, higher cetane number and lower heating value of C810 and
C1214, may cause shorter premixed combustion and lower in cylinder temperature
responsible for less NOx emissions. Therefore the discrepancy in reported (Redel-
Macías et al. 2012) NOx emissions, among different biodiesel studies in the
literature, may be due to biodiesel chemical composition. The generally adopted
concept of increase in NOx emissions for biodiesels does not always stand. Rather,
whether biodiesels will increase or reduce NOx emissions depends upon their
chemical composition.
192 Chapter 7: Diesel engine testing with biodiesel of controlled chemical composition
Figure 7-7-5: Brake specific NOx emission at (a) 1500 rpm 100% load and (b) 2000
rpm 100% load.
7.3.6 Influence of fuel physical properties and chemical composition on particle emissions
To understand what is the relative influence of fuel physical properties as well as
chemical composition on particle emissions, PM and PN emissions for all used fuels
were plotted against fuel viscosity, surface tension and oxygen contents. Variations
in PM and PN emissions with fuel viscosity, surface tension and oxygen contents at
100% load are shown in Figure 7-6, where the other loads are shown in appendix
(Figures A7-8 to A7-10). As shown in Figure 7-6, particle emissions increased with
the increase of fuel viscosity and surface tension but only within a specific blend. For
higher blend percentages (B50 and B100) there was almost a linear relationship
between surface tension, viscosity and particle emissions. On the other hand, for the
same viscosity and surface tension, particle emissions also found significantly
different among fuel/fuel mix. It is evident from the literature, both viscosity and
surface tensions have noticeable influence on fuel atomisation process (Ejim, Fleck
and Amirfazli 2007), which is a key parameter relative to in-cylinder soot formation.
Lower the viscosity and surface tension of fuel, more easily they evaporate, atomise
Chapter 7: Diesel engine testing with biodiesel of controlled chemical composition 193
and mix into in-cylinder air, and more complete their combustion are (Lee et al.
2002; Chen et al. 2013). In this case, the used engine was employed with common
rail injection system where fuel injection pressure was high (around 200 bars). Such
a high injection pressure might be minimised the effect of small variation in fuel
viscosity and surface tension on fuel atomisation and subsequent particle emissions.
On the other hand, a more consistent negative relationship was observed between
fuel oxygen content and particle emissions. This relationship did not depend on the
blend percentage. Similar reduction in particle emissions with fuel oxygen content
has also been reported in the literature (S.S. Gilla 2011; Rahman et al. 2013; Xue,
Grift and Hansen 2011). Therefore, this is a clear indication that the fuel chemical
composition, particularly the oxygen content, could be more important than its
physical properties in terms of engine exhaust particle emissions.
Figure 7-7-6: Variation in specific PM and PN emissions with used fuel surface
tension, viscosity and oxygen content
((a), (b), (c) for PM and (d), (e), (f) for PN), Ordinate of (a), (b), (c) and (d), (e), (f) are same where abscissa of (a), (d) and (b), (e) and (c), (f) are same.
194 Chapter 7: Diesel engine testing with biodiesel of controlled chemical composition
7.3.7 Comparison of engine performance and particle emissions among used biodiesels
A comparison between engine performance parameters and particle emissions is
shown in Figure 7-7. In this figure, the vertical axis represents the percentage change
of engine power, break specific fuel consumption and specific PM/PN emissions
while the horizontal axis indicates biodiesel proportion in the blends. Neat diesel was
used as a reference fuel to calculate the percentage changes. There is a significant
difference among all four biodiesels used. For example, C810 provides the highest
reduction in particle mass and number but the penalty for that is also highest, around
25% reduction in engine brake power and an additional 25% increase of specific fuel
consumption due to that reduced engine power. This fuel and power penalty is lowest
for C1618 but particle mass and number reduction is also the lowest in this fuel
blend. Therefore it is necessary to make a trade-off between particle emission
reduction, fuel and power penalty, ensuring maximum benefit; not just for emission
levels but for engine power and fuel economy as well. Considering the
aforementioned factors, C1822 seems to have advantage over the rest of the fuels, as
it maintains the lowest power and fuel penalty regardless of the blending ratio to
diesel and a reasonable reduction in engine exhaust particle emissions. The evidence
suggests that biodiesels with a longer carbon chain length and higher degree of
unsaturation might be a solution to reduce particle emissions to a certain extent with
less fuel and engine power penalty.
Chapter 7: Diesel engine testing with biodiesel of controlled chemical composition 195
Figure 7-7-7: Comparison of engine performance (power, BSFC) and particle
emissions (PM, PN) among biodiesels and their blends where petroleum diesel was
used as a reference fuel.
7.4 CONCLUSIONS
In conclusion, biodiesel fuels with shorter carbon chain lengths and higher degrees of
saturation have more potential to decrease engine exhaust particle emissions. With
the increase of carbon chain length and degree of unsaturation, particle emissions
also increase. Particle size also depends on type of fuel used. Fuel or fuel mix
responsible for higher PM and PN emissions was also found to be to have a larger
particle median size. This indicates that Coagulation plays a role in overall engine
exhaust particle size. Particle emissions increase linearly with fuel viscosity and
surface tension for higher diesel- biodiesel blend percentages (B100, B50). It reduces
consistently with fuel oxygen content regardless of the proportion of biodiesel in the
blends. High fuel injection pressure by common rail injection systems might
minimise the effects of small variation in fuel viscosity and surface tension on
particle emissions. Fuel oxygen content increases with the decrease of FAME carbon
chain length; therefore it is not clear whether FAME carbon chain length or oxygen
content is the driving force that decreases particle emission. The results support the
view that chemical composition of biodiesel is more important than its physical
properties in regards to reducing engine exhaust particle emissions.
Power BSFC PM PN
-40
-30
-20
-10
0
10
20
30
40
50
60
70
80
90
100
Incr
ease
d(%
)R
educ
ed(%
)
B100
Power BSFC PM PN
B50
Power BSFC PM PN
B20
C810 C1214 C1618 C1822
196 Chapter 7: Diesel engine testing with biodiesel of controlled chemical composition
APENDIX A7
Particle emissions from biodiesels with different physical properties and chemical
composition
Figure A7-1: Brake specific PM emissions at 75%, 50% and 25% loads respectively
while the engine operated at 1500 and 2000 rpm respectively.
C810 C1214 C1618 C18220.00
0.02
0.04
0.06
0.08
0.10
Diesel
Bra
ke s
peci
fic P
M (
g/kW
-hr)
Biodiesel type
B20 B50 B100
1500 rpm 75% load
C810 C1214 C1618 C18220.00
0.01
0.02
0.03
0.04
0.05Diesel
2000 rpm 75% load
Bra
ke s
peci
fic P
M (
g/kW
-hr)
Biodiesel type
B20 B50 B100
C810 C1214 C1618 C18220.00
0.02
0.04
0.06Diesel
1500 rpm 50% load
Bra
ke s
peci
fic P
M (
g/kW
-hr)
Biodiesel type
B20 B50 B100
C810 C1214 C1618 C18220.00
0.01
0.02
0.03
0.04
0.05
Diesel
2000 rpm 50% load
Bra
ke s
peci
fic P
M (
g/kW
-hr)
Biodiesel type
B20 B50 B100
C810 C1214 C1618 C18220.00
0.02
0.04
0.06
0.08
0.10
0.12
Diesel
1500 rpm 25% load
Bra
ke s
peci
fic P
M (
g/kW
-hr)
Biodiesel type
B20 B50 B100
C810 C1214 C1618 C18220.00
0.01
0.02
0.03
0.04
0.05
0.06
0.07
Diesel
2000 rpm 25% load
Bra
ke s
peci
fic P
M (
g/kW
-hr)
Biodiesel type
B20 B50 B100
Chapter 7: Diesel engine testing with biodiesel of controlled chemical composition 197
Figure A7-2: Brake specific PN emissions at 75%, 50% and 25% loads respectively
while the engine speed was 1500 and 2000 rpm
C810 C1214 C1618 C18220.0
3.0x1013
6.0x1013
9.0x1013
1.2x1014
1.5x1014
1.8x1014
Bra
ke s
peci
fic P
N(#
/kW
-hr)
Biodiesel Type
B20 B50 B100
1500 rpm 75% load
Diesel
C810 C1214 C1618 C18220.0
2.0x1013
4.0x1013
6.0x1013
8.0x1013
1.0x1014
1.2x1014
1.4x1014
Bra
ke s
peci
fic P
N(#
/kW
-hr)
Biodiesel Type
B20 B50 B100
2000 rpm 75% load
Diesel
C810 C1214 C1618 C1822
2.0x1013
4.0x1013
6.0x1013
8.0x1013
1.0x1014
1.2x1014
1.4x1014
Bra
ke s
peci
fic P
N(#
/kW
-hr)
Biodiesel Type
B20 B50 B100
1500 rpm 50% load
C810 C1214 C1618 C18220.0
4.0x1013
8.0x1013
1.2x1014
1.6x1014
2.0x1014
Bra
ke s
peci
fic P
N(#
/kW
-hr)
Biodiesel Type
B20 B50 B100
2000 rpm 50% load
Diesel
C810 C1214 C1618 C18220.0
5.0x1013
1.0x1014
1.5x1014
2.0x1014
2.5x1014
3.0x1014
Bra
ke s
peci
fic P
N(#
/kW
-hr)
Biodiesel Type
B20 B50 B100
1500 rpm 25% laod
Diesel
C810 C1214 C1618 C18220
1x1014
2x1014
3x1014
4x1014
5x1014
Bra
ke s
peci
fic P
N(#
/kW
-hr)
Biodiesel Type
B20 B50 B100
2000 rpm 25% load
Diesel
198 Chapter 7: Diesel engine testing with biodiesel of controlled chemical composition
Figure A7-3: Particle number size distribution for B100, B50, and B20 at 1500 rpm
100% load (a, b, c respectively) and 2000 rpm 100% load (d, e, and f respectively)
Figure A7-4: Particle number size distribution for B100, B50, and B20 at 1500 rpm
75% load (a, b, c respectively) and 2000 rpm 75% load (d, e, and f respectively)
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266 Appendices
Appendices
APPENDIX A: MatLab code for ANN models training
%Md Jahirul Islam; BERF, QUT, Brisbane %date: August 18, 2014 %** Preparation of training data. clc, clear all; close all; load('C:\Users\n7325819\Desktop\Matlab Codes_Kajol\Data_raw.mat'); Data=Data_raw; % get the data set as you like from initial total data size % percentage of data set for training, testing & validation training_set=0.6 ; validating_set=0.1; testing_set=0.3; %(%) % call the data_Preparation.m file for split the data for training, testing & validation; And prepare the data %for NN traing DataVec = size(Data,2); No_of_Samples = length(Data); %SampleIndex = [1:No_of_Samples]'; % use this if random sample is not allowed SampleIndex = randperm(No_of_Samples)'; % Use this if random sampling is allowed. data_Preparation % call the data preparation m file cd([pwd,'\Results']); save Training_data % save the whole workspace % Prepare the data here for training, testing and validation. %Call the matrix 'Data' with size n*m. First m-1 columns are inputs and last column is the target.) %=================================================================================================== function [TrainSet ValdSet TestSet] = datasplit(A, ptr, pvd, pts) rows = max(size(A)); nVald = round(pvd * rows); % number of samples for validation nTest = round(pts * rows); % number of samples for test nTrain = rows - nVald - nTest; TrainSet = A(1:nTrain,:); ValdSet = A(nTrain+1:nTrain+nVald,:); TestSet = A(nTrain+nVald+1:end,:); %Data=data_PI_dis; %========================================================================== [Index_TrainSet Index_ValdSet Index_TestSet] = datasplit(SampleIndex,training_set,validating_set,testing_set); % Split Data into Test, Validation, and Testing for j = 1:size(Index_TrainSet,1) % Index for Training Set k = Index_TrainSet(j);
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TrainSet(1:DataVec,j) = Data(k,1:DataVec); end for j = 1:size(Index_ValdSet,1) % Index for Validation Set k = Index_ValdSet(j); ValdSet(1:DataVec,j) = Data(k,1:DataVec); end for j = 1:size(Index_TestSet,1) % Index for Testing Set k = Index_TestSet(j); TestSet(1:DataVec,j) = Data(k,1:DataVec); end %=============================================================================================== Data=Data_raw; % get the data set as you like from initial total data size (10,998) %Data=data_NN_PI_ND; % percentage of data set for training, testing & validation training_set=0.7 ; validating_set=0.1; testing_set=0.2; %(%) % call the data_Preparation.m file for split the data for training, testing & validation; And prepare the data %for NN traing DataVec = size(Data,2); No_of_Samples = length(Data); %SampleIndex = [1:No_of_Samples]'; % use this if random sample is not %allowed SampleIndex = randperm(No_of_Samples)'; % Use this if random sampling is %allowed. data_Preparation %=============================================================================================== I = [1:DataVec-1]; % Inputs O = [DataVec]; % Output %=============================================================================================== % Training Set pTr = TrainSet(I,:); tTr = TrainSet(O,:); [pTr,pTrMin,pTrMax,tTr,tTrMin,tTrMax] = premnmx(pTr,tTr); % Preprocessing data with converting the range from -1 to 1. % Validation data pVd = ValdSet(I,:); tVd = ValdSet(O,:); pVd = tramnmx(pVd,pTrMin,pTrMax); tVd = tramnmx(tVd,tTrMin,tTrMax); % Test data pTs = TestSet(I,:); tTs = TestSet(O,:); pTs = tramnmx(pTs,pTrMin,pTrMax); tTs = tramnmx(tTs,tTrMin,tTrMax); MinMax = minmax([pTr pVd pTs]); %===================================================================================================
268 Appendices
Data=Data_raw; % get the data set as you like from initial total data size (10,998) %Data=data_NN_PI_ND; % percentage of data set for training, testing & validation training_set=0.6 ; validating_set=0.1; testing_set=0.3; %(%) % call the data_Preparation.m file for split the data for training, testing & validation; And prepare the data %for NN traing DataVec = size(Data,2); No_of_Samples = length(Data); %SampleIndex = [1:No_of_Samples]'; % use this if random sample is not %allowed SampleIndex = randperm(No_of_Samples)'; % Use this if random sampling is %allowed. data_Preparation % Training the NN to generate *** model % define the training, testing and validating data set %========================================================================= %clear all load('C:\Users\n7325819\Desktop\Matlab Codes_Kajol\Data_raw.mat'); val.P =pVd; val.T=tVd; % Define the validation data test.P = pTs; test.T=tTs; % Define the test data % define the network structure % pTr and tTr are for training data set %========================================================================= Ntotal=10; % total NN models Neur=[6*ones(1,Ntotal/10), 8*ones(1,Ntotal/10), 10*ones(1,Ntotal/10), 12*ones(1,Ntotal/10), 14*ones(1,Ntotal/10), 16*ones(1,Ntotal/10),18*ones(1,Ntotal/10),22*ones(1,Ntotal/10),24*ones(1,Ntotal/10),26*ones(1,Ntotal/10)]; % neuron size in hidden layer MAPE=[]; RMSE=[]; SSE=[]; for netopt=1:Ntotal clear Net1 an MAPE1 RMSE1 SSE1 neur=Neur(netopt) mout=1; % the number of moel output Net1=newff(minmax(pTr),[neur mout],{ 'tansig' 'purelin'}, 'trainbr'); % tansig= tan sigmoid transfer function for hidden neuron input % purelin=linear output function for hidden neuron % Set training parameters % Net.trainParam.epochs=500; % Maximum number of epochs Net1.trainParam.show=100; % Period of showing calculation progress Net1.trainParam.lr=0.1; % Algorithm learning rate Net1.trainParam.goal=.01; % Optimisation goal Net1.trainParam.min_grad=1e-10; % Minimum gradient Net1.trainParam.mem_reduc=1; % Memory reduction parameter Net1.trainParam.max_fail=1000; time0 = cputime; % Use the 'train' command to start the training process. The trained % network will be saved in the structure Net. pTr and tTr are input and
Appendices 269
% targets for the training data set. val and test are the validation and testing data sets respectively. %Net = train(Net,pTr,tTr); [Net1,tr]=train(Net1,[pTr,pVd,pTs],[tTr,tVd,tTs],[],[], val,test); %========================================================================= % Simulate the network with the testing (normalized) data. an = sim(Net1,pTs); % Un-normalize the network prediction data % convert the data as real values a = postmnmx(an,tTrMin,tTrMax); Output=postmnmx(tTs,tTrMin,tTrMax); close all figure(3); plot(a,Output);ylabel('Actual'); xlabel('Predicted'); figure(4); plot(Output); hold; plot(a,'m'); legend('Actual','Predicted'); MAPE1 = mean(abs((tTs-an)./tTs))*100 RMSE1=sqrt((sum((tTs-an).^2))/size(tTs,2)); SSE1=sum((tTs-an).^2); MAPE=[MAPE,MAPE1]; RMSE=[RMSE,RMSE1]; SSE=[SSE,SSE1]; NNtrain(netopt)=struct('NNmodel',Net1, 'trecord', tr, 'MAPE',MAPE1,'RMSE1',RMSE,'SSE1',SSE,'actual',Output,'Predicted',a); end %========================================================================== cd([pwd,'\Results']); save NNtrain %==========================================================================
270 Appendices
APPENDIX B: The eigenvalue for each of the PCs
CN KV Density HHV OS CFPP FP IV
PC 1 13.8644 13.3400 10.0326 12.0796 11.5437 12.0405 11.1067 13.3676
PC 2 4.8300 5.5200 6.7413 5.3383 6.7804 6.0536 5.7753 6.0168
PC 3 1.8400 2.7050 1.7298 2.2664 2.1142 2.0142 2.5236 1.5142
PC 4 0.8820 1.0232 1.8750 1.7064 1.5755 1.3542 1.7066 1.1954
PC 5 0.6046 0.2555 1.8756 1.1426 0.7002 0.7764 0.5986 0.3072
PC 6 0.4692 0.1122 0.4364 0.2334 0.1409 0.4972 0.3698 0.1960
PC 7 0.0951 0.0311 0.1827 0.1396 0.0843 0.2662 0.2962 0.1509
PC 8 0.0962 0.0226 0.0738 0.0564 0.0341 0.0267 0.3302 0.1750
PC 9 0.0759 0.0149 0.0157 0.0120 0.0072 0.0057 0.0701 0.0372
PC 10 0.0710 0.0110 0.0115 0.0088 0.0053 0.0042 0.0516 0.0273
PC 11 0.0643 0.0102 0.0107 0.0082 0.0050 0.0039 0.0480 0.0255
PC 12 0.0516 0.0071 0.0075 0.0057 0.0035 0.0027 0.0335 0.0178
PC 13 0.0365 0.0045 0.0047 0.0036 0.0022 0.0023 0.0287 0.0152
PC 14 0.0132 0.0003 0.0003 0.0003 0.0002 0.0012 0.0147 0.0078
PC 15 0.0059 0.0025 0.0026 0.0020 0.0012 0.0009 0.0117 0.0062
PC 16 0.0011 0.0011 0.0022 0.0017 0.0010 0.0008 0.0098 0.0052
PC 17 0.0005 0.0005 0.0016 0.0012 0.0007 0.0006 0.0078 0.0041
PC 18 0.0003 0.0003 0.0015 0.0012 0.0007 0.0005 0.0075 0.0040
PC 19 0.0002 0.0002 0.0011 0.0009 0.0005 0.0004 0.0055 0.0029
PC 20 0.0002 0.0002 0.0008 0.0006 0.0003 0.0003 0.0040 0.0021
PC 21 0.0001 0.0001 0.0005 0.0001 0.0000 0.0000 0.0000 0.0000
PC 22 0.0000 0.0000 0.0002 0.0000 0.0000 0.0000 0.0000 0.0000
PC 23 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000