Optimization and Economics of Small-scale, On-farm Biodiesel Production using Oilseed Crops 
and Waste Vegetable Oil 
 
by 
 
Daniel Keith Mullenix 
 
 
 
 
A thesis submitted to the Graduate Faculty of 
Auburn University 
in partial fulfillment of the 
requirements for the Degree of 
Master of Science 
 
Auburn, Alabama 
August 6, 2011 
 
 
 
 
Keywords: biodiesel, small-scale, on-farm, transesterification, economics, irrigation 
 
 
Copyright 2011 by Daniel Keith Mullenix 
 
 
Approved by 
 
John P. Fulton, Co-Chair, Associate Professor of Biosystems Engineering  
Ahjeong Son, Co-Chair, Assistant Professor of Civil Engineering 
Sushil Adhikari, Assistant Professor of Biosystems Engineering 
Timothy P. McDonald, Associate Professor of Biosystems Engineering 
Mark Dougherty, Associate Professor of Biosystems Engineering 
 
 
ii 
 
 
Abstract 
 
 
Recent concerns over unpredictable fuel prices and desire to reduce farm input costs have 
warranted producer interest in growing alternative crops for the production of biofuels such as 
biodiesel. Ideally, biodiesel could be produced with a small-scale, on-farm system allowing a farm to 
retain full value of an oilseed crop. Similarly, on-farm biodiesel feedstock could be supplemented with 
waste vegetable oil (WVO) from local sources, which would be an inexpensive input to lower production 
costs. The objectives of this research were to: 1) evaluate the impact of an irrigation regime on oilseed 
constituents (oil concentration, oil free fatty acid (FFA), and protein concentration) within three oilseed 
crops (cotton, soybeans, and canola) grown in a non-traditional rotation; 2) determine a biodiesel 
production and phosphorus removal procedure that could be economical for Alabama farmers and a 
storage interval for vegetable oil in which FFA will remain within acceptable limits for base catalyzed 
transesterification; and 3) assess economics of a current small-scale biodiesel production system in 
Auburn University?s Biosystems Engineering department with the data collected being used to develop a 
prediction model for a small-scale, on-farm biodiesel production system scenario. Five different 
experiments were performed along with a literature review of biodiesel production practices, efficiency 
analyses of two mechanical oil extruders, and economic assessments of two small-scale biodiesel 
production systems. An irrigation experiment examined oilseed constituent responses to varying levels 
of irrigation for biodiesel production. Triglyceride hydrolysis rates and microbial activity of three 
vegetable oils were studied over time. The third experiment determined differences in methanol 
recovery efficiency for two methods. The final two experiments investigated phosphorus removal from
iii 
 
 two crude vegetable oils, quality of biodiesel produced, and phosphorus removed resulting from 
transesterification.  
Results from the irrigation experiment indicated that seed and oil yields significantly improved 
with irrigation and rainfall. As a result, theoretical biodiesel yields increased 380%, 166%, and 200% for 
soybean, cottonseed, and canola, respectively. Protein concentrations tended to decrease with 
irrigation, but FFA values showed little response with emphasis on biodiesel production except when 
seeds were exposed to excessive end of the season moisture. Based catalyzed transesterification with 
methanol and sodium was determined to be least expensive yielding favorable results. WVO and canola 
oil oxidized 68% and 43% during storage and no microorganism activity was detected. While soybean oil 
oxidized 50%, FFA levels remained well under one percent. Vacuum pump assistance increased 
methanol recovery by 600%. ASTM requirements of phosphorus removal were not met during oil 
degumming experiments. Bench scale transesterification reactions resulted in higher ester conversion 
than the Biodiesel Logic, Inc. processor due to increased oil/methanol interaction, but AMBERLITE? 
BD10DRY satisfactorily removed free glycerin from biodiesel. A mechanical screw press with fixed 
components required less operator aptitude to increase oil extraction of canola, however soybean oil 
extraction proved efficient on the Henan Double Elephant mechanical press. Oil extraction efficiency 
was heavily dependent on oilseed moisture content and operator ability. WVO biodiesel cost of 
production was $0.58/L while soybean and canola biodiesel production costs were substantially higher. 
However in 2010, Alabama soybean farmers could have reduced losses by $24.70/ha when producing 
biodiesel as opposed to selling crops through traditional market outlets.  
 
 
 
 
iv 
 
 
 
 
 
 
Acknowledgements 
 
 
I would first like to thank God for His provisions and blessings and for putting special people in 
my life to guide and assist me on a daily basis. I would like to thank my fianc?e, Kim, my parents, Keith 
and Deborah, and my sisters, Lauren and Sarah, for their love, support, and guidance throughout my 
academic endeavors and life up to this point. Appreciation is also extended to Dr. Sushil Adhikari, Dr. 
Ahjeong Son, Dr. Timothy McDonald, Dr. Mark Dougherty, and most of all to Dr. John Fulton for granting 
me this opportunity, offering both professional and academic advice, and seeing me through the 
completion of this project. I would also like to thank the Biosystems Engineering department as a whole 
and the following people who contributed their assistance and offered advice during this research: Tyrel 
Harbuck, Ajay Sharda, Xiaofang Wang, Harideepan Ravindran, Suchithra Gopakumar, Chad Carter, 
Jonathan Hall, and most of all Drs. Shaoyang Liu and Edzard van Santen for their expert assistance in 
experimental and statistical analyses, respectively. 
 
 
 
 
 
 
 
 
v 
 
 
 
 
Table of Contents 
Abstract ......................................................................................................................................................... ii 
Acknowledgements ...................................................................................................................................... iv 
List of Tables ................................................................................................................................................ ix 
List of Figures ................................................................................................................................................ x 
Chapter One Introduction ............................................................................................................................. 1 
1.1 Problem Statement .......................................................................................................................... 1 
1.2 Research Objectives ......................................................................................................................... 3 
1.3 Thesis Organization .......................................................................................................................... 3 
Chapter Two Review of Literature ................................................................................................................ 5 
2.1 Introduction ...................................................................................................................................... 5 
2.2 Oilseed Response to Varying Irrigation ............................................................................................ 5 
2.2.1 Oil Concentration .................................................................................................................. 6 
2.2.2 Protein Concentration ........................................................................................................... 8 
2.2.3 FFA Concentration ................................................................................................................. 9 
2.2.4 Yield ..................................................................................................................................... 12 
2.3 Oil Degumming ............................................................................................................................... 14 
2.3.1 Issues with the Degumming Process ................................................................................... 19 
2.3.2 Preparing Seeds for Extrusion ............................................................................................. 20 
2.4 Hydrolysis Issues ............................................................................................................................. 20 
2.4.1 Seed ..................................................................................................................................... 21 
2.4.2 Vegetable Oil ....................................................................................................................... 21 
2.4.3 Biodiesel .............................................................................................................................. 23 
2.5 Biodiesel Production ....................................................................................................................... 25 
2.5.1 Transesterification............................................................................................................... 25 
2.5.1.1 Acid catalysis .............................................................................................................. 26 
2.5.1.2 Base catalysis ............................................................................................................. 28 
2.5.1.3 Two Step Reactions.................................................................................................... 29 
2.5.2 Influences on Transesterification ........................................................................................ 30 
vi 
 
2.5.2.1 Catalyst ...................................................................................................................... 30 
2.5.2.2 Water ......................................................................................................................... 31 
2.5.2.3 Alcohol ....................................................................................................................... 32 
2.5.2.4 Reaction Temperature ............................................................................................... 34 
2.5.2.5 Reaction Time ............................................................................................................ 34 
2.5.2.6 Glycerol Settling Time ................................................................................................ 35 
2.5.3 Refining Crude Biodiesel ..................................................................................................... 35 
2.6 Economic Analysis .......................................................................................................................... 36 
2.7 Effects of Microorganisms in Biodiesel Storage Tanks ................................................................... 42 
2.7.1 Biodiesel Storage ................................................................................................................. 43 
2.7.1.1 Storage Temperature ................................................................................................. 43 
2.7.1.2 Water Contamination in Biodiesel ............................................................................. 43 
2.7.2 Microbial Growth ................................................................................................................ 47 
2.7.3 Environmental Impacts ....................................................................................................... 48 
2.7.4 Biodiesel Storage Stability ................................................................................................... 49 
2.8 Summary ......................................................................................................................................... 49 
Chapter Three Research Methods .............................................................................................................. 52 
3.1 Introduction .................................................................................................................................... 52 
3.2 Irrigation Experiment for a Non-traditional Energy Crop Rotation ................................................ 52 
3.3 Review of Biodiesel Production Procedures .................................................................................. 56 
3.4 Vegetable Oil Storage Experiment ................................................................................................. 57 
3.4.1 Triglyceride Hydrolysis ........................................................................................................ 57 
3.4.2 Microbial Contamination .................................................................................................... 58 
3.5 Methanol Recovery Efficiency Experiment .................................................................................... 60 
3.6 Phosphorus Removal from Vegetable Oil Experiment ................................................................... 64 
3.6.1 Hot Water Method .............................................................................................................. 64 
3.6.2 Citric Acid Method ............................................................................................................... 65 
3.6.3 Anion Exchange Resin Method ?AMBERSEP? BD19 .......................................................... 66 
3.6.4 Control ................................................................................................................................. 68 
3.6.5 Experimental Design............................................................................................................ 68 
3.7 Biodiesel Quality Experiment ......................................................................................................... 69 
3.7.1 Biodiesel Logic, Inc. Biodiesel Processor ............................................................................. 69 
3.7.2 Degummed Oils ................................................................................................................... 70 
vii 
 
3.8 Mechanical Extruder Efficiency Analysis ........................................................................................ 72 
3.9 Economic Analysis of Small-Scale Biodiesel Production Systems .................................................. 72 
3.9.1 Auburn University?s Biosystems Engineering Biodiesel Production System ....................... 72 
3.9.2 Small-scale, On-farm Biodiesel Production System ............................................................ 75 
3.10 Statistical Analysis ........................................................................................................................ 76 
3.10.1 Irrigation Experiment for a Non-traditional Energy Crop Rotation ................................... 76 
3.10.2 Vegetable Oil Storage Experiment .................................................................................... 77 
3.10.3 Methanol Recovery Experiment........................................................................................ 78 
3.10.4 Biodiesel Quality Experiment ............................................................................................ 78 
3.10.5 Mechanical Extruder Efficiency Analysis ........................................................................... 78 
3.10.6 Economic Analysis of Small-Scale Biodiesel Production Systems ..................................... 79 
Chapter Four Results and Discussion .......................................................................................................... 80 
4.1 Introduction .................................................................................................................................... 80 
4.2 Irrigation Experiment for a Non-traditional Energy Crop Rotation ................................................ 80 
4.2.1 Cotton .................................................................................................................................. 80 
4.2.2 Soybeans ............................................................................................................................. 85 
4.2.3 Canola .................................................................................................................................. 88 
4.2.4 Irrigation Summary.............................................................................................................. 89 
4.3 Review of Biodiesel Production Procedures .................................................................................. 89 
4.4 Vegetable Oil Storage Experiment ................................................................................................. 95 
4.4.1 Triglyceride Hydrolysis ........................................................................................................ 95 
4.4.2 Microbial Contamination .................................................................................................... 97 
4.5 Methanol Recovery Efficiency Experiment .................................................................................... 99 
4.6 Phosphorus Removal from Vegetable Oil Experiment ................................................................. 102 
4.7 Biodiesel Quality Experiment ....................................................................................................... 104 
4.7.1 Biodiesel Logic, Inc Biodiesel Processor ............................................................................ 104 
4.7.2 Degummed Oils ................................................................................................................. 107 
4.8 Mechanical Extruder Efficiency Analysis ...................................................................................... 113 
4.9 Economic Analysis of Small-scale Biodiesel Production Systems ................................................. 116 
4.9.1 Auburn University?s Biosystems Engineering Biodiesel Production System ..................... 116 
4.9.2 Small-scale, On-farm Biodiesel Production System .......................................................... 117 
Chapter Five Summary and Conclusions ................................................................................................... 126 
5.1 Summary ....................................................................................................................................... 126 
viii 
 
5.2 General Conclusions ..................................................................................................................... 131 
5.3 Future Research ............................................................................................................................ 132 
References ................................................................................................................................................ 134 
Appendix A Biodiesel Production Procedures and Conversion Efficiency Review ................................... 142 
A.1 Table of various transesterification techniques in published literature. ..................................... 143 
Appendix B Experimental Equipment and Chemical Specifications ......................................................... 147 
B.1 Chemyx? Nexus Model 6000 syringe pump ................................................................................ 148 
B.2 Gast Manufacturing, Inc. vacuum pump model DOA-P704-AA ................................................... 149 
B.3 New Brunswick Scientific Innova 40 incubator shaker ................................................................ 150 
B.4 Molecular Devices Spectramax M2 Microplate Reader ............................................................... 151 
B.5 Agilent Technologies Gas Chromatograph model 7890A ............................................................ 152 
B.6 Henan Double Elephant Machinery Co., Ltd (Henan, China) model 6YL-120 .............................. 153 
B.7 Karl Str?hle GmbH & Co. KG (Dettingen, Germany) SK 60/2 ....................................................... 154 
B.8 Biodiesel Logic, Inc. BDL-55-SS-A ................................................................................................. 155 
B.9 AMBERSEP? BD 19 ....................................................................................................................... 156 
B.10 AMBERLITE? BD10DRY? Biodiesel Purification Technology ..................................................... 158 
B.11 Labconco? Purifier Biological Safety Cabinet ............................................................................. 161 
Appendix C Oil Storage Experiment Regression Tables. ........................................................................... 162 
C.1 WVO linear regression analysis for hydrolysis rate. ..................................................................... 163 
C.2. Canola oil linear regression analysis for hydrolysis rate. ............................................................ 164 
C.3. Soybean oil linear regression analysis for hydrolysis rate........................................................... 165 
C.4 Test for hydrolysis slope homogeneity and interaction. .............................................................. 166 
Appendix D Irrigation Experiment Results by Crop and Year & Irrigation Scheduling Spreadsheet  
Example ........................................................................................................................................ 168 
D.1 2008 canola results. ..................................................................................................................... 169 
D.2 2010 canola results. ..................................................................................................................... 169 
D.3 2008 rotation 1 cotton results. .................................................................................................... 170 
D.4 2009 rotation 1 cotton results. .................................................................................................... 170 
D.5 2009 rotation 2 cotton results. .................................................................................................... 171 
D.6 2010 rotation 1 cotton results. .................................................................................................... 171 
D.7 2008 soybean results. .................................................................................................................. 172 
D.8 2010 soybean results. .................................................................................................................. 172 
D.9 Irrigation scheduling spreadsheet example. ................................................................................ 173 
ix 
 
 
 
 
List of Tables 
Table 2.1. Breakeven oilseed crop values at various biodiesel prices. (Kenkel and Holcomb,  
unpublished data). ......................................................................................................................... 40 
Table 2.2. Water contents (ppm) in various fuels and blends after vigorous mixing with water for  
various periods at 25 oC. (Van Gerpen et al., 1996) ....................................................................... 46 
Table 2.3. The growth of  microorganisms on various diesel and methyl soyate liquid media. ................ 48 
Table 3.1. Methanol recovery experiment summary. ................................................................................ 64 
Table 3.2. Order of operations for AMBERSEP? BD19 phosphorus removal experiment. ........................ 67 
Table 3.3. Summary of phosphorus removal tests performed. .................................................................. 68 
Table 4.1 2008 through 2010 Cottonseed oil results. ................................................................................. 84 
Table 4.2. 2008 & 2010 canola results. ....................................................................................................... 89 
Table 4.3. High ester yielding biodiesel production procedures with low chemical costs. ........................ 90 
Table 4.4. Published biodiesel production procedures based on oil type. ................................................. 93 
Table 4.5. Initial water content of samples for the oil storage experiment. .............................................. 96 
Table 4.6. Evaporative method reliability for methanol determination. ................................................. 100 
Table 4.7. Volumetric balance and economic analysis of methanol recovery experiment. ..................... 102 
Table 4.8. Results of phosphorus removal from canola oil. ..................................................................... 103 
Table 4.9. Results of phosphorus removal from soybean oil and biodiesel. ............................................ 104 
Table 4.10. Quality of biodiesel produced from the Biodiesel Logic, Inc. processor. ............................... 106 
Table 4.11. Phosphorus content in degummed, transesterified canola oil. ............................................. 108 
Table 4.12 Quality of biodiesel produced from degummed vegetable oils (soybean and WVO). ........... 112 
Table 4.13. Quality of biodiesel produced from degummed vegetable oils (canola). .............................. 112 
Table 4.14. Henan Double Elephant model 6YL-120 (1) and Karl Str?hle SK60/2 (2) mechanical  
screw press assessment summary. .............................................................................................. 113 
Table 4.15. Annual economic analysis for Auburn University?s Biosystems Engineering biodiesel 
production system. ...................................................................................................................... 117 
Table 4.16. Annual economic model for small-scale, on-farm biodiesel production. .............................. 119 
Table 4.17 Small-scale, on-farm biodiesel production system recommended equipment. ..................... 120 
Table 4.18. 2006-2010 Soybean Profit/Loss Economics (ACES, 2011). ..................................................... 123 
Table 4.19. Biodiesel cost of production sensitivity analysis. ................................................................... 125 
x 
 
 
 
List of Figures 
Figure 1.1. Soybean and diesel fuel prices from 1980 ? 2010 (USDA NASS, 2011). ..................................... 2 
Figure 2.1. Chemical formula of transesterification. .................................................................................. 26 
Figure 2.2. Purities of biodiesel degradation at different temperatures. ................................................... 44 
Figure 2.3. Saturation MC of biodiesel and diesel at three temperatures. ................................................ 46 
Figure 3.1. Layout of study site and treatment assignment. ...................................................................... 55 
Figure 3.2. Vegetable oil storage experiment ............................................................................................. 57 
Figure 3.3. Biodiesel Logic Processor (Biodiesel Logic, Inc. Troy, AL). ........................................................ 61 
Figure 3.4. Incubator shaker used for agitation of oils (left) and settled gum phase in soybean  
oil (right)......................................................................................................................................... 66 
Figure 3.6. Spill pallet with dolly ramp and 30-gallon drums required by Auburn University Risk 
Management. ................................................................................................................................. 73 
Figure 3.7. Primary (left) and secondary (right) WVO settling tanks. ......................................................... 74 
Figure 3.8. Storage tank for processed biodiesel. ....................................................................................... 75 
Figure 4.1. 2008 through 2010 cotton yield results. ................................................................................... 81 
Figure 4.2. 2008 through 2010 cotton protein results. .............................................................................. 82 
Figure 4.3. Cottonseed germination inside the boll and prior to harvest, 2009. ....................................... 82 
Figure 4.4. 2008 through 2010 cottonseed FFA results. ............................................................................. 84 
Figure 4.5. 2008 & 2010 soybean yield results. .......................................................................................... 85 
Figure 4.6. 2008 & 2010 soybean protein results. ...................................................................................... 86 
Figure 4.7. 2008 & 2010 soybean oil results. .............................................................................................. 87 
Figure 4.8. FFA accumulation in vegetable oil samples over time. ............................................................. 96 
Figure 4.9. Experimental sample data from absorbance testing over time. .............................................. 98 
Figure 4.10. Bacteria positive control sample analysis from absorbance testing ...................................... 99 
Figure 4.11. Operator adjusting screw position on a Henan Double Elephant 6YL-120 mechanical  
screw press for optimal oil output ............................................................................................... 114 
Figure 4.12. Karl Strahle SK60/2 mechanical screw press. ....................................................................... 115 
Figure 4.13. Hopper and augering system designed and built for the Henan Double Elephant  
mechanical screw press. .............................................................................................................. 115 
xi 
 
Figure 4.14. Soybean biodiesel production cost based on historical commodity prices and  
production area. .......................................................................................................................... 121 
Figure 4.15. Canola biodiesel production cost based on historical commodity prices and  
production area. .......................................................................................................................... 122 
Figure 4.16. WVO biodiesel production cost. ........................................................................................... 123 
 
 
 
 
 
1 
 
 
 
 
CHAPTER ONE 
INTRODUCTION 
1.1 Problem Statement 
Emphasis on alternative energy in the U.S. has increased with the desire for fuel independence 
and threat of petroleum oil scarcity. One heavily researched outlet for bioenergy production is biodiesel. 
Biodiesel can be produced from any renewable fat or oil source (e.g. animal fat or vegetable oil) and 
requires little to no infrastructure or diesel engine modifications to transport and store or utilize, 
respectively. In recent years, various large scale production plants were established around the country 
to produce biodiesel, but many have since ceased production. ?Growing pains? in large scale biodiesel 
production (Crooks, 2008), are the result of increasing commodity prices and logistics of transportation 
costs. But commodity prices have not suffered alone as a result of market volatility. Many farming inputs 
(e.g. fertilizer and fuel) have become unstable in recent years, leaving some profit margins in the red. 
Figure 1.1 illustrates market price fluctuation in soybeans and diesel fuel over the past 30 years (USDA 
NASS) demonstrating recent increase. Given this data, it is understandable how in 2009 Alabama 
soybean farmers experienced an average profit of $53/ha, but a loss of $250/ha in 2010 (ACES, 2011). A 
requisite for consistent and manageable farming input costs is imperative for the future of sustainable 
agriculture.  
Implementation of a small-scale, on-farm biodiesel production system could provide normalcy 
and security in the midst of volatile markets by giving farmers more options for maximizing returns on 
their crops and minimizing potential losses as experienced several times in recent years.  Biodiesel could 
2 
 
be produced from portions of oilseed crops grown on farm, supplementing yearly fuel and livestock feed 
requirements, or crops could be sold through traditional commodity markets. This scenario allows the 
farmer to weigh his options each year and determine where the most economic gain can be achieved. 
Despite the recent increase in commodity markets, Faircloth (2008) suggested that if farmers could  
Figure 1.1. Soybean and diesel fuel prices from 1980 ? 2010 (USDA NASS, 2011).  
 
develop on-farm processing and handling of oilseeds, the value of (vegetable) oils would be retained on 
the farm and producers might have an opportunity to become more fuel independent, thus 
relinquishing total reliance on one unpredictable parameter, the petroleum fuel market. However, in 
terms of farming operations in which bioenergy crops may be grown to facilitate on-farm fuel demand 
and in the event small-scale, on-farm biodiesel production facilities are employed, limited research 
exists on the effectiveness of pragmatic development and operational aspects of an integrated system. 
Research highlighting methods of managing on-farm biodiesel production cost and bringing a system 
such as this to fruition in current markets are also limited. Therefore, the scope of this research covered 
oilseed crop constituent responses to irrigation that were important for biodiesel production, practical 
methods of processing crude vegetable oils in preparation for transesterification and techniques to 
increase biodiesel processor efficiencies and end product quality. Economic assessment of a current 
3 
 
small-scale biodiesel production process was ascertained and the data acquired was used to predict 
production costs for an on-farm scenario and assist in determining how to minimize production costs. 
This research will allow the feasibility and economic impacts of a small-scale, on-farm biodiesel 
production system to be better understood in order to equip Alabama farmers with vital decision 
making tools and opportunities that will ensure successful future farming operations.   
1.2 Research Objectives 
The main goal of this experiment was to investigate practical parameters of small-scale, on-farm 
biodiesel production that would increase the efficiencies and effectiveness of oilseed production and oil 
and biodiesel processing techniques; ultimately resulting in quality end products that would benefit 
Alabama farmers. Therefore, the objectives of this research were to: 
1. Evaluate the impact of an irrigation regime on oilseed constituents (oil concentration, oil free fatty 
acid (FFA), and protein concentration) within three oilseed crops (cotton, soybeans, and canola) 
grown in a non-traditional rotation, 
2. Determine a biodiesel production and phosphorus removal procedure that is economical for 
Alabama farmers and a storage interval for vegetable oil in which FFA will remain within acceptable 
limits for base catalyzed transesterification, and  
3. Assess economics of a current small scale biodiesel production system in Auburn University?s 
Biosystems Engineering department with the data collected being used to develop a prediction 
model for a small-scale, on-farm biodiesel production system scenario. 
1.3 Thesis Organization 
The Introduction chapter provides a brief overview of problems currently facing biodiesel 
production systems along with the goals of this research. The Literature Review highlights previous 
4 
 
research on topics related to the objectives of this project and additional pertinent material. Next, 
chapter three Research Methods outlines equipment, procedures, design and data that will be collected 
for each experiment. The Results and Discussion chapter reports collected data and analysis along with 
discussion to support the results. Finally, the last chapter offers experimental summary and final 
conclusions for this study along with suggestions for future research. 
5 
 
 
 
 
CHAPTER TWO  
  REVIEW OF LITERATURE 
2.1 Introduction 
In today?s economy, alternative energy production is a popular topic of discussion in arenas of 
academia, industry and small businesses such as family-owned farms. Knowledge dissemination, 
especially for biodiesel production, is allowing the average person to begin trying his hand at shade-tree, 
small-scale biodiesel manufacturing in hopes of alleviating partial dependence on petroleum fuels. 
However, questions still remain as to best practice methodologies of optimizing feedstock production 
and processing techniques and practical means of obtaining a quality end product and economics of a 
small-scale biodiesel production system. To begin studying the topic of optimization and economics of 
small-scale, on-farm biodiesel production using oilseed crops and waste vegetable oil, previously 
performed research has been carefully reviewed. This chapter presents previous research and 
experimentation on various issues including crop responses to irrigation, oil refining, oil and biodiesel 
stability, biodiesel production, and economics.  
2.2 Oilseed Response to Varying Irrigation 
In the southeastern United States where rainfall can be limited during the growing season, 
irrigation is utilized to minimize crop yield variability from year-to-year, maximize yield and increase 
return. New technology such as variable-rate irrigation could improve in-field water use efficiency. 
Additionally, with the rise in fuel prices, producers have become interested in growing alternative crops 
for the production of biofuels such as biodiesel. An ideal oilseed used for biodiesel production would 
6 
 
include high oil and protein concentrations along with low oil free fatty acid (FFA) constituents.  With a 
high level of protein, the co-product or cake, from the mechanical oil extraction process could be used 
as a protein increasing livestock feed ration supplement. Likewise, a high concentration of oil would lend 
itself to increased yields of biodiesel and a low concentration of FFA would allow for simple 
transesterification techniques to be used. According to Livingston et al. (1995), Faircloth (2008) and 
Boydak et al. (2002), canola contains about 40% oil while cottonseed and soybeans range from 18% to 
25% oil. However, these oilseed constituents may be affected by irrigation at different levels.  
Cottonseed, soybeans, and canola have seed properties favorable for our intentions as well as 
climatic growing requirements experienced in the southeastern United States and irrigation at a certain 
level may facilitate these properties rendering them more favorable for biodiesel production. 
Traditionally, cotton has been the predominant crop grown in the southeastern United States and, 
according to Sawan et al. (2006), cottonseed meal is classed as a protein supplement in the feed trade. 
However, soybean is the primary oilseed used in U.S. biodiesel production at present (Faircloth, 2008). 
The actual composition of soybeans depends on many factors, including genotype, growing season, 
geographic location, and agronomic practices (Boydak et al., 2002). This statement holds true for not 
only soybeans, but also a majority of agronomic crops including canola and cotton. 
2.2.1 Oil Concentration 
With soybean being the dominant oilseed crop in the US and maintaining substantial presence 
in commodity markets alongside its derivatives of oil and meal, understanding irrigation and soybean oil 
concentration interaction is especially important. Bellaloui and Mengistu (2008) experimented with two 
cultivars of soybeans (Dwight and Freedom) and three irrigation regimes (full-season irrigation; FS, 
reproductive stage/supplemental irrigation; RI, and non-irrigation; NI) to compare yield and seed 
composition differences. This experiment was conducted at the Delta Research and Extension Center, 
Stoneville, MS. They found that Freedom resulted in higher oil percentages under FS and RI than Dwight 
7 
 
but 2004 was the only cropping season in which significant differences were experienced with a mean 
difference of 1.3%. However, under NI conditions, Dwight resulted in higher oil accumulation (23.5%) 
compared to Freedom (17.8%). There work suggested that seed composition differences may be 
attributed to cultivar, maturity group, water stress and/or adaptability to irrigation. 
Boydak et al. (2002) experimented with irrigation and row spacing on oil concentration in 
soybeans in the Harran region of Turkey. Irrigation was performed every 3rd, 6th, 9th, or 12th day. They 
found that oil concentration was not significantly different for soybeans irrigated on the 3rd, 9th or 12th 
day and 6th day irrigation intervals resulted in lowest oil yield. It is unclear as to the degree of 
significance because the oil concentration P value for 6th day irrigated soybean was not reported, but 
the mean oil concentration was only one percent lower than the average of high oil yielding treatments. 
And in a similar study, Ibrahim and Kandil (2007) explained yield and chemical constituent effects of 
soybeans due to irrigation and plant row spacing in Egypt. Soybeans were irrigated at 7, 14, and 21 day 
intervals. No significant differences were observed during the two years of the study for oil 
concentration responses to irrigation with averages ranging from 19% to 20%. The work of these 
researchers recognized that irrigation was an important factor in facilitating crop yield, but irrigation 
intervals from 3 days to 21 days did not impact oil production in soybeans. 
Sprecht et al. (2001) measured oil concentration response to water replenishment of soybeans. 
Plots were irrigated at varying levels of 0%, 20%, 40%, 60%, 80%, and 100% pan evaporation 
replenishment. During the 1995 growing season, drought allowed the full water potential to be 
measured with 100% pan evaporation replenishment plots requiring 41-cm water. They found that 
increasing irrigation decreased oil concentrations from 18.1% to 17.5% for 0% to 100% pan evaporation 
replenishment, respectively. This result suggested that increased water quantity, not irrigation interval, 
may negatively impact oil concentration in soybeans. 
8 
 
Canola, being genetically developed in and native to Canada is traditionally a spring crop. 
However, in the southeast US, it is grown as a winter crop as this season?s climatic conditions more 
closely follow those of springtime in Canada. According to Livingston et al. (1995) and Neilsen (1998), 
canola may contain a range of 36.6% to 44.2% oil concentration. Similar to findings of Sprecht et al. 
(2001) for soybeans, Sims et al. (1993) found that increased water led to decreased oil content in canola. 
Taylor et al. (1991) found that no irrigation of canola coupled with increasing nitrogen rates, from 0 
kg/ha to 200kg/ha, decreased oil content from 46.4% to 40.6%, respectively. However, irrigated plots 
experienced increased oil but a similar trend existed in which nitrogen rate increase caused a decrease 
in oil content. This leads to a conclusion that irrigation alone may not necessarily be the deciding factor 
in oil concentration within a canola seed. Other parameters such as macronutrients, in this case 
nitrogen, may have an impact on oil concentration within canola seeds. 
2.2.2 Protein Concentration 
On average, 35% to 45% protein can be expected in soybeans (Boydak et al., 2002). However, 
protein content may be influenced by irrigation according to Ibrahim and Kandil (2007) who found that 
increased irrigation intervals for soybeans resulted in increased protein concentrations. On the other 
hand, severe drought led to decrease in protein concentration in soybeans (Specht et al., 2001).  
In the study by Bellaloui and Mengistu (2008) outlined above, Dwight exhibited higher protein 
concentrations under FS and RI than NI, however, Freedom resulted in higher protein percentages under 
NI. Freedom exhibited the highest level of protein for both cultivars during the experiment under NI 
(45.5%).  As with oil concentration, cultivar differences appear to impact protein concentration as 
Dwight responded positively to irrigation but Freedom responded positively to non-irrigation.  
In addition to measuring oil concentration responses of soybeans to varying levels of irrigation 
as illustrated above, Sprecht et al. (2001) measured protein concentration responses as well. They found 
that by increasing irrigation, protein concentrations increased from 44.8% to 46.0% for 0% to 100% pan 
9 
 
evaporation replenishment, respectively. Soybean experiments by Chung et al. (2003) closely followed 
those of Sprecht et al. (1986, 2001) with an explicit negative correlation being made between oil content 
and protein. To quantify this assessment, Chung et al. (2003) irrigated a hybrid soybean with one of six 
irrigation regimens: 0%, 20%, 40%, 60%, 80%, and 100% replacement of weekly evapotranspiration. 
They found that protein concentration was enhanced with incremental seasonal irrigation increases, 
while oil content was decreased.  
Boydak et al. (2002) and Ibrahim and Kandil (2007) took a slightly different approach by 
experimenting with interactions between irrigation and row spacing on protein concentration in 
soybeans with experimental procedures being conducted as outlined above. Boydak et al. (2002) found 
that protein concentrations were not significantly different for 3rd, 6th, and 9th day irrigated beans with 
3rd day irrigated beans exhibiting highest protein yields (38.6%) and 12th day lowest (37.6%). This 
research parallels the findings of Bellaloui and Mengistu (2008) with certain cultivars but contrasted 
Ibrahim and Kandil (2007). An intermediate interval of 14 days resulted in highest protein concentration 
of 28.9% as opposed to 7 day or 21 day irrigation intervals (Ibrahim and Kandil, (2007). Along with 
diverging experimental results, overall protein percentages were different as well. Suggestions of 
Boydak et al. (2002) may once again explain this discrepancy by recognizing that genotype, growing 
season, geographic location, and agronomic practices can significantly impact oilseed constituents.  
2.2.3 FFA Concentration 
Free fatty acid (FFA) concentration is among the most important constituent of oilseeds for 
biodiesel production. Along with high yields of oil, irrigation effects on FFA concentrations are of 
importance for biodiesel production since it has been reported that high levels of FFA in canola oil, in 
particular, hasten rancidity (hydrolysis) and shorten the shelf life of the oil. Levels of FFA above 1% result 
in extra costs for removing the FFA from the oil (May et al., 1994). Low concentrations of FFA in 
10 
 
vegetable oil are desirable for the aforementioned reasons and because it will minimize the amount of 
catalyst required for the biodiesel process and avoid the potential for acid catalysis pretreatment.  
In the experiment of Boydak et al. (2002) outlined above, FFA values were measured in addition 
to oil and protein concentrations. They reported that FFA concentrations are dictated by variety, 
planting date, and meteorological factors. Oleic acid content was found to be highest for 12th day 
irrigation of soybeans as opposed to 3rd, 6th, or 9th day irrigation intervals and correlated to oil 
concentration increase as well.  
Lee et al. (2008) investigated unsaturated fatty acid content in soybeans in modified varieties 
under irrigation and rainfed scenarios in Missouri. They found that irrigation had no significant effect on 
unsaturated fatty acid accumulation in soybeans but significant differences did exist over years. 
However, trends existed in eight of the nine varieties studied, in which oleic acid (18:1) was higher in 
irrigated plots than non-irrigated plots. They suggested that fatty acid levels correlate to growing season 
temperature. Two- and three-way interactions of year, environment and genotype, excluding irrigation, 
resulted in significant differences for fatty acids. 
In the two cultivars of soybeans studied by Bellaloui and Mengistu (2008), Dwight and Freedom, 
FFA concentrations were not consistent in their response to irrigation but cultivar differences were 
observed averaging 30% more FFA in Dwight as opposed to Freedom for the three irrigation treatments. 
Their results correspond to the research above suggesting that factors other than irrigation can have a 
profound impact on FFA and fatty acids in oilseeds. 
To investigate factors that may lead to FFA accumulation in oilseeds, Evans et al. (1974) 
capitalized on an anomally that illustrated the potential of factors outlined by Boydak et al. (2002).  
Harvest and crude oil quality of the 1974 southeastern U.S. soybean crop was poor as measured by FFA 
accumulation. Damage to soybeans is known to increase FFA in crude oil. FFA increase was believed to 
be a direct result of prolonged moisture exposure in the field experienced in November of 1974.  
11 
 
Extensive field and storage damage to soybeans was reported to be 25% to 35%. Once the bean was 
ready for harvest but was exposed to excessive moisture, the lipase enzyme is activated (Smouse, 1995) 
within the soybean and germination is initiated. In order for the seed to reach emergence, energy was 
needed. This energy was obtained from the breakdown of triglycerides, thus releasing FFA and creating 
diglycerides and eventually monoglycerides. This reaction and energy release supports the early stage of 
the soybean plant until the root system is established to acquire energy through nutrient and water 
uptake and begin photosynthesis. Smouse (1995) went on to suggest that prolonged moisture exposure 
and/or split beans will contain higher non-hydratable phospholipids with split beans containing higher 
FFA levels. If seeds have been damaged by heat, wet weather, excessive moisture, poor storage, or 
other damaging conditions, then the crude oil obtained will require further processing to obtain an 
edible or usable product plus have poor stability during storage.  
May et al. (1994) investigated the effects of agronomic practices on spring canola FFA levels in 
Ontario, Canada. Canola plots were irrigated every fourth day with 2 cm of water until physiological 
maturity. They suggested that decreasing seeding rates, high nitrogen, delay in planting date, and 
drought stress will raise FFA levels in canola. However, irrigation during seed filling decreased FFA levels. 
Irrigation increased yield and oil content but decreased FFA during this study, although the effects of 
irrigation on FFA were not significant within years. Increasing seeding rates from 1 to 9 kg/ha resulted in 
lower FFA, 0.54% to 0.35%, respectively. Likewise, 200 kg/ha nitrogen application resulted in the highest 
yield, lower oil yield and highest FFA concentrations as opposed to the 100 kg/ha and 0 kg/ha nitrogen 
rate treatments. 
Taylor et al. (1991) found that rainfed conditions increased FFA in canola but Boydak et al. 
(2002) reported that irrigation had no significant effect on FFA levels in canola. Data from these 
researchers coupled with the research above, further solidifies the statement by Boydak et al. (2002) 
12 
 
that irrigation may have an effect on oilseed constituents, but interactions among environmental 
factors, agronomic practices and cultivars account for much of the significant differences experienced. 
2.2.4 Yield 
It has long been an accepted theory in the scientific community that irrigation facilitates crop 
yield. Nevertheless, no one parameter can be singled out as the determining factor in crop performance 
or yield. In the study by Bellaloui and Mengistu (2008), irrigation, cultivar and year were found to be 
highly significant (P ? 0.01) for yield differences. Irrigation proved to increase yield in both cultivars 
(approximately 100% and 30% increase in two of the three years of the study for Dwight and Freedom, 
respectively), however yield was also dictated by cultivar and growing conditions (such as soil water 
content and temperature) for the respective years. 
Sprecht et al. (2001) measured water response of soybeans on yield concentration. Plots were 
irrigated with 0%, 20%, 40%, 60%, 80%, and 100% pan evaporation replenishment. During the 1995 
growing season drought allowed the full water potential to be measured with 100% pan evaporation 
replenishment plots requiring 41 cm water. They found that increasing irrigation increased yield from 
933 kg/ha to 2,085 kg/ha for 0% to 100% pan evaporation replenishment, respectively.  
Similarly, Chung et al. (2003) experimented with the same irrigation treatments applied to 
replace weekly evapotranspiration. They discovered correlations among yield, protein concentration 
and oil concentration such that yield and protein were enhanced with incremental seasonal irrigation 
increases, while oil content was decreased. These finding correlated to Sprecht et al. (1986, 2001). 
However, Ibrahim and Kandil (2007) found that an intermediate, 14 day irrigation interval had the most 
impact on yield rather than row spacing. Fourteen day irrigation intervals resulted in highest yield 
compared to 7 day and 21 day intervals and the interaction between irrigation interval and row spacing 
resulted in 14 day irrigation coupled with 10-cm, 20-cm and 30-cm row spacing yielded highest soybean 
harvest of all treatment interactions, respectively. 
13 
 
It has been reported that the highest yields of canola will be obtained when adequate soil 
moisture is maintained throughout the growing season (Thomas et al., 1986) suggesting that sufficient 
rainfall or higher levels of irrigation are required to optimize yield in canola. Imtiyaz et al. (2000) 
researched yield under variable irrigation, 20%, 40%, 60%, 80%, and 100% pan evaporation 
replenishment. They found that the highest marketable yields of rape at 80% pan evaporation 
replenishment through irrigation. This level is speculated to have provided adequate soil moisture as 
recommended by Thomas et al. (1986). Irrigation production efficiency was highest at 20% pan 
evaporation replenishment; however, yields did increase with irrigation replenishment but did not result 
in profitable yields considering the seasonal application of water required at higher irrigation levels. 
Likewise, Sims et al. (1993) found that increased irrigation led to increased yield. In Alberta, 
Canada, canola produced seed yields of about 1000 kg/ha with 20 cm of water. Each additional 2.54 cm 
of water resulted in 150 kg/ha increase in yield, Anonymous (1985). Likewise, Nutall et al. (1992) found 
that canola yield in Saskatchewan, Canada increased by 150 kg/ha for each additional 2.54 cm of 
precipitation in July and August. However, Rife and Salgado (1996) stated the winter-type canola 
varieties generally have greater yield potential than spring-type varieties. 
In addition to irrigation and as is the case in other agronomic crops, irrigation alone is not be the 
sole driving force behind crop yields. Taylor et al. (1991) investigated yield responses of canola to 
irrigation and nitrogen application. Plots were rainfed or irrigated at 50-mm deficit with 0, 50, 100, or 
200 kg N/ha applied. They found that increased nitrogen and irrigation levels on canola increased yield. 
They recommended that a minimum nitrogen rate of 100 kg/ha was required to optimize canola yield. 
Any additional nitrogen would increase yield but may induce lodging because of excessive vegetative 
growth and plant height, ultimately resulting in yield loss. 
Because cotton is typically valued for its lint yield, research is limited on cottonseed responses 
to irrigation, environmental factors, agronomic practices, etc. As reported by McKevith (2005) 
14 
 
cottonseed averages 32.6% protein making it a valuable protein increasing livestock feed supplement 
which is already the case for most of cottonseed in the US at present. However, it appears that macro 
and micronutrients can influence cottonseed and cottonseed constituents. Sawan et al. (2006) found 
that cottonseed oil content, protein content and yield could be increased and oil FFA could be decreased 
with applications of potassium, zinc, and phosphorus. However, FFA differences were not significant at 
95% confidence interval.  
2.3 Oil Degumming 
Vegetable oil degumming is standard for commercial and industrial practices. Gums can cause 
problems during the transportation of oils by settling and forming deposits on tank walls or clogging 
pipelines. In engines where vegetable oil or biodiesel is used, gums or phospholipids can cause catalytic 
converter issues or coking within the engine. Mittelbach (1996) reported that increased phosphorus 
content in biodiesel can result in higher particulate emissions, possibly influencing the effectiveness of 
the catalytic converter. He also suggested that fully refined oils, in theory, should have less phosphorus 
concentrations than non-refined oils and that transesterification can lower phosphorus levels from over 
100 ppm down to 20 ? 30 ppm. Cvengros and Povazanec (1996) stated that cold pressing is ideal for a 
farm level biodiesel operation. There is little phospholipid output and the protein in the cake is not 
denatured, providing a good livestock feed ration supplementation. 
Brekke (1980) summarized several techniques of vegetable oil degumming frequently used by 
practitioners. He stated that there are two types of phosphatides in vegetable oils, hydratable and non-
hydratable. Hot water can be used to remove hydratable phosphatides from oils. Once mixed with water 
these hydratable phosphatides swell to form gels of higher density than oil. They will tend to 
gravitationally separate from the oil and can be removed. However, for best separation of the oil and 
gum layers, centrifugation should be used. Non-hydratable phospholipids may be removed through the 
15 
 
use of reagents such as citric acid, phosphoric acid, and hydrochloric acid, among others. When using 
water to degum vegetable oils, the proper amount was recommended at 75% of the oil?s phosphatide 
content. 
Choukri et al. (2001) experimented with a modified procedure for soft degumming of soybean 
oil. Two wt.% hot water was used to remove hydratable phospholipids from the soybean oil, then a 5% 
aqueous solution of chelating agent (EDTA) and emulsifying agent (sodium dodecyl sulfate, SDS) was 
added. Phosphorus concentration was assessed according to AOCS method Ca 12-55 (2009j). They found 
that dephospholipidation was directly related to the increased concentrations of EDTA or emulsifying 
agent. Differences observed for varying phosphorus concentrated oils were attributed to cation content. 
Higher cation content is thought to decrease the chelating agent?s effects on phosphorus removal.  
Concentrations of EDTA and emulsifying agent higher than 50 mM and 30 mM, respectively removed 
adequate amounts of phosphorus in oils containing 46 ppm initial phosphorus so that it adhered to 
ASTM standard D6751 (2007) in terms of phosphorus concentration (10 ppm). The research did not yield 
favorable results for oils initially containing >180 ppm phosphorus. When a hot water method of 
phospholipid removal was used to remove hydratable phospholipids first followed by EDTA and an 
emulsifying agent, a 10% aqueous solution of 50 mM emulsifying additive removed sufficient amounts 
of phosphorus so that the oil adhered to ASTM D6751 (2007) standard of 10 ppm maximum 
phosphorus. Temperatures greater than 65 oC resulted in best results of phosphorus removal and were 
attributed to the phospholipid structure. Incubation time results were best at 20 minutes. Longer times 
resulted in increased phospholipid concentrations due to scattering of phospholipidic esters as fine 
particles in the oil phase and thereby reducing centrifugation efficiency. Using a minimum of 150 mM of 
EDTA and 50 mM of emulsifying agent, in conjunction with hot water degumming, resulted in less than 5  
ppm phosphorus for tested oils (linseed, soybean, colza, sunflower, and palm); the efficiency was 
independent of initial phosphorus concentration.  
16 
 
Cvengros et al. (1999) used degummed canola oil as an alternative diesel engine fuel. They 
stated that canola oil containing less than 20 ppm phosphorus can be obtained by simple cold pressing 
of oilseeds. They obtained mechanically pressed oil and used a hot water procedure of 0.8 wt% water to 
remove phosphorus. The oil was heated to 80 oC then the water was added and gently stirred for 20 
minutes. In their experiment, cold pressed canola oil contained 14.6 ppm phosphorus while 
coldpressed/extracted oil contained 352 ppm phosphorus. After degumming, phosphorus 
concentrations were 13.4 ppm and 216 ppm, respectively. Bleaching the canola oil with bleaching clay 
resulted in further phosphorus removal down to 5.4 ppm and 186 ppm, respectively. Of particular 
interest, Cvengros et al. (1999) found that a considerable amount of phosphorus goes into the glycerol 
phase of transesterification. Unrefined canola oil containing 14.57 ppm phosphorus, initially, exhibited 
only 3 ppm phosphorus after transesterification. Likewise, refined oil (5.23 ppm phosphorus, initially) 
contained only 0.23 ppm phosphorus after transesterification. If biodiesel feedstock is obtained through 
mechanical pressing, phosphorus concentrations should be relatively low without a degumming 
procedure. However, this level is not as low with pressed/solvent extracted oils. Phosphorus 
concentrations are significantly higher and require intensive refinement to meet ASTM standard D6751 
(2007) for phosphorus concentrations. Once transesterification of the cold pressed vegetable oils is 
conducted, phosphorus concentrations should be well within ASTM D 6751 (2007) limitations, 10 ppm.  
Phospholipids are one parameter attributed to yield losses during transesterification of 
vegetable oils. Du et al. (2004) experimented with transesterification of crude and refined soybean oils. 
Yield losses in biodiesel from crude oil vs. refined oil were attributed to phospholipid concentrations, 
FFA, and water content. Phospholipid content was found to be the most limiting factor in that as 
phospholipid concentration increased, biodiesel yield decreased. While a lipase enzyme (Novozym 435) 
was used for transesterification, Du et al. (2004) found that ester yield was substantially lower for crude 
oil vs. refined oils. Although phosphorus may be removed during transesterification as stated by 
17 
 
Cvengros et al. (1999), there is potential for ester yield losses if not removed before transesterification 
takes place. 
Evans et al. (1974) analyzed phosphorus concentrations in damaged soybeans from the 
Southeast, Midwest, and Southwest U.S. Contrary to Brekke (1980), they found that crude oil from 
damaged beans, on average, contained low levels of phosphorus, less than 500 ppm. While Brekke 
(1980) and Smouse (1995) suggested that damage beans will have higher phosphorus content, in 
particular non-hydratable phospholipids. However, the oil from these beans was solvent extracted 
which Brekke (1980) suggested causes exhibition of high levels of phosphorus as opposed to 
mechanically extracted soybean oil. Given that damaged beans contain less phosphorus than normal 
beans and mechanical extraction releases less phosphorus than solvent extraction, it stands to reason 
that damaged beans, having little value as a food-grade product may hold potential as a biodiesel 
feedstock. But for this scenario to reach fruition, the oil must be mechanically extracted. Oil 
mechanically extracted from damaged beans should exhibit lower phosphorus content as opposed to 
solvent extracted oil from normal soybeans. 
Many researchers report satisfactory results of acid degummed oils. List et al. (1978) is not 
different as they researched the effects of phosphoric acid and water degumming on phosphorus 
removal. They discovered that phosphoric acid pretreatment before water degumming resulted in 
similar phosphorus levels as water degumming alone. With water, phosphorus levels could be lowered 
from 660 ppm to 53 ppm. With a further water wash and bleaching, phosphorus levels could be further 
lowered to 15 ppm. Using phosphoric acid pretreatment with water degumming and water washing and 
bleaching resulted in 3 ppm phosphorus. List et al. (1978) also made an observation that optimal 
oxidative stability of oil was obtained with phosphorus levels ranging from 2 ppm to 20 ppm. 
McDonnell et al. (2000) were concerned with high cost feedstocks of rapeseed methyl esters in 
Europe and experimented with adding rapeseed oil to conventional diesel fuel as a diesel fuel extender. 
18 
 
The rapeseed oil was degummed using acidified hot water and filtration down to 5 microns. Adverse 
results were reported in terms of shorter service intervals when operating equipment with 15% semi-
refined rapeseed oil using their method. However, in direct injected diesel engines, common to most 
agricultural tractors, this diesel fuel extender provides potential. 
Smiles et al. (1988) experimented with six reagents to degum canola, soybean and sunflower 
oils. They determined that citric acid or phosphoric acid, in conjunction with water, were effective at 
removing 91% and 93% phosphorus, respectively. For soybean oils, all reagents except solely water 
performed satisfactorily in removing up to 98% of the phosphorus.  The initial phosphorus concentration 
of canola oil samples was 617 ppm. Water, citric acid, and phosphoric acid lowered phosphorus levels in 
canola oil to 247 ppm, 57 ppm, and 45 ppm, respectively. Soybean oil initially contained 716 ppm 
phosphorus while water, citric acid, and phosphoric acid were effective in reducing phosphorus levels to 
100 ppm, 14 ppm, and 15 ppm, respectively. 
Many reagents have been experimented with as potential degumming tools. Diosady et al. 
(1982) experimented with 54 reagents on canola oil. The oils degummed were samples that had been 
expelled or mechanically pressed, and extracted or solvent extracted. Similar to findings of Cvengros et 
al. (1999), Diosady et al. (1982) found that mechanically pressed oils exhibited less than 30% phosphorus 
to that of solvent extracted oils. Degumming procedures were performed exactly the same for all 54 
reagents with results showing phosphorus level reduction from 508 ppm to 167 ppm, 102 ppm, 35 ppm, 
for water, phosphoric acid and citric acid, respectively. The most effective reagent in terms of 
phosphorus removal was hydrochloric acid resulting in 23 ppm. Diosady et al. (1984) took their results 
from previous research and extrapolated them to a pilot scale plant. They researched optimizing oil 
degumming with citric acid. Oil contact times with the reagent and water were each optimized at 30 
minutes, resulting in 38.2 ppm phosphorus concentrations in a pilot scale degumming process. Results 
were similar to ones obtained through laboratory scale experiments. 
19 
 
Zufarov et al. (2009) experimented with degumming rapeseed and sunflower oils with water, 
acid, and other methods. They found that water degumming reduced phospholipids concentrations 
from 445 ppm to 51.1 ppm in rapeseed oil and from 163 ppm to 44.2 ppm in sunflower oil. They 
concluded that water degumming alone is insufficient in adequate removal of phospholipids. Citric acid 
was also used for phosphorus removal. Zufarov et al. (2009) stated that citric acid will convert non-
hydratable phospholipids to hydratable phospholipids while working as a chelating agent to keep metals 
in a water soluble complex. This method reduced phosphorus from 445 ppm to 8.7 ppm in rapeseed oil 
and from 163 ppm in sunflower oil to 5.2 ppm. 
Like soft degumming, a dry degumming method can potentially prove effective at removing 
phospholipids from vegetable oils. Koris and Vatai (2002) experimented with dry degumming of soybean 
oil through membrane filtration. The molecular weight of triglycerides and phospholipids are 900 g/mol 
and 800 g/mol respectively. Phospholipid molecular size is between the range of ultra-filtration (UF) and 
nano-filtration (NF) so a UF/NF membrane was used in the experiment. They found that 70% to 77% 
phospholipids could be retained on the membrane filter by heating the oil to 40-60 oC pressurizing the 
system to 2-5 bar and having an oil flow of 0.3-0.4 L/m3 in the system. This retention percentage was 
only obtained after adding 5% water to the oil when using a membrane of 0.2 ?m pore size. Apparently, 
the hydratable phospholipids swelled and were able to be retained because of larger particle size. ASTM 
standard D6751 (2007) was not met with membrane filtration of soybean oil in this experiment as all 
membrane filtration tests resulted in greater than 10 ppm phosphorus concentrations. 
2.3.1 Issues with the Degumming Process 
Smouse (1995) suggested that if oils are kept at high temperatures, phospholipids will not be 
easily removed. Cooling crude oil results in easier water degumming properties but prolonged storage 
inhibits degumming effectiveness. The more crude oil is cooled, the longer it can be stored and still 
20 
 
possess excellent water-degumming properties. However, phospholipids have shown to be effective 
antioxidants and chelation agents and will increase the oxidative stability.  
2.3.2 Preparing Seeds for Extrusion 
Some methods of mechanically pressing oilseeds to extract oil suggest that the seed be heated 
to obtain maximum oil extraction. However, Proir et al. (1991) discovered that preheating oilseeds 
before mechanical extraction can increase phosphorus concentrations in canola oil extruded. Cold 
pressed oils contained 13 ppm phosphorus while oil from heated seeds contained 64 ppm phosphorus 
on average. Oil degumming consisting of citric acid followed by hot water reduced phosphorus 
concentrations to 19 ppm in oil from heated seeds.  They also suggested a correlation exists for non-
triglyceride material in oil; as seed heating increased, phosphorus and FFA concentrations increased as 
well. 
Smouse (1995) stated that expansion of seeds or introduction of steam increases the efficiency 
of oil extraction. Likewise, expansion may decrease lipase or phospholipase in soybeans, yielding oil with 
lower free fatty acid and less nonhydratable phospholipids. Likewise, Cvengros and Povazanec (1996) 
state that cold pressing is ideal for a farm level biodiesel operation. There is very little phospholipid 
output and the protein in the cake is not denatured, making it a good source for livestock feed ration 
supplementation. 
2.4 Hydrolysis Issues 
Hydrolysis in oilseeds that threaten the quality of biodiesel produced entails triglyceride 
hydrolysis. When the triglyceride molecule is oxidized, one, two or three free fatty acids are cleaved 
from a glycerol molecule. High FFA levels in vegetable oils, desired for transesterification to biodiesel, 
can prove expensive and intensive to treat and overcome so that a quality product is ultimately 
21 
 
produced. Hydrolysis can happen in oil at any stage of the process but the oil is most susceptible to 
hydrolysis after it is extruded from the seed. 
2.4.1 Seed 
Banks (1998) studied the effects of storage conditions on canola seed hydrolysis. He concluded 
that less than optimal storage conditions for canola can lead to, among others, increased FFA levels. 
Decreased temperatures and moisture content in storage facilities can ensure lower FFA concentrations 
in canola after they have been stored for the desired duration. He found that oil hydrolysis is dependent 
on storage temperature and relative humidity. At a storage temperature of 25 oC and a relative humidity 
of 70%, oil FFA would change by 1% in a 65 week time period. At storage temperatures greater than 30 
oC there would be excessive FFA formation. FFA accumulation would be a direct result of triglyceride 
hydrolysis and is similar to hydrolysis experienced for frying oils. However, hydrolysis in frying oils is 
expedited due to substantially higher temperatures and hydrolysis of canola oil during seed storage at 
temperatures of 30 oC would occur considerably slower. 
2.4.2 Vegetable Oil 
Andersson and Lingnert (1998) researched the influence of oxygen and copper on the oxidation 
of rapeseed oil. They state that one of the most important factors to influence oxidation is metals in the 
oil and copper is most pro-oxidative. Also, storing oils in containers of materials other than glass and 
metals that do not have absolute barriers to oxygen will increase oil oxidation and hydrolysis of 
triglycerides. Similarly, Smouse (1995) indicated that factors affecting oxidative stability in vegetable oils 
include phospholipids, oil storage, fatty acid composition, light, anti oxidants, metals, and seed storage. 
In addition, Banks (1998) and Benjelloun et al. (1991) investigated oxidation effects due to metal traces. 
They demonstrated that metal traces (e.g. copper and iron) increase oxidation by catalyzing reactions 
22 
 
between unsaturated fatty acids and oxygen. Due to rapeseed oil?s fatty acid content, it oxidizes rapidly 
as temperature increases.  The removal of these metals can reduce oxidation. 
In similar research, Rao and Artz (1989) researched the oxidative effects of extrusion 
temperature on corn meal/starch with soybean oil. They found that increasing the extruder 
temperature (115 oC to 175 oC) had decreasing effects on oil stability as well as metal contents, in 
particular iron. Metal contents have been shown to increase oxidation as well. Increased temperature 
tended to increase the back pressure in the extruder die, which suggested the source of the metal 
contents was from the extruder parts.  Likewise, Wang and Johnson (2001) investigated qualities of 
soybean oil resulting from differing process methods. The found that extruded oil resulted in lower FFA 
values than solvent extracted oils. They attributed lower FFA levels to the rapid inactivation of lipases 
during extrusion.  
Kucuk and Caner (2005) researched hydrolysis of sunflower oil as impacted by storage container 
material. Glass and polyethylenterephthalate (PET) were used as storage containers and samples were 
stored with and without exposure to light and air. Oil stored without air exposure and in the dark 
showed the lowest hydrolysis for both materials. However, less hydrolysis occurred in the oil stored in 
glass versus oil stored in PET because oxygen could permeate the PET. The combination of light and air 
exposure, regardless of the storage container material, had significant effects on hydrolysis of sunflower 
oil in this study.  
Malcolmson et al. (1994) studied oxidative stability and shelf life of vegetable oils under 
accelerated storage at temperatures above 30oC. They found that canola oil was more stable under 
lighted storage conditions than cottonseed and soybean oils, but less stable than sunflower oils. Canola 
oil is more prone to autohydrolysis than cottonseed oil at storage temperatures of 40 oC. Similarly, 
Przybylski et al. (1993) researched two varieties of canola oil. The canola oil with lower linolenic acid 
exhibited higher oxidative stability under accelerated storage conditions of 60 oC. For regular storage 
23 
 
conditions at room temperature that are not accelerated with elevated temperature, Martin-Polvillo et 
al. (2004) studied sunflower oils differing in unsaturation degree during long-term storage. They found 
that as the degree of unsaturation of sunflower oil increased, hydrolysis occurred at an increasing rate. 
These results correlate to saturation degree of oils studied by Malcomson et al. (1994).  
If oils are high in unsaturation level, steps can be taken to prolong shelf-life and increase 
oxidative stability. Merrill et al. (2007) investigated oxidative stability of high-oleic (unsaturated) 
vegetable oils with and without antioxidants added. Among oils studied were soybean, corn, sunflower, 
high-oleic sunflower, canola, high-oleic canola, very high-oleic canola. Factors affecting the oxidative 
stability of vegetable oil include the fatty acid composition of the oil, antioxidants, oxygen, light, and 
storage temperature. Natural antioxidants, such as tocopherols, in oils increase oxidative stability. 
Likewise, Warner and Dunlap (2006) reported that extruded soybean oil may exhibit natural 
antioxidants as compared to hexane extracted soybean oils along with having natural ability to inhibit 
hydrolysis.  
2.4.3 Biodiesel 
Bondioli et al. (1995) investigated the storage stability of biodiesel by designing an experiment 
in which samples of biodiesel were stored in the dark, at two temperatures (20 oC and 40 oC), in both 
glass and iron containers with other samples exposed to increasing amounts of water. It was determined 
that, like vegetable oil, storing biodiesel at 20 oC decreases the potential for oxidation over a six month 
period. Even though oxidation was observed for samples stored at 40 oC with moisture exposure, it was 
minimal and the oil remained within technical specification. Biodiesel oxidation was dependent on 
container material and temperature. Storage in iron containers caused secondary oxidation, oxidation as 
a result of primary oxidation. Glass containers proved to be more favorable a storage containers than 
iron containers in terms of oxidative stability. Biodiesel storage in iron containers also had a tendency to 
24 
 
increase emission particulates derived from the storage container, however performance tests showed 
that storage containers had no significant effect on fuel performance in a diesel engine.  
Bouaid et al. (2008) performed an oxidation stability study on biodiesel derived from rapeseed 
oil and bioethanol. They concluded that acid value, peroxide value and viscosity increased with an 
increased storage time and like Bondioli et al. (1995), recommended limiting the storage temperature 
and moisture content to avoid oxidation. Likewise, Dunn (2005) concluded that autooxidation can result 
in increased viscosity, acid value, and peroxide value.    
In a long-term storage study, Mittelbach and Gangl (2001) investigated the stability of rapeseed 
and used frying oil biodiesels under varying conditions. They found that storing biodiesel in closed 
containers in the absence of daylight allowed for less hydrolysis than storing samples open to air and in 
daylight conditions. Likewise, Leung et al. (2006) researched the degradation of rapeseed biodiesel 
under different storage conditions. They found that biodiesel stored at temperatures less than 40 oC 
degraded minimally, while samples stored above 40 oC degraded as much as 40% within a 52 week 
period. They concluded that high temperatures in conjunction with air exposure greatly increased 
biodiesel degradation. Temperature or air exposure alone had little effect on biodiesel degradation. 
Water content in biodiesel contributed to degradation but to a lesser degree than the combination of 
temperature and air exposure.  However, Thompson et al. (1998) studied long-term storage 
deterioration of methyl and ethyl esters derived from rapeseed oil. Samples were placed in glass and 
steel containers both inside and outside. Peroxide and acid values increased over a 24-month period 
with greatest increase experienced from 21 to 24 months.  
Biodiesel does have the potential to oxidize as the research above shows. However, the shelf-
life or storage can be extended and oxidation slowed by the addition of anti-oxidants. Tang et al. (2008) 
found that adding either natural or synthetic antioxidants to biodiesel increased storage life of the 
25 
 
product. In addition, Shahidi and Wanasundara (1994) reported that oxidative stability of canola oil 
treated with natural antioxidants was equivalent or superior to oil treated with synthetic antioxidants. 
2.5 Biodiesel Production 
2.5.1 Transesterification 
There are different methods of producing biodiesel. Some work well for specific feedstocks, 
others work best for high ester conversion efficiencies, while others work best from a cost perspective. 
For the average Alabama farmer wishing to offset fuel expenditures by producing his own biodiesel from 
crops grown on-farm, cost of producing biodiesel is the most limiting factor that may extend his reliance 
on petroleum based products. A farmer who is able to buy petroleum fuel cheaper than he can make his 
own will obviously choose to do so.  
There are two typical methods used in the transesterification process; base-catalyzed 
transesterification and acid-catalyzed transesterificaiton. Base-catalyzed transesterification can be 
conducted with, among other things, catalyst such as sodium hydroxide (NaOH), potassium hydroxide 
(KOH), potassium methoxide (KOCH3), or sodium methoxide (NaOCH3). Acid-catalyzed transesterification 
can be carried out with, among other things, sulfuric acid (H2SO4), sulfonic acid (RSO3H), hydrochloric 
acid (HCl). Both of these methods use alcohols, whether butyl, ethyl, or methyl alcohol. Many 
combinations and procedures at various concentrations exist that utilize these chemicals for biodiesel 
production. These amalgamations are associated with a wide range of costs, some of which may be cost 
prohibitive to farmers. Figure 2.1 illustrates the chemical formula of base catalyzed transesterification 
using methanol as the alcohol. A triglyceride reacts with three moles of alcohol, in the presence of a 
catalyst, to form three moles of esters and one mole of glycerol.  
26 
 
 Figure 2.1. Chemical formula of transesterification. 
 
Moser (2009) stated that biodiesel has many advantages and disadvantages. Among advantages 
are inherent lubricity properties, low toxicity, derivation from a renewable and domestic feedstock, 
superior flash point and biodegradability, negligible sulfur content, and lower exhaust emissions. 
Disadvantages mentioned included high feedstock cost, inferior storage and oxidative stability, lower 
volumetric energy content, inferior low-temperature operability, and potentially higher NOx emissions. 
2.5.1.1 Acid catalysis 
Canakci and Van Gerpen (1999) suggested that biodiesel feedstocks containing high levels of FFA 
are complicated to transesterify with alkaline catalysts and soaps are likely to form. Soaps can prevent 
separation of the biodiesel from the glycerin fraction. They experimented with transesterification of 
food grade soybean oil. They compared the effect of different alcohol types (methanol, ethanol, 2-
propanol, and n-butanol), molar ratios of alcohol to oil (3.3:1, 3.9:1, 6:1, 20:1, and 30:1), sulfuric acid 
catalyst amounts (1%, 3% and 5% by weight), reaction times of 48 and 96 hours,  reaction temperatures 
of 60 oC, 75 oC, 110 oC, and 75 oC for methanol, 2-propanol, 1-butanol, and ethanol, respectively, and 
reaction temperatures of 25 oC, 45 oC, 60 oC with methanol as the alcohol in their study. Summarized 
results of their experiment include: 
27 
 
 A molar ratio of 30:1 and 20:1 resulted in 98.4% and 97% conversion, respectively.   
 Ester conversion increased with temperature increase; 87.8% ester conversion at 60 oC.  
 72.7% to 95% ester conversion was obtained with 1% to 5% sulfuric acid, respectively.  
 96 and 48 hour reaction times resulted in 95% and 87.8% ester conversion, respectively.   
 88%, 93%, 92% and 95% ester conversion efficiencies resulted for methanol, 2-propanol, 1-
butanol, and ethanol, respectively.  
 Higher conversion rates were obtained by using higher reaction temperatures. Longer chain 
alcohols with higher boiling points allowed for the temperature increase. 
 As little as 0.1 wt% water addition lowered ester conversion. 
 More than 0.5 wt% water will lower ester yield to below 90%.  
 5 wt% water lowered ester conversion to 5.6%  
 Base catalyzed transesterification with 3% water resulted in 90% ester conversion.  
 FFA concentration above 5% resulted in less than 90% ester conversion.  
 5% FFA feedstock resulted in a solid soap mixture that prevented the separation of biodiesel 
from the glycerin fraction.  
 
They concluded that factors affecting ester formation included molar ratio, reaction 
temperature, catalyst amount and reaction time. Acid catalyzed transesterification was much slower 
than alkali-catalyzed transesterification. Ester conversion was greatly affected by the molar ratio of 
alcohol to oil with acid catalyzed transesterification requiring larger amounts of alcohol for complete 
reactions. Reaction temperature just below the boiling point of the respective alcohol will result in 
greatest ester yield and ester formation increases with increased acid catalyst amount.  
It is known that acid catalyzed esterification is more time consuming than base catalyzed 
transesterification. Freedman et al. (1984a) performed a similar study to Canakci and Van Gerpen (1999) 
28 
 
comparing molar ratios of 6:1, 20:1, and 30:1 for acid catalysis with methanol and 1% sulfuric acid. 
Ratios of 6:1 and 20:1 provided unsatisfactory conversion after 18 hours of reaction. A 30:1 molar ratio 
produced the highest conversion; previously discovered by Freedman and Pryde (1982). They also 
studied conversion effects as a result of different alcohols at temperatures just below their boiling 
points (60 oC, 75 oC, and 114 oC for methyl, ethyl, and butyl alcohols, respectively) and 1% sulfuric acid 
catalyst. The reaction times required to achieve satisfactory conversion were 2, 22, and 69 hours for 
butyl, ethyl, and methyl alcohols, respectively. Reaction temperature appeared to control conversion 
rate rather than alcohol used; also confirmed by Canakci and Van Gerpen (1999). Reaction temperature 
effects on conversion efficiency were confirmed by performing an experiment with each alcohol at 65 
oC. After 69 hours, all experiments reached satisfactory conversion.  
In instances where FFA is higher than 1.0%, Freedman et al. (1984b), Mbaraka et al. (2003), 
Zhang et al. (2003), Wang et al. (2005) recommended acid pretreatment. This process entails two steps 
where an acid catalyst in conjunction with methanol is first used to lower the FFA value and inhibit soap 
formation from a basic catalyst and FFA. Once the FFA concentration is lowered to within acceptable 
limits, ca. 1.0%, base catalyzed transesterification is used to complete the reaction.  
2.5.1.2 Base catalysis 
In addition to chemical combinations, one of the major variables affecting the biodiesel 
production process is FFA content. Predojevic (2008) found that biodiesel yield was inversely linked to 
acid value (FFA) and viscosity of vegetable oil. It has been reported that FFA concentrations greater than 
3% decrease the ester conversion efficiency (Anggraini et al., 1999; Knothe et al., 2005). Canakci and Van 
Gerpen (1999) reported that FFA levels above 5% can lower ester conversion rate below 90% and that 
high free fatty acid feedstocks transesterified with a basic catalyst will form soaps that inhibit biodiesel 
separation from the glycerin fraction.  However, Kemp (2006) detailed that oil with FFA levels above 1% 
require pretreatment. Some researchers emphasize that in vegetable oil containing more than 1% free 
29 
 
fatty acid, the acid catalyst will be much more effective than the alkali catalyst (Freedman and Pryde 
1982; Freedman et al., 1984a; and Liu, 1994). However, Canakci and Van Gerpen (1999) also stated that 
transesterification with alkaline catalyst was more tolerant of water than with acid catalyst but acid-
catalyzed transesterification is much slower than alkali-catalyzed transesterification. Likewise, 
Ballesteros et al. (1993) reported that base catalyzed transesterification aids in avoiding corrosion 
problems in engine components that are due to the presence of acid traces from acid catalysts. Of the 
scientists reporting experiments using base-catalyzed transesterification, Dorado et al. (2004) used KOH 
because it decreased ester forming reaction time; however, larger amounts of KOH are necessary due to 
is molecular weight. They also recommended basic catalyst to avoid the potential of engine corrosion 
caused by acid catalyst. Experiments were performed with canola oil using KOH and NaOH at room 
temperature. NaOH required up to 14 hour reaction time while KOH required only 1 hour reaction time 
at room temperature and 91.9% ester conversion was achieved using non-erucic canola oil with a FFA 
content of 2.2%.   
Cvengros and Povazanec (1996) stated that the presence of water and high FFA will form soaps 
in a base catalyzed transesterification reaction. Soap formation consumes the catalyst and the reaction 
comes to equilibrium and stops. The glycerol phase was emulsified in the ester phase and separation 
was inhibited. A two step reaction can aid in overcoming this problem. By adding half the amount of 
alcohol and base catalyst, partial conversion was attained with less soap formation. The second step, 
adding the other half of alcohol and catalyst, completed the reaction with less susceptibility to reaction 
equilibrium.  
2.5.1.3 Two Step Reactions 
Kucek et al. (2007) investigated the possibility of two stage reaction. After two different single 
stage reactions with 12:1 ethanol molar ratio, 30 oC reaction temperature, 0.3 wt% NaOH and 1.0 wt% 
KOH, a second stage was added. Half the catalyst and molar ratio of ethanol were used for the second 
30 
 
stage as compared to the first due to lower amounts of unreacted glycerides. With NaOH, ester yield 
dropped from 97.2% to 96.3% and with KOH, ester yield dropped from 95.6% to 94.6%, however these 
yield losses were not significant. It appeared that no advantage was gained as a result of a second stage 
reaction.   
The table in Appendix A.1 outlines varying biodiesel production techniques used in 
experimentation by different researchers. Some of the reported biodiesel yields were measured 
intermittently during the transesterification process and may not be the final ester yield obtained for 
the reaction. However, each provided valuable insight to better understand ester formation and the 
necessary procedures required to obtain satisfactory conversion. 
2.5.2 Influences on Transesterification 
2.5.2.1 Catalyst 
A catalyst in the transesterification reaction is neither a product nor input of the reaction, but is 
meant to facilitate the reaction and push it to completion. Base catalysts in the transesterification 
reaction also serve a dual purpose as they neutralize FFA that otherwise may impede the reaction. 
Feuge and Gros (1949) and Lehman and Gauglitz (1966) found a base catalyst also has the potential to 
influence glycerol yield from the biodiesel, which in turn, dictated ester conversion; tying the catalyst 
back to is fundamental purpose. Results from their study indicated that glycerol yield, when 
transesterifying peanut oil, was 77% and 95% for 0.2 wt.% and 0.8 wt.% sodium hydroxide, respectively 
with a 6:1 molar ratio of methanol to oil. This catalyst amount is lower than the generally accepted 
amount of one percent by weight used by most researchers and illustrated the necessity for higher 
catalyst amount recommendations.  
Kucek et al. (2007) investigated the differences in NaOH and KOH as catalysts in transesterifying 
soybean oil with a molar ratio of ethanol to oil of 12:1, reaction temperature of 70 oC, and reaction time 
31 
 
of one hour. More KOH (40%) was used in comparison to NaOH to account for difference in molar mass. 
KOH of 0.42 wt% led to no phase separation, however at 1.4 wt.% KOH yield losses were apparent 
stemming from soap formation. One wt.% KOH was then used. It was determined that KOH resulted in 
lower ester yields as opposed to NaOH and was attributed to moisture in KOH. 
Singh et al. (2006) investigated process optimization of biodiesel production using alkaline 
catalysts with canola oil and methanol. They studied, sodium hydroxide, potassium hydroxide, sodium 
methoxide, and potassium methoxide at molar ratios to canola oil of 0.1, 0.2, and 0.3 in order to 
develop an optimization model. The reaction temperature was varied from 40 ? 60 oC and the reaction 
time was 10 minutes. They determined that potassium based catalysts gave better ester yields than 
sodium based catalysts and methoxide yields gave highest conversions. Highest conversion efficiencies 
were attributed to the fact that methoxide was theoretically free of water. In the formation of 
methoxide from methanol and hydroxide when mixed simultaneously with oil feedstock, some water 
was formed which consumed a portion of the catalyst. However, more potassium based catalyst was 
required for the reaction because of its elevated molecular weight as compared to sodium based 
catalyst and potassium based catalysts have greater tendency to form soap. They also stated that strong 
emphasis should be placed on the catalyst molar ratio to feedstock as molecular weight varies among 
alkali catalysts; most researchers use weight percentage, typically 1 wt.%, when transesterifying oil 
feedstocks with alkali catalysts. Their optimization model resulted in 99% conversion using 1.59 wt.% 
KOCH3, 4.5 molar ratio of methanol to canola oil, 50 oC reaction temperature and 10 minute reaction 
time. The model had an R2 value of 0.85 and when tested resulted in 95.8% ester yield.  
2.5.2.2 Water 
Wright et al. (1944) found that as little as 0.3% water could reduce ester conversion by 
consuming the catalyst necessary for the reaction. Bradshaw and Meuly (1944), Bradshaw (1942), and 
Feuge and Gros (1949) reasoned that limiting water content to less than 0.5% is imperative for 
32 
 
satisfactory ester conversion. Likewise, Canakci and Van Gerpen (1999) reported that ester formation 
was greatly retarded by the presence of water. Less than 0.5% water was recommended for best 
conversion. FFA in vegetable oil can have a significant impact on transesterification. If base catalyst was 
used the water formed as a result of the base catalyst introduction to the FFA can inhibit the reaction as 
well as soaps formed. FFA concentrations greater than 5% resulted in ester yields less than 90%. 
Similarly, Freedman et al. (1984a) stressed that when using sodium methoxide or hydroxide as a 
transesterification catalyst, both should be kept in an anhydrous form. Prolonged contact with air will 
diminish the effectiveness of these catalysts through interaction with moisture and carbon dioxide and 
Kucek et al. (2007) attributed low ester conversion to water in KOH catalyst noting that NaOH was less 
susceptible to water than KOH.  
2.5.2.3 Alcohol 
Another important variable of biodiesel production affecting the yield of esters is the molar ratio 
of alcohol to vegetable oil employed (Freedman and Pryde, 1982; Canakci and Van Gerpen, 1999). In 
acid-catalyzed transesterification, a higher molar ratio of alcohol to oil is required than that of alkali-
catalyzed. The most frequently used alcohols are butyl, methyl, and ethyl alcohols at molar ratios 
ranging from 3.3:1 to 30:1. These alcohols vary in price and performance characteristics. Bradshaw and 
Meuly (1944) and Bradshaw (1942) stated that a 4.8:1 molar ratio of alcohol to oil resulted in 98% 
conversion and if added in three or four portions, the molar ratio could be reduced to 3.3:1 with similar 
results. Bradshaw and Meuly (1944), Bradshaw (1942), and Freedman and Pryde (1982) also found that 
molar ratios greater than 5.25:1 and 6:1, respectively, interfered with gravity separation of the glycerol 
and added unnecessary cost. Higher conversion rates were found for the longer chain alcohols as 
compared with methyl ester and are probably due to the higher reaction temperatures allowed by their 
higher boiling points. This effect apparently dominated any decrease in reaction rate associated with the 
longer chain alcohols. Butyl alcohol exhibited the shortest reaction time in the biodiesel production 
33 
 
process as compared to ethyl and methyl alcohol because higher temperatures were used but after one 
hour, the conversions were about the same.  
Dorado et al. (2004) suggested that transesterification of canola oil can be performed with 1.4% 
KOH, 4.6:1 molar ration of methanol to oil at 20-45 oC and 30 minutes of stirring. They suggested that a 
greater amount of KOH reduced the amount of required methanol. However, there was an optimal level 
of each constituent, if lower or higher concentrations of methanol or KOH were used, soap formation or 
incomplete reaction occurred. Therefore, they recommended lower amounts of methanol be used 
compared to other researchers but this reduction was due to higher concentration of KOH in the 
reaction. However, methanol amounts of 20 wt.% made glycerol separation difficult, thus decreasing 
ester yield. They also discovered that methanol amounts up to 10% produced two layers, a lower 
gelatinous layer and an upper opaque layer. This amount of methanol was insufficient to perform 
complete transesterification. 
The generally accepted molar ratio of alcohol to oil is 6:1. Freedman et al. (1984a) found that a 
this molar ratio of methanol to oil resulted in 98% ester conversion at a 60 oC reaction temperature, one 
hour reaction time, and 0.5% sodium methoxide catalyst. At 3:1 molar ratio, the ester conversion was 
reduced to 82%. High ester conversion (93% - 98%) was observed for soybean, sunflower, peanut, and 
cottonseed oils at 6:1 molar ratio of methanol to oil. They also experimented with ethanol, butanol, and 
methanol at molar ratios of 6:1 and 3:1 at temperatures just below the boiling points of each respective 
alcohol and 0.5 wt.% sodium methoxide. All ester conversion at 6:1 molar ratio was similar after one 
hour (96% - 98%). For 3:1 molar ratio, butyl alcohol reaction was at 88%, while ethyl and methyl were at 
81% and 82%, respectively. Excess alcohol was needed for satisfactory reaction.  
Kucek et al. (2007) experimented with ethanolysis of soybean oil. The experiment was 
conducted with molar ratios of ethanol to oil of 6:1 and 12:1, NaOH concentrations of 0.3 wt% and 1.0 
wt%, and reaction temperatures of 30 oC and 70 oC. No phase separation was observed at a molar ratio 
34 
 
of 6:1, NaOH of 0.3 wt% and reaction temperatures of 30 oC or 70 oC, due to the low alcohol and catalyst 
content. Highest ester yields in a single phase reaction, 96.8% and 97.2%, were observed at 12:1 molar 
ratio and 0.3 wt% catalyst at, 30 oC and 70 oC, respectively. Ester yields beyond 98% were achieved with 
a two stage reaction.  
2.5.2.4 Reaction Temperature  
Reaction temperature can dictate the reaction duration according to Canakci and VanGerpen 
(1999) with optimal temperatures ranging a few degrees below the boiling point of the respective 
alcohol used for shortest reaction times. Dorado et al. (2004) found that transesterification of canola oil 
with 1.4% KOH and 16 vol.% methanol was achieved at room temperature after one hour, while 
transesterification with NaOH took up to 14 hours to complete at room temperature. However, Trent 
(1945) found that reaction temperatures greater than 60 oC should be avoided because they tended to 
accelerate saponification of glycerides by basic catalysts. 
Freedman et al. (1984a) found that transesterifying cottonseed, peanut, soybean, and sunflower 
oils with either sodium hydroxide or methoxide with 6:1 molar ratio of methanol, ethanol, or butanol to 
oil and a reaction temperature of 60 oC resulted in complete conversion after 1 hour. At a moderate 
temperature of 32 oC, oils were transesterified after 4 hours, slightly exceeding the conversion at 45 oC 
and 60 oC.  Freedman et al. (1984a) and Canakci and Van Gerpen (1999) determined that best 
conversion was achieved at reaction temperatures just below the respective alcohol?s boiling point. 
2.5.2.5 Reaction Time 
Dorado et al. (2004) found that transesterification of canola oil with 1.4% KOH and 16 vol.% 
methanol was achieved at room temperature after one hour, while transesterification with NaOH took 
up to 14 hours to complete at room temperature. Similarly, Freedman et al. (1984a) found that at a 
molar ratio of 6:1 methanol to oil, 80% of the oil was converted to ester after only one minute of 
35 
 
reaction for soybean and sunflower oils. However, under the same conditions, only 55% of peanut and 
cottonseed oil were converted to ester. After one hour, the conversion efficiency for all four oils was 
90% or higher with cottonseed oil being the lowest at 90%.  On the other hand, after only 10 to 15 
minutes, Canakci and Van Gerpen (1999) suggested that ester conversion is near completion with basic 
catalysts and various alcohols. When using acid catalysts, reactions may take up to 48 to 96 hours for 
satisfactory conversion. Most published biodiesel production literature recommended one hour reaction 
time for base catalyzed transesterification reactions. 
2.5.2.6 Glycerol Settling Time 
Dorado et al. (2004) found that a settling time of 1-3 hours was sufficient to achieve satisfactory 
phase separation of glycerol and methyl ester at a temperature of 38 oC. When the temperature was 
lowered by 10 oC to 15 oC, several days were required to achieve satisfactory phase separation. In the 
event settling temperature and time was low and took several days, respectively, the opaque ester 
samples turned crystalline and a slight glycerol lower phase appeared. They suggested that glycerol was 
more soluble at lower temperatures and does not fully separate.  
2.5.3 Refining Crude Biodiesel 
For a quality end product, biodiesel must be refined to remove impurities and excess catalyst 
and glycerol. Washing is most often used in published literature as a means to refine biodiesel and can 
be performed with water (water washing or wet washing) or a resin (dry washing). However, Kucek et al. 
(2007) used Magnesol?, a commercial adsorbent, to purify biodiesel. It was a synthetic amorphous 
magnesium silicate that removes contaminants such as water, soaps, free glycerin, and unreacted 
glycerides. They found that two wt% Magnesol? eliminated the need for water washing and generated a 
product with low unreacted glycerides. The only drawback; Magnesol? was not recyclable and 
36 
 
procedures for its disposal were unclear. However, it virtually eliminated the usage of water and water 
washing in the biodiesel production process. 
Predojevic (2008) experimented with purifying biodiesel with different products. He used silica 
gel, 5% phosphoric acid, and hot distilled water and a two step alkali transesterification reaction with 
waste sunflower oil.  For the silica gel purification, a column was designed and silica gel was added (3 g). 
A top layer of anhydrous sodium sulfate was added to remove any traces of water (3.14 cm3). 
Purification of 200 g of methyl ester with 5% phosphoric acid was performed by washing the crude 
biodiesel with 50 cm3 of a 5% phosphoric acid/water solution up to 7 times until reaching a neutral pH 
after which the biodiesel was dried with sodium sulfate. Hot distilled water was used in a third 
purification experiment. 50 cm3 of water was used to wash the biodiesel up to 10 times after which the 
biodiesel was dried with sodium sulfate. The results indicated that silica gel and phosphoric acid gave 
higher ester yields than hot water washing. However, the purity of biodiesel using all three prescriptions 
was similar. Lower yields due to water washing were attributed to the number of washings required (10) 
to reach neutral pH.   
2.6 Economic Analysis 
Economics of the biodiesel production process can be studied from several angles. Obviously the 
financial element is important from a small-scale production standpoint, but net energy return of 
biodiesel compared to other fuels is also a parameter of significant interest. Cvengros and Povazanec 
(1996) stated that the energetic output of rapeseed methyl ester ranged from 2.5:1 to 3.5:1, while the 
energetic output for ethanol is only 1.63:1. However, net energy production of one hectare of rapeseed 
crop is 76 GJ for methyl ester and 160 GJ for ethanol. They also advocated for a small production plant in 
which a farmer can take his crop. The crop is converted into biodiesel and cake and returned to the 
farmer for use as fuel and livestock feed supplementation, respectively. The advantage of this process, 
37 
 
according to Cvengros and Povazanec (1996), was that continuous production was not required, it was 
performed on a supply basis. This system was a low cost investment, allowing for rapid implementation, 
and cost savings on transportation. In contrast, a large production system is better equipped to control 
fuel quality, production quality, and byproduct employment. According to Cvengros and Povazanec 
(1996), alternative fuels, such as rapeseed methyl ester, reduce environmental impacts of 
transportation, reduce the dependence on crude oil imports and ease political and economic factors. 
Rapeseed based biodiesel offered business possibilities to agricultural enterprises for periods of excess 
agricultural production, they allowed economic uses of devastated soils, and were a low-waste 
technology generating no by-products with environmental impact. However, Paulson and Ginder (2007) 
and Reka-Schill (2008) stated that feedstock acquisition currently accounts for over 80% of biodiesel 
production expenses, resulting in a serious threat to the economic viability of the biodiesel industry. 
Moser (2009) reported that desirable characteristics of alternative oilseed feedstocks for 
biodiesel production included adaptability to local growing conditions (rainfall, soil type, latitude, etc), 
regional availability, high oil content, favorable fatty acid composition, compatibility with existing farm 
infrastructure, low agricultural inputs (water, fertilizer, pesticides), definable growth season and uniform 
seed maturation rates. These desirable characteristics would also include potential markets for 
agricultural by-products, and the ability to grow on agriculturally undesirable lands, and/or in the off-
season from conventional commodity crops. Biodiesel fuels prepared from feedstocks that meet at least 
a majority of the above criteria would hold the most promise as alternatives to petrodiesel.  
A major destabilizing factor that producers faced in 2007 which led to an unstable market was 
biodiesel feedstock prices (soybeans) (Crooks, 2008). He indicated that soybean oil prices were only 44 
cents per kilogram not long ago but endured a 160% price increase from 2005 to 2007. This price 
increase was attributed to decreased soybean production acres as a result of increased demand for 
corn. Many biodiesel production facilities have closed and are for sale. The ones that are operational are 
38 
 
only at 20% capacity. However, biodiesel exports were at an all-time high with 80% of biodiesel 
produced in the US being exported.  
Crooks and Dunn (2006) stated that although great interest exists for renewable fuels, it is 
unlikely that there will be a shift toward production in the southern US in the near future. As of 2008, 
there existed a small agri-biodiesel producer tax credit which subsidized small-scale, on-farm biodiesel 
production at 2.6 cents per liter. This credit is available to producers for their first 57 million liters per 
year produced but not applicable when producing more than 227 million liters per year. They also 
mentioned that there existed hope of biodiesel becoming a preferred lubricity solution to the EPA?s 
regulation of ultra low sulfur petrodiesel which requires lubricity additives to overcome the absence of 
sulfur.  
Increased employment and value-added economic activity could arise as a result of biodiesel 
plants established in rural economies (Gustafson, 2003). Biodiesel plants that utilize minor oil crops as 
their feedstock source could yield comparable increases in economic activity. Economic feasibility in 
terms of investment costs were around $0.26 per liter but increase proportionally with plant capacity 
because ease of production limits economies of scale. Costs of production are decreasing due to 
increased plant efficiency and conversion but still pale in comparison to $0.05 to $0.13 per liter 
production costs of petrodiesel. In conclusion, he stated that production costs, like ethanol, were 
expected to decrease as a result of increased efficiency and innovations in production techniques. 
Favorability may also lie in consumers who wish to purchase biodiesel for its environmental benefits and 
potential to lessen strains on climate change. However, these consumers may typically be in more 
densely populated areas where feedstock production was displaced requiring transportation of the 
biodiesel and increased overall cost.  
Kenkel and Holcomb (2006) stated that geographic concentration for ethanol plants in the mid-
west has been driven by feedstock availability and the lack of need for transportation when considering 
39 
 
ethanol production. Similarly, biodiesel plants have been located near crushing facilities in soybean-rich 
areas. Van Dyne and Raymer (1992) are in agreement as they see largest production potential of 
biodiesel with integrated systems in grain or feed handling and processing facilities.  But, market forces 
are lessening the need for these geographic restraints. The implementation of train transport of 
feedstocks in conjunction with lower utility costs and plants utilizing co-products of the 
transesterification process are negating the need for biodiesel production plants to be located in 
traditional venues and newly built plants in alternative locations are beginning to experience close 
competition with traditionally located biodiesel production plants. Kenkel and Holcomb (2006) also 
rationalized that plant locations near co-product utilization facilities were favorable. The most common 
co-product from the transesterification process is oilseed meal. The oilseed meal accounted for 60% to 
80% of the initial oilseeds used depending upon the feedstock. These meals were highly desired for both 
ruminant and non-ruminant livestock protein sources in feed rations. By products directly from 
transesterification, namely glycerin, are highly valued for soap production and also development of films 
and casing materials. Favorably for biodiesel plants, as opposed to ethanol plants, was the relatively low 
consumption of utilities such as electricity and natural gas. However, this advantage is negated when 
considering the vast amount of utilities requirement for the oilseed crushing process.  
Kenkel and Holcomb (2006) also investigated economics of on-farm or small-scale oilseed 
processing and biodiesel production. Table 2.1 highlights their research of breakeven oilseed costs at 
various biodiesel prices. They concluded that small-scale crushing and biodiesel production was 
marginally feasible and that on-farm biodiesel production was not feasible at current prices. However, 
the pricing structure used in their research was that of 2005-2006 actual costs a farmer would incur. 
Returns were sensitive to the farm level price of commodities, biodiesel, and meal value. On-farm 
processing of oilseed higher in oil content (canola, sunflower) was marginally feasible, but feasibility of 
soybean based biodiesel required a biodiesel value above $1.32/liter with historically high meal prices.   
40 
 
Table 2.1. Breakeven oilseed crop values at various biodiesel prices. (Kenkel and Holcomb, 
unpublished data). 
Biodiesel Price $2.50 $2.75 $3.00 $3.25 $3.50 
Breakeven Canola Price ($/kg) $0.181 $0.214 $0.249 $0.287 $0.322 
Breakeven Sunflower Price ($/kg) $0.163 $0.201 $0.238 $0.276 $0.315 
Breakeven Soybean Price ($/kg) $0.195 $0.221 $0.246 $0.272 $0.298 
  
In potential opposition to biofuel production plants, commodity groups in grain-deficit areas 
fear that increased production costs and decreased production of their respective crops may result. The 
Oklahoma Wheat Growers? Association opposed a canola processing/biodiesel plant in OK because it 
had the potential to decrease wheat acres. The fact that cropping shifts or experimental crops for 
bioenergy production often incur lag time in adoption and production of sufficient crop acreage, making 
it extremely difficult to justify bioenergy production plants in some areas with so much inherent 
uncertainty.  It is difficult to establish a plant without sufficient production of the needed feedstock(s); 
likewise it is also hard to justify cropping changes without a production plant or subsequent market for 
the crop.  
Because the price of soybean oil, prior to transesterification, is higher than petrodiesel, Raneses 
et al. (1999) found that biodiesel had not penetrated the petrodiesel market in the US. One possible 
solution they suggested was similar to Crooks and Dunn (2006), in which biodiesel can be used as a fuel 
additive in concentrations of 1% to 3% by volume to enhance fuel performance. They suggest that 
petrodiesel fuel additives currently used cost more than biodiesel but biodiesel out performs these 
additives in terms of cetane number and lubricity and the real market potential of biodiesel may lie in 
this sector.  
Van Dyne and Raymer (1992) made a statement that biodiesel feedstock can possibly be grown  
on land that the US government currently holds idle annually to normalized supply and demand of food 
crops. In the southeast region of the US, there are a total of 3.5 million hectares held idle annually and 
some acreage is the result of a 10-year Conservation Reserve Program (CRP). Large volumes of biodiesel 
41 
 
could be produced from this land. These researchers investigated the potential of growing canola on this 
land and estimated that an average of 2.4 billion liters of biodiesel could be produced annually and if 
maximum yields were experienced, 3.2 billion liters could be produced in one year.  
Using several methods and several feedstock avenues, Zhang et al. (2003) conducted a 
sensitivity and economic feasibility analysis of biodiesel production. They concluded that alkali-catalyzed 
transesterification from virgin oil had the lowest capital investment because of relatively small 
infrastructure requirements, but had the highest manufacturing cost. Likewise, virgin oils were higher 
priced than waste vegetables oils (WVO) previously used in frying. Zhang et al. (2003) found that the 
pretreatment of WVO in a base catalyzed reaction negated the savings from the low cost oil itself. They 
determined that acid-catalyzed transesterification was the best option in terms of economics and the 
glycerin co-product could offset production costs by as much as 10% annually. Sensitivity analysis 
showed that plant capacity, price of WVO, and price of biodiesel were major factors affecting the 
economic feasibility of production. 
Paulson and Ginder (2007) stated that rapid growth in the biodiesel industry caused many 
recent studies to become obsolete within months of plant completion. The estimates these researchers 
studied, 115 million and 230 million liter per year plants, differed by 0.37 cents per liter of production 
cost with biodiesel from the 230 million liter per year plants costing less per liter to produce. These 
savings came from marginal labor costs and capital costs. They speculated that increasing returns to 
scale may provide larger scale plants with an advantage in terms of bidding for scarce high-quality virgin 
feedstocks. Since almost 85% of the total operating expenses were tied up in feedstock, this bidding 
advantage established whether a plant continued to operate or shut down. Smaller plants with variable 
operating costs would be forced to shut down in these scenarios. However, in 2007 there were no plants 
operating at 230 million liters per year capacity and the authors stressed that these theoretical 
estimates are extrapolated from real data for a 115 million liter per year plant and need to be 
42 
 
interpreted with caution. The researchers also stress that the quality of biodiesel produced from non-
virgin feedstocks can be highly variable and costs can range widely making profit margins difficult to 
ascertain for these type plants.   
2.7 Effects of Microorganisms in Biodiesel Storage Tanks 
 Whether petroleum-based fuel or bio-based fuel is stored long term, contamination from water 
is a major issue in storage tanks and can potentially lead to microbial growth. With petroleum-based 
fuel, water contamination is minor as petroleum fuel has little affinity for water. Van Gerpen et al. 
(1996) found that biodiesel has a water saturation level of approximately 1,500 ppm, nearly 40 times 
higher than petroleum diesel. When water is introduced to a storage tank through vents, seals, openings 
in the tank, or from humidity in the atmosphere, it collected in the bottom of the tank upon saturation 
of the fuel. ASTM standard D 6751 (2007) requires that biodiesel not contain more than 500 ppm 
dissolved water, but the introduction of water during transport and storage is almost inevitable and 
biodiesel will absorb 1,200 ppm to 1,500 ppm. However, because this amount of water will be bound to 
the biodiesel and is not free water, it is not available for microbial growth. Because of the water 
saturation limit of biodiesel, any additional water in the tank will collect in the bottom of the tank and 
can potentially lead to microbial growth.  Water collection at the bottom of the storage tank, coupled 
with biodiesel?s biodegradability, allows for microbial growth if good ?house-keeping? practices are not 
followed. Biodiesel has increased bioavailability to microorganisms such as strains of Pseudomonas as 
compared to petroleum diesel. In terms of fuel spills, degradation of biodiesel from microbial growth 
may have a positive impact on the environment as compared to petroleum diesel but a negative impact 
on fuel stability during storage.  
Van Gerpen et al. (1996) researched treatment methods for reducing microbial growth and 
found they can include periodic removal of water that collects in the bottom of storage tanks, transport 
43 
 
and storage of biodiesel in separate infrastructure from petroleum diesel that is completely sealed, or 
the addition of biocides. However, biocides can have an adverse effect on the biodegradability of 
biodiesel. Van Gerpen et al. (1996) also recommended that biocides should be added to biodiesel not 
used within a short period of time to inhibit mold growth in the storage tank.  
2.7.1 Biodiesel Storage 
2.7.1.1 Storage Temperature 
Biodiesel is more susceptible to microbial growth because of its biodegradability as compared to 
petroleum diesel. A study by Williamson and Badr (1998) showed that biodiesel degraded over 98% 
biologically within three weeks while petroleum diesel degraded only 50% during the same time period. 
One component effecting degradation of biodiesel is storage temperature. Leung et al. (2006) reported 
that storage temperatures of 40 oC had the largest degradation impact on biodiesel (Figure 2.2). This 
degradation was especially evident when biodiesel was stored in an unsealed container. Biodiesel stored 
at lower temperatures in sealed containers, whether water was present not, showed the lowest 
degradation rates. However, there existed little differences among samples stored in sealed conditions, 
irrespective of temperature. They concluded that high temperature, coupled with air exposure, led to 
increased degradation rates of biodiesel due to oxidation.  
2.7.1.2 Water Contamination in Biodiesel 
 Once a vegetable oil or animal fat feedstock has been transesterified, it must be purified. 
Purification of biodiesel can be performed in several ways and is necessary to remove excess glycerol, 
unused catalyst, and soaps that may have formed during transesterification. Water-wash purification is 
one technique sometimes used that entails spraying water through the biodiesel. Because water is 
denser than biodiesel, it settles to the bottom of the washing vessel and can then drained off but some 
water may be retained in the biodiesel. Biodiesel is hydrophilic indicating its ability to absorb water it 
44 
 
may come in contact with. According to Van Gerpen et al. (1996), biodiesel has the ability to absorb up 
to 1,500 ppm of water. ASTM standard D 6751 (2007) outlines that biodiesel contain no more than 500 
ppm water. This method will require further energy input to ?dry? the biodiesel in order to meet ASTM 
standard D 6751 (2007). If biodiesel is water-washed and not dried, there exists potential for it to 
contain excessive amounts of water and even free water. Upon storage, free water will collect in the 
bottom of the vessel and introduce an environment for microbial growth.  
 
Figure 2.2. Purities of biodiesel degradation at different temperatures.   
(i) sealed, (ii) with air exposure, (iii) sealed with water presence, (iv) with air exposure and water 
presence. (?) 0 ?C; (?) 20 ?C; (?) 40 ?C. (Leung et al. 2006). 
 
Further, biodiesel is hygroscopic in nature and can absorb moisture from the air. Once biodiesel 
has reached saturation capacity from water washing, moisture from the air or contact with water from 
another source, excess water above 1,500 ppm will accumulate in the bottom of the storage tank due to 
its dense nature compared to biodiesel. Therefore, water can be present in biodiesel storage tanks in 
two forms, free water or dissolved water. Dissolved water in biodiesel is not readily available for 
microbial growth, however free water existing in a biodiesel storage tanks provides a conducive 
45 
 
environment for microorganism growth. On the other hand, biodiesel may be dry-washed with resins 
such as AMBERLITE? BD10DRY?. Dry washing with a resin provides a ?water free? process thereby 
reducing the potential of initial water contamination in a storage tank. 
In addition to storage temperature affecting biodiesel degradation, it can also affect water 
solubility into biodiesel. Van Gerpen et al. (1996) studied water solubility as a result of different 
biodiesel and petroleum diesel temperatures. At 25 oC, they found that 60 ppm of water will be 
absorbed into petroleum diesel fuel. The water saturation level for biodiesel was observed to be 40 
times greater, or approximately 1500 ppm, than petroleum diesel. Table 2.2 illustrates these findings. 
Likewise, efforts by He et al. (2007) recommended underground storage tanks for biodiesel as they 
maintained a relatively constant temperature that lessens temperature effects on moisture absorption. 
Figure 2.3 illustrates findings of He et al. (2007) for biodiesel and petroleum diesel (D2) saturation levels 
at different temperatures. They concluded that no significant differences existed for biodiesel from 
different feedstocks tested: soybean, canola and mustard. Of interest, they discovered that as 
temperature increased, moisture content increased at a rate of 22.2 ppm/oC. They speculated that 
biodiesel absorbs moisture as the temperature rises, then the moisture precipitates as the temperature 
falls. If the temperature in the storage tank oscillates, water accumulation in the tank bottom can result 
and provide sufficient media for microbial growth. Burkhalter (1976) studied similar storage effects on 
oil and agreed with these findings, suggesting that tank sweating caused moisture to drip into the oil 
and resulted in an effective degumming process. However, unwanted solids did accumulate in the tank 
bottom. This would most likely not be the case with biodiesel, but a water washing effect may take 
place. In the event biodiesel was not previously washed effectively, unwanted glycerol, soaps, etc may 
accumulate in the tank bottom. 
 
 
46 
 
Table 2.2. Water contents (ppm) in various fuels and blends after vigorous mixing with water 
for various periods at 25 oC. (Van Gerpen et al., 1996) 
 0 HR 1 HR 3 HR 18 HR 1 D 5 D 
Methyl soyate (PV=124, Interchem) 0 0 0 0 - - 
Methyl soyate (PV=20) 37 c 1460 a 1595 a - 1255 b 1225 b 
20% Blend (PV=20) 40 a 37 a 38 a - 33a 45 a 
Diesel fuel (No. 2) 28 a - 81 a 30 a - - 
*Values are the average of duplicate measurements. 
Values with different superscript in the same row are significantly different at ?=0.05. 
 
Water can enter a storage tank through vents, seals, or openings. It may be necessary to avoid 
using current petroleum diesel storage infrastructure to ensure biodiesel is kept free of water. On the 
other hand, good house-keeping measures can be taken to reduce water contamination and thereby the 
potential for microbial growth. Periodic draining from the bottom of the tank will ensure free water is 
removed. Sealing the tank and replacing degraded fixtures may aid in preventing moisture from seeping 
into the tank.  
Figure 2.3. Saturation MC of biodiesel and diesel at three temperatures.  
(He et al., 2007) 
In addition to engine corrosion problems, water in biodiesel can facilitate microbial growth. Van 
Gerpen et al. (1996) found that species of yeast, fungi, and bacteria can grow at the interface between 
biodiesel and free water in a storage tank. These organisms can produce sludge and slimes that can 
47 
 
cause fuel filter plugging. Some organisms can even convert sulfur in fuel to sulfuric acid resulting in 
corrosion of metal storage tanks although ASTM standard D 6751 (2007) states that no more than 500 
ppm sulfur is allowed in biodiesel.  
2.7.2 Microbial Growth 
In the same study by Van Gerpen et al. (1996) outlined above, a microbial growth experiment 
was conducted. Samples of methyl soyate and petroleum diesel fuel were tested with various nitrogen 
sources (Yeast N-base and Pseudomonas basal mineral N-base). Carbon sources used were No. 2 diesel 
fuel and methyl soyate with microbial growth compared. The culture media was incubated in the dark at 
room temperature while microbial growth was assessed using a spectrophotometer testing for optical 
density. The yeast in a 1% biodiesel sample showed no significant growth until day 6, after which visible 
mold was detected. However, the 1% petroleum diesel fuel showed signs of mold growth only 3 days 
after inoculation. Pseudomonas produced growth in biodiesel after only 2 days and development in 
petroleum diesel after 3 days. The cultures from biodiesel and petroleum diesel contained different 
morphologies indicating different types of microbial growth. However, two cultures were the same 
morphology for both biodiesel and petroleum diesel. Table 2.3 highlights results from this study and it 
was evident that Pseudomonas grew approximately 70 times faster in biodiesel than petroleum diesel 
within 6 days.  
Short to medium length hydrocarbons were greatly affected by strains of Pseudomonas 
aeruginosa, Bacillus sp. and Micrococcus in comparison to longer chains. Ghazali et al. (2004) and Lazar 
et al. (1999) reported that these strains indicated effectiveness in degrading hydrocarbons. However, 
Schleicher et al. (2009) reported that blends of petroleum diesel and biodiesel showed strong 
hydrocarbon degradation. B20 exhibited strongest microbial growth as compared to B5 and B100. 
Microbial growth experienced in B20 was 1700 times higher after 84 days of incubation than at the 
commencement of the experiment. However, B100 and B5 blends only experienced microbial growth of 
48 
 
13 and 20 times higher than initial measurements, respectively. Nonetheless, microorganism population 
was dependent on the concentration of petroleum diesel and was highest for B5 at 80%. Under 
anaerobic conditions, microorganisms in B100 grew better at 37 oC as compared to B5 and B20 blends in 
which microorganisms grew better in aerobic conditions. Highest levels of sedimentation as a result of 
microbial activity were experienced in B20. Less was measured in B5 and no sediment formed in B100.   
Table 2.3. The growth of  microorganisms on various diesel and methyl soyate liquid media.  
(Van Gerpen et al., 1996). 
 Absorbance 
Liquid media 1 day 2 days 3 days 6 days 
Yeast N + methyl ester 0.016 0.023 0.030 0.009 
Yeast N + diesel 0.042 0.067 0.250 0.448 
Pseu N + methyl ester 0.057 2.004 4.700 10.240 
Pseu N +diesel 0.038 0.073 0.177 0.147 
*The data reported in the table are the optical densities measured by the spectrophotometer at 520 nm. 
Research by Ryu et al. (1996) suggested that bacteria and molds that grow in petroleum diesel 
fuel storage tanks in the aqueous phase are different than microorganisms that are found in biodiesel 
storage tanks. Similar to biodiesel storage tanks, these bacteria and molds grow on the interface 
between the fuel and water and can form large populations of biomass. However, Ryu et al. (1996) 
states that methyl soyate may inhibit microorganisms that can grow in petroleum diesel fuel storage 
tanks. They speculated that the unsaturation level of the oil plays an integral role in inhibiting microbial 
growth in petroleum fuel storage tanks. However, a side-by-side comparison of this study with results by 
Van Gerpen et al. (1996) cannot be made because different bacteria were used. 
2.7.3 Environmental Impacts 
In terms of using biodiesel for fuel, microbial activity and degradation negatively impacted its 
intended purpose. However, as outlined by Hill (2008) it could hold positive implications for the 
environment in the event of a fuel spill. Bioremediation of soil or a water system contaminated by 
biodiesel may require minimal attention as compared to the site of a petroleum diesel spill. Likewise, 
biodiesel constituents are natural and less harmful than constituent of petroleum diesel.  
49 
 
2.7.4 Biodiesel Storage Stability 
Prevention of microbial growth by the eradication of free water or inhibition of water ever 
contacting biodiesel would be the most preferred method of ensuring biodiesel integrity. However, this 
is not always possible. To impede microbial growth in biodiesel, biocides may be recommended. On the 
other hand, Hill (2008) stated that biocides added to a tank currently experiencing heavy microbial 
activity may make the situation worse. In this case, biocides can lead to increased levels of particulates 
in the fuel inevitably causing downstream issues in other fuel tanks or engines. Hill (2008) also implied 
that the use of biocides should not relinquish the need for care being taken to ensure minimal microbial 
activity or periodic monitoring. If biocides are desired for use, products such as Kill-Em? or GrotaMar 
71? are commercially available for purchase and use. 
2.8 Summary 
Irrigation applied to agronomic crops during the growing season has shown positive implications 
in terms of increasing yield. For oilseed crop production, increased yields result in amplified oil 
availability, thereby enhancing biodiesel output potential. However, oilseed constituents important for a 
small-scale, on-farm biodiesel production system include oil content along with FFA and protein. Low 
FFA is desired for least expense in ester conversion from the vegetable oil and high protein content is 
advantageous for livestock feed ration supplementation. Oilseed cake, which will contain the majority of 
the protein in the oilseed, can be utilized to offset livestock feed costs and contribute to the economics 
of an on-farm biodiesel production system. These constituents have shown no significant responses to 
irrigation if sufficient rainfall was received during the growing season of the respective crop. The actual 
composition of oilseeds depends on many factors, including genotype, growing season, geographic 
location, and agronomic practices (Boydak et al., 2002 and Bellaloui and Mengistu, 2008). Also, some 
research suggested that increased water quantity, not irrigation interval, may negatively impact oil 
50 
 
concentration in soybeans (Sprecht et al., 2001) while protein concentration was inversely correlated to 
oil content (Specht et al., 2001).  
 Irrigation alone may not necessarily be the deciding factor in oilseed constituents. Other 
parameters such as macronutrients and even micronutrients had an impact on oil content, protein 
concentration and yield within canola and cottonseeds (Sims et al., 1993, Sprecht et al., 2001 and Sawan 
et al., 2006). Sawan et al. (2006) also found that FFA could be decreased in cottonseed with applications 
of macronutrients. Environmental factors other than those imposed by irrigation or nutrients can also 
impact oilseeds. Damage by heat, wet weather, excessive moisture, poor storage, or other unfavorable 
conditions required extensive processing to obtain an edible or usable product plus have poor stability 
during storage.  
Hydrolysis is another factor that can heavily impact feedstock and biodiesel quality. Andersson 
and Lingnert (1998) found that metal traces such as copper or iron enhance hydrolysis. These metals can 
come in contact with oil or biodiesel in mechanical screw presses or storage containers.  Seeds, oil and 
biodiesel are all susceptible to hydrolysis with degree of unsaturation influencing hydrolysis stability 
(Merrill et al., 2008). However, measures can be taken to prevent degradation. Seeds should be stored 
in cool dry places while oil and biodiesel should be stored in dry, airtight containers not in direct sunlight 
or exposed to elevated temperatures.  
There are several methods used in the transesterification process with different concentrations 
of catalysts or alcohols. Acceptable reactions can occur at different temperatures and time durations. 
However, base catalyzed transesterification proved to be the most effective and least expensive from 
chemical cost as well as time requirement. Research suggested that one wt.% NaOH, 6:1 molar ratio of 
alcohol to oil, reaction temperature of 60 oC and reaction time of one hour is sufficient to satisfactorily 
transesterify vegetable oils with less than 0.5% water and less than 2% FFA. Feedstocks with higher 
51 
 
water or FFA contents will require further processing or pretreatment before transesterification to 
obtain a quality product. 
Care should be taken when storing biodiesel long-term to ensure water does not contaminate a 
storage tank. Frequent monitoring of a storage tank as well as periodic draining of free water from the 
bottom of a tank can ensure an environment conducive for microbial activity is not present. Water can 
enter storage tanks from vents or seals in the storage tank. Biodiesel is hydrophilic and hygroscopic and 
can absorb moisture and water. Additionally, high storage temperatures of approximately 40 oC can 
degrade biodiesel especially when biodiesel is exposed to air. Biodiesel degradation or oxidation can 
yield a carbon source from which bacteria may propagate. While biodiesel degradation may promote 
timely remediation of soil or water in the event of a spill as compared to petroleum diesel, there are 
negative connotations associated with long-term storage stability of biodiesel. If bacterial growth is not 
prevalent in an existing storage tank, biocides may be used for control measures.  
Small-scale, on-farm systems are proved to be marginally feasible in Oklahoma. Kenkel and 
Holcomb (2006) concluded that on-farm biodiesel production was not feasible at commodity prices 
experienced after 2005. Returns were sensitive to the farm level price of commodities, biodiesel, and 
meal value. On-farm processing of oilseed higher in oil content (canola, sunflower) was marginally 
feasible, but feasibility of soybean based biodiesel required a biodiesel value above $1.32/liter with 
historically high meal prices.   
 
 
 
 
 
 
52 
 
 
 
 
CHAPTER THREE 
RESEARCH METHODS 
3.1 Introduction 
This chapter outlines methodologies used within this study to attain the three research 
objectives. First, those for the experimental irrigation energy crop rotation will be presented, followed 
by methodology used in determining best-practices for conversion of vegetable oils into biodiesel, 
phosphorus removal from vegetable oils, and vegetable oil storage procedures conducive to a small-
scale, on-farm biodiesel production. Methanol recovery assessment and biodiesel quality of an in-house 
processor will then be discussed followed by biodiesel quality of degummed oils. Also included will be 
procedures used to assess the efficiencies of in-house mechanical extruders with varying oilseeds. Lastly, 
methodology for an economic assessment of a current small-scale biodiesel production system and 
proposed small-scale, on-farm biodiesel production operation will be outlined.  Included are variables 
tested along with statistical analyses performed. 
3.2 Irrigation Experiment for a Non-traditional Energy Crop Rotation 
A split-plot experimental design consisting of 48 plots was used for this experiment at the 
Tennessee Valley Research and Extension Center, Belle Mina, Alabama (Figure 3.1). Each plot was 12.2 
m in length by 11.9 m in width with the middle two rows or 1.5 m harvested for yield and other 
experimental data. The plots consisted of two rotations with a crop growing regimen of 1) continuous 
cotton and 2) canola-soybeans-cotton grown with six different irrigation treatments. Irrigation 
53 
 
treatments included rainfed, 25%, 50%, 75%, 100% and 125% pan evaporation replenishment levels 
adjusted for crop canopy. All cotton plots were planted around the third week of April with irrigation 
commencing in June depending upon cotton vegetative growth and environmental conditions. Soybeans 
were planted the third week of June for the canola-soybeans-cotton plots followed by cotton planted in 
the third week of April of the following year. After the fall cotton harvest from the canola-soybeans-
cotton plots, in the first part of October, canola was planted and then harvested the following second 
week of June. The experiment commenced in October, 2007 with the planting of canola and concluded 
in October, 2010 with the harvesting of cotton from rotation 1 and soybeans from rotation 2. The study 
site was under no-tillage management with soil fertility directed according to Alabama Cooperative 
Extension System fertility recommendations. Soil samples were collected within each plot after each fall 
harvest at a depth of 0 ? 15 cm. Samples were analyzed for fertility and pH levels by the Auburn 
University Soil Testing Laboratory, Auburn, Alabama. 
An irrigation scheduling spreadsheet was developed to closely monitor pan evaporation and 
crop canopy cover with the information used to determine daily application levels for each plot. Pan 
evaporation was determined from AWIS Weather Services, Inc. (www.awis.com) and daily rainfall was 
recorded with both parameters entered into an MS Excel spreadsheet on a daily basis. Crop canopy was 
measured weekly and a percentage value assigned relative to soil area covered between rows. Canopy 
cover was also recorded in the MS Excel spreadsheet. These parameters were used to calculate an 
application run-time for the irrigation system (eqn. 1). Water was applied to all plots at a rate of 2.26 
cm/hr with irrigation commencing when the accumulated pan evaporation, adjusted for canopy cover, 
required a minimum of 21 minutes of application for replenishment. In order to determine when the 21 
minute threshold was met, daily calculated irrigation run-time was carried over and added to the next 
day until a minimum of 21 minutes was reached. Run-times for a treatment were then programmed into 
54 
 
a Rain  Bird? Maxicom2 Central Control  (Rain Bird Corporation, Azusa, CA)  irrigation controller when 
required.    
A combine with a platform header width of 1.52 m was used to harvest soybeans corresponding 
to a harvested area of 18.6 m^2. The center pass was harvested for data analysis with the accumulated 
weight determined using a weigh buggy. Another combine with header width of 1.1-m was used to 
harvest canola making two passes across the plot center for data analysis with the approximate area 
harvested being 27 m^2 and yield was calculated after cleaning the seeds to remove trash. Canola was 
grown at 18-cm row spacing with the 12 center rows (2.2-m) harvested for data analysis. Cotton was 
harvested using a 2-row cotton picker with the center 4 plot rows used for yield data with seed cotton 
yield computed by dividing the harvested, accumulated mass by the harvested area. All yield 
calculations were the same for all crops.  
 
60AR T R TCCP a nR u n tim e  
where,                                                Runtime = irrigation runtime (min.) 
 Pan = AWIS pan evaporation (cm) 
 CC = percent canopy cover (%) 
 TRT = irrigation treatment (%) 
 AR = irrigation application rate (cm/hr) 
(3.1) 
55 
 
 
Figure 3.1. Layout of study site and treatment assignment. 
 
Fifty cotton boll samples were handpicked from the cotton plots just prior to harvest. The cotton 
samples were ginned using a laboratory scale, 10 saw cotton gin with the seeds being chemically de-
linted. Immediately after harvest, canola and soybean seed samples were analyzed for moisture content 
using a Dickey John GAC 2100b moisture meter, however, cottonseed sample moisture contents were 
measured at the time of seed analysis. All samples were analyzed by Hahn Laboratories, Columbia, SC. 
56 
 
De-linted cottonseed composite samples by plot were analyzed for protein (AOCS Official Method, 
2009b), oil (AOCS Official Method, 2009a), and FFA (AOCS Official Method, 2009c). Canola composite 
samples by plot were analyzed for protein (AOCS Official Method, 2009h), oil (AOCS Official Method, 
2009g), and FFA (AOCS Official Method, 2009f). Soybean composite samples by plot were analyzed for 
protein (AOCS Official Method, 2009e), oil (AOCS Official Method, 2009d), and FFA (AOCS Official 
Method, 2009f).  
3.3 Review of Biodiesel Production Procedures  
To evaluate the quality of biodiesel to be produced in a small-scale on-farm setting, a biodiesel 
production procedure must first be determined that yields satisfactory fuel at low input costs. The first 
step in the process was to conduct a literature review of biodiesel production procedures. Research of 
biodiesel production procedures using varying chemicals, procedures and feedstocks is well 
documented and further experimentation of multiple procedures is unnecessary. Therefore, this 
literature review consisted of recording procedure and result information including: catalyst type and 
amount used, alcohol type and molar ratio with respect to oil, reaction time and temperature and 
conversion efficiency. Because ASTM Standard D 6751 (2007) requires a transesterification conversion 
efficiency of 98% for the retail of biodiesel, the literature review focused on transesterification 
conversion efficiencies near or exceeding this value. However, in order to assimilate a comprehensive 
list, biodiesel production procedures yielding 95% transesterification conversion efficiency or greater 
were included as a potential recommended production procedure. The production procedures that did 
meet the conversion requirement to be included were evaluated first by chemical amount required and 
second on reaction time and ranked accordingly. These two parameters were assumed to be of most 
importance to a farmer. The method of least expense and reaction time was then used to produce 
biodiesel for experiments outlined below. 
57 
 
3.4 Vegetable Oil Storage Experiment 
3.4.1 Triglyceride Hydrolysis 
Since FFA concentration heavily dictates simplicity of biodiesel production, it was recommended 
that oil with concentrations less than 2% be used as biodiesel feedstocks for base catalyzed 
transesterification methods. Oil stored in conditions exposed to the atmosphere and subject to heat, 
can lead to hydrolysis and increased FFA concentration. Therefore, tests were performed to assess FFA 
concentration changes under sealed container storage conditions on WVO, cold pressed canola oil, and 
cold pressed soybean oil. Assuming oil will be stored on-farm and pre-phosphorus removal, only crude 
oil samples were stored in this experiment. Oil storage simulated typical on-farm techniques that would 
be used by a farmer. During the pressing of the oilseeds (e.g. canola and soybeans), four random one-
gallon samples of each oil type were collected starting at 30 minutes after press warm-up and 
continuing at one-hour intervals afterward until all samples were collected. These samples were placed 
in sealed, one-gallon HDPE (high density polyethylene) containers.  
 
Figure 3.2. Vegetable oil storage experiment. 
Control samples Experimental samples 
Thermocouple 
Temperature 
data logger 
58 
 
Water in vegetable oil can expedite triglyceride hydrolysis and FFA accumulation and was 
initially tested in each sample prior to beginning the experiment. After soybean and canola oils had been 
mechanically extruded and WVO had been collected from Auburn University dining facilities, each 
replicate sample was tested using ASTM Standard E 203-01 (2001). All samples were stored in a non-
climate controlled room to simulate oil storage in an on-farm shed or barn (12 samples total including 
four random samples of WVO collected from AU dining facilities). Figure 3.2 shows the samples used for 
this experiment. One-liter amber glass bottles were used for auto-claved control samples (WVO and 
canola, respectively) and to prevent hydrolysis due to sunlight. Four-liter containers were used to store 
samples of canola, WVO and soybean oils respectively. A summary of replications are as follows: 
? WVO 
? Cold pressed canola oil 
? Cold pressed soybean oil 
? Cold pressed auto-claved canola oil control 
? Auto-claved WVO control 
Samples were tested monthly for FFA concentration according to ASTM Standard D5555-95 
(2008) to determine rate of hydrolysis and for OD (optical density) to determine if microbial growth was 
present. The duration of the experiment was approximately nine months. Prior to FFA testing, each 
sample was shaken vigorously for 20 seconds in the sealed storage container to homogenize the sample 
and avoid stratification. Temperature measurements were recorded every 30 minutes during the 
duration of the experiment using Dickson SM 325 display temperature data loggers (Dickson, Addison, 
IL) (Figure 3.2) to understand the relationship of temperature to oil hydrolysis. Two thermocouples were 
suspended beside the storage containers and two were placed inside containers of oil. All containers 
were sealed from the atmosphere.  
3.4.2 Microbial Contamination 
Microbial growth may only be present if a free water layer exists because water bound in the 
fuel or oil is unusable for microbial growth. Therefore an experiment was performed, in conjunction 
59 
 
with the triglyceride hydrolysis research (Section 3.4.1), to determine if moisture in the seed or moisture 
accumulated over the storage period was sufficient to lead to microbial growth. The design and 
experimental procedures were the same as Section 3.4.1. However, control samples were included in 
the event microbial contamination existed in the samples prior to storage. Four control samples of 
canola oil, 0.5-L each, were collected concurrently with mechanically pressed experimental samples and 
four 0.5- L WVO control samples were collected from AU dining facilities. Control samples were stored in 
amber glass containers and autoclaved to reduce hydrolysis due to light exposure and sterilize the 
feedstock, respectively. They were then stored in the same location and conditions of the non-control 
samples (Figure 3.2).  
Once per month for approximately nine months, samples were tested for microbial 
contamination. All samples were vigorously shaken initially for 20 seconds to homogenize the oil in the 
event any stratification had occurred during storage. A 100-?l sample was drawn from each of the non-
control sample containers and placed in a clear bottom, 96 well microplate. Control samples were only 
opened inside a Labconco? Purifier Biological Safety Cabinet (Labconco Corporation, Kansas City, MO).   
The bio-safety cabinet was cleaned with ethanol and sterilized with 5 minutes of exposure to an ultra-
violet light. The amber glass bottles containing the oil were placed inside the cabinet then opened to 
collect a 100-?l sample which was added to the tray. Four 100-?l samples of distilled water were also 
added to four wells in the tray for control measurements. Next, the microplate was placed in a 
Spectramax M2 Microplate Reader (Molecular Devices, Sunnyvale, CA) ultra-violet spectrophotometer. 
The samples were tested for absorbance at a spectral range of 420 to 650 nm with an incremental step 
of two nanometers. Bacteria typically have an absorbance of 550 nm, so this wavelength will be the 
focus of the results.  
To ascertain results, bacteria positive samples in the ultra-violet spectrophotometer, bacteria 
positive control samples were created using Pseudomonas putida and either water or WVO. Five 
60 
 
bacteria concentrations were selected to test. Pseudomonas putida was cultured in substrate and 
volumetric samples of 1 ml, 2 ml, 5 ml, and 10 ml were measured from the culture and centrifuged to 
separate the bacteria and substrate. The bacteria samples were then washed twice with the respective 
media it was to be tested in (water or WVO) to remove traces of the culture media. To wash the 
bacteria, the culture media was poured off after centrifugation, then 2 ml of the media (water or WVO) 
were added to the vial, the vial was then vortexed to mix the bacteria and media. Next, the mixture was 
centrifuged and the process was repeated once more. After washing, 100 ?l of media was added to the 
bacteria and the sample was vortexed again to thoroughly mix the bacteria and media. Then a 100-?l 
positive control sample was drawn from the vial and placed in the 96 well microplate for absorbance 
analysis. A control sample of each media was measured for absorbance as well. Pseudomonas putida 
was used as the bacteria in the positive control samples because it was readily available and its 
absorbance value at a 550-nm wavelength is similar to other bacteria that might grow in vegetable oil 
storage containers. 
3.5 Methanol Recovery Efficiency Experiment 
The transesterification reaction is pushed to completion with the addition of approximately 
twice the amount of methanol required and excess must be removed after the reaction for safety and 
efficiency reasons. The current methanol recovery process of the Biodiesel Logic processor (Biodiesel 
Logic, Inc. Troy, AL), located in Auburn University?s Biosystems Engineering department, was believed to 
be poor and the addition of a vacuum pump may increase methanol recovery efficiencies. For this 
reactor, the current alcohol recovery process will be assessed for efficiency by analyzing biodiesel pre- 
and post-alcohol recovery using an evaporative method of methanol determination. The 
implementation of a vacuum pump for alcohol recovery will be assessed in the same manner. Current 
transesterification methods performed with this processor by lab technicians entail a volumetric ratio of 
61 
 
5:1 oil to methanol or approximately 4.79:1 molar ratio of methanol to oil. Since the chemical reaction 
requires 3:1 molar ratio of methanol to oil, 1.79 moles of methanol per mole of oil can be recovered or 
distilled from the biodiesel/methanol mixture. For a 90-L batch of oil, this amount equates to 
approximately 6.69 L of methanol.  
Figure 3.3. Biodiesel Logic Processor (Biodiesel Logic, Inc. Troy, AL). 
 
The maximum batch capacity of the Biodiesel Logic biodiesel processor is 208-L, however, 90-L 
batches were processed in this experiment to ensure WVO consistency and storage reserves were not 
depleted. Once 90-L of WVO were added to the reactor vessel (figure 3.3), the oil was heated to 57 oC. 
Upon the WVO reaching the desired temperature, chemical grade methanol (17.9-L) and sodium 
hydroxide (NaOH), granular form, were mixed for three minutes in the small HDPE (high density 
polyethylene) chemical mixing tank of the reactor. The amount of sodium hydroxide added was dictated 
by the FFA% of the oil. A 1-ml sample of oil from each batch was titrated using ASTM Standard D 5555-
95 (2008) to determine FFA% as oleic. The sodium hydroxide and methanol mixture was pneumatically 
pumped into the reactor while the WVO was being circulated in the reactor vessel. The mixture entered 
the reactor through the oil circulating pipeline just in front of the pump and was vigorously mixed with 
Mixing tank 
Reactor vessel 
Sodium hydroxide and 
methanol entry point 
Dry wash vessel 
62 
 
the oil as both were pumped to the top of the reactor and discharged into the vessel. This form of 
mixing is the only active mixing that took place during the reaction; therefore, the reaction time was 
increased from manufacturer recommendations of one hour to three hours. Upon completion of mixing 
for the reaction, the mixture was allowed to cool and glycerol was allowed to gravitationally settle from 
the biodiesel. The mixture cooled to at least 41 oC before glycerol was drained off through a valve in the 
bottom of the reactor vessel.  
After the glycerol was drained, the biodiesel underwent methanol recovery by one of the 
treatments outlined below. The biodiesel was heated to 80 oC in order to vaporize the residual methanol 
not consumed in the reaction. A small tube in the top of the reactor vessel allowed vapors to travel to a 
condenser. The condenser consisted of a water jacket or double walled tube in which chilled water was 
pumped through with a commercially available 12-volt water pump. The chilled water was kept at or 
near 0 oC at all times. During methanol recovery, a valve at the outlet of the condenser tube was opened 
and condensed methanol was collected in a vessel. For the standard methanol recovery process outlined 
by the manufacturer (standard), methanol vapor was thought to have entered the condenser only 
because of vapor pressure in the reactor. However, the vacuum treatment consisted of methanol 
recovery with the assistance of a Gast Manfacturing, Inc vacuum pump model DOA-P704-AA (Gast 
Manufacturing, Inc., Benton Harbor, MI). The vacuum pump line was plumbed into the methanol 
catchment vessel and created 129 mm Hg negative pressure in the system. Along with slightly lowering 
the boiling point of methanol, the methanol vapors were allowed to more freely move towards the 
condenser. Methanol recovery was allowed to run for 4 hours, as recommended by the manufacturer. 
A randomized complete block experiment was designed to test methanol recovery efficiencies 
as a result of each treatment. Four replications were performed. Each replication consisted of two 
treatments that were randomly assigned, either vacuum pump assisted (vacuum) or standard methanol 
recovery procedure (standard). Random 50-g sample from each batch of biodiesel produced were 
63 
 
collected after methanol recovery and for efficiency analysis purposes. The samples collected are 
outlined in table 3.1 and were ?BD post methanol recovery? and ?glycerol?. 
An evaporation method was used for methanol determination in biodiesel samples post 
methanol recovery and glycerol samples. This analysis resulted in a volumetric methanol balance of the 
system.  A known volume of methanol was initially added to the WVO; part of it was consumed in the 
transesterification reaction and can be determined using equation 3.2 and assuming three moles of 
methanol are consumed in the transesterification reaction for every one mole of WVO. A portion of 
methanol was recovered using the experimental procedures above and was volumetrically measured 
after methanol recovery concluded. A portion of methanol separated from the biodiesel with the 
glycerol layer and was drained from the reactor. Another portion of the methanol was residual in the 
biodiesel. The residual methanol content in glycerol and biodiesel was measured using the evaporative 
method.  
A Rotavapor R110 (B?chi Laboratory Equipment, Switzerland) was used to evaporate methanol 
from all samples. The water bath was heated to only 70 oC to avoid volatilizing non-methanol portions of 
the sample. Approximately 50 g of sample was placed in the round bottom flask and weighed. The flask 
was attached to the rotary evaporator, chilled water was passed through the condenser during the 
experiment, and the vacuum pump was attached to the rotary evaporator operating at a negative 
pressure of 129 mm Hg. Each sample was allowed to evaporate for 1.5 hours. The round bottom flask 
and its contents were then weighed and the final weight was recorded. To ensure the integrity of this 
method and prior to the actual experiment, control samples of a mixture of purified canola oil and a 
known amount of methanol were evaporated under the conditions outlined above.  
 
 
64 
 
MO
MOMW MWVCM *10* *3***  
where,    M  =  Methanol consumed during transesterification (L) 
   C  =  Conversion efficiency of transesterification (%)    
   V  =  Volume of oil transesterified (L) 
   ?O  =  Density of oil (kg/L) 
   MWM  =  Molecular weight of methanol (g/mol) 
   MWO  =  Molecular weight of oil (g/mol) 
   ?M  =  Density of methanol (kg/L)  
Table 3.1. Methanol recovery experiment summary. 
TRT Vacuum Standard 
Samples 
collected  
Raw BD Raw BD 
BD post MeOH recovery BD post MeOH recovery 
BD post dry wash BD post dry wash 
WVO WVO 
glycerol glycerol 
BD denotes biodiesel 
Post MeOH recovery denotes after methanol recovery from raw biodiesel 
Post dry wash denotes after washing biodiesel with AMBERLITE? BD10DRY resin for purification 
3.6 Phosphorus Removal from Vegetable Oil Experiment 
Simple, cost effective methods of degumming vegetable oils were explored in this Section that 
required non-hazardous chemicals and simple procedures. Further, gravitational separation of oil and 
gums was utilized for these experiments, where applicable, to obtain results a typical farmer may 
experience. 
3.6.1 Hot Water Method 
A hot water method of phosphorus removal was adapted from Brekke (1980). He stated that 
hydration by the addition of water causes most phosphatides (phospholipids) and gums present in a 
crude oil to become insoluble in the oil. The phosphatides, proteins, and other colloidal impurities are 
hydrated by the hot water and swell or expand forming gels of higher specific gravity than the vegetable 
oil and agglomerate as flocculent particles, which can be gravitationally separated (figure 3.4). Three, 
150-ml samples of each crude oil type (soybean and canola) were placed in erlenmeyer flasks and 
(3.2) 
65 
 
heated in a New Brunswick Scientific, Innova 40 incubator shaker (Edison, NJ) (figure 3.2) to 65 oC. Once 
heated, the samples were mixed with water, based on the average phosphatide content for the 
respective oil, and mechanically agitated at 200 rpm for 30 minutes. Brekke (1980) stated that the 
appropriate amount of water to add was equivalent to 75% of the phosphatide content in the oil. 
Soybean oil can contain 1.1 to 3.2 vol.% phosphatides, with the average being about 1.8 vol.%, therefore 
2.1 ml (1.4 vol.%) of water was added. Canola oil contains on average 1.25% phosphatide (Przybylski, 
2001), therefore 1.4 ml (0.94 vol.%) of water was added to canola oil samples. Once the samples were 
agitated for 30 minutes, they were allowed to cool and settle for two days according to procedures 
outlined by Brekke(1980). Hot water degummed oil samples were analyzed by Hahn Laboratories, 
Columbia, SC and AOCS Official Method (2009j) was used to determine phosphorus concentrations.  
3.6.2 Citric Acid Method 
The citric acid phosphorus removal procedure used in this experiment was adapted from 
Diosady (1982). Three, (150 ml) samples of each oil type (soybean and canola) were placed in 
erlenmeyer flasks and heated to 65 OC in a New Brunswick Scientific, Innova 40 incubator shaker 
(Edison, NJ) (figure 3.2). A 50% aqueous citric acid solution was added to the oil until a concentration of 
2,500 mg/kg citric acid to oil was obtained (675 g of citric acid solution). After the oil was mixed for 10 
minutes at 200 rpm, a further aliquot of 2 wt% water was added for hydration. Agitation continued for 
20 minutes, then the oil was allowed to cool and gums settled from the oil for two days. The citric acid 
removed non-hydratable phospholipids and the water removed hydratable phospholipids. When the 
mixture cooled, a phase separation appeared in which the degummed oil was on top and the water and 
phospholipids on the bottom (figure 3.4). The degummed oil was then decanted. Citric acid degummed 
oil samples were analyzed by Hahn Laboratories, Columbia, SC and AOCS Official Method (2009j) was 
used to determine phosphorus concentrations.  
66 
 
 
Figure 3.4. Incubator shaker used for agitation of oils (left) and settled gum phase in soybean oil 
(right).  
 
3.6.3 Anion Exchange Resin Method ?AMBERSEP? BD19 
AMBERSEP? BD19 was used as a third method of phosphorus removal. Procedures for this 
experiment were followed according to manufacturer?s recommendations (The Dow Chemical Company, 
Midland, MI) and were confidential. A Nexus 6000 Ultra High Pressure syringe pump by Chemyx Inc., 
Stafford, TX (figure 3.5) was used to meter fluids into the column at a controlled rate. First, five volumes 
of dry methanol were injected into the reactor using the syringe pump, displacing the water used for 
setup. Before experimental samples were collected, 200 ml of soybean oil were metered through the 
column to remove methanol. A resin depletion test was required, so 100 ml of soybean oil was then put 
through the column to document initial resin effectiveness in removing phosphorus. 
Next, 600 ml of soybean oil was put through the column, three 100-ml samples for phosphorus 
analysis and three 100-ml samples for transesterification. Then, 200 ml of canola oil was pumped 
through the column to avoid cross contamination in experimental canola samples. A volume of 600 ml  
67 
 
Figure 3.5. Column positioned inside oven (left), oil preheating on the hotplate (center), and syringe 
pump metering oil into the column through the top of the oven (right). 
 
of canola oil was then put through the column, three 100-ml samples for phosphorus analysis and three 
100-ml samples for transesterification. To ensure no cross contamination, 200 ml of soybean oil was 
metered through the column, then 100 ml of soybean oil was pumped through the column to complete 
the depletion test sampling. Table 3.2 summarizes complete throughput of oil into the column. 
AMBERSEP? BD19 degummed oil samples were analyzed by Hahn Laboratories, Columbia, SC and AOCS 
Official Method (2009j) was used to determine phosphorus concentrations. 
Table 3.2. Order of operations for AMBERSEP? BD19 phosphorus removal experiment.  
Order Operation 
1 Inject 200 ml of soybean oil to remove methanol 
2 Inject 100 ml of soybean oil for resin depletion test 
3 Inject 600 ml of soybean oil (three 100 ml samples for phosphorus analysis, and three 100-ml samples for transesterification) 
4 Inject 200 ml of canola oil to avoid contamination 
5 Inject 600 ml canola oil (three 100 ml samples for phosphorus analysis, and three 100-ml samples for transesterification) 
6 Inject 200 ml of soybean oil to avoid cross contamination 
7 Inject 100 ml of soybean oil to complete depletion test 
 
68 
 
3.6.4 Control 
Three control samples of both oil types (soybean and canola), having no phosphorus removed, 
were analyzed for phosphorus concentration in order to determine effectiveness of each degumming 
method outlined above. Control samples were analyzed by Hahn Laboratories, Columbia, SC and AOCS 
Official Method (2009j) was used to determine phosphorus concentrations.  
3.6.5 Experimental Design 
A randomized, complete block experimental design was developed to test feasibility and 
effectiveness in phosphorus removal according to the procedures outlined above. Using an in-house 
mechanical screw press, canola harvested from Tennessee Valley Research and Extension Center (Belle 
Mina, AL), was pressed and at three random times during pressing, oil samples were drawn from the 
press signifying three replications. Each replication was further split into four samples. Each of the four 
oil samples were randomly assigned a method of phosphorus removal (i.e. hot water, citric acid, or 
AMBERSEP BD19 ion exchange resin) or control. The same experimental design was implemented for 
soybean oil as well. Eight tests with three replications each were performed for a total of 24 tests. A 
summary of test replications is illustrated in table 3.3.  
Table 3.3. Summary of phosphorus removal tests performed. 
Oil Type Phosphorus Removal Treatment 
Soybean 
Hot water 
Citric Acid 
AMBERSEP? BD19 resin 
Control 
Canola 
Hot water 
Citric Acid 
AMBERSEP? BD19 resin 
Control 
69 
 
3.7 Biodiesel Quality Experiment 
3.7.1 Biodiesel Logic, Inc. Biodiesel Processor 
In conjunction with the methanol recovery experiment performed on the Biodiesel Logic 
biodiesel processor, a concurrent experiment was performed to assess biodiesel quality. Given the fact 
that raw biodiesel is heated and agitated again during methanol recovery and that it is washed in a dry 
wash procedure using AMBERLITE? BD10DRY (Rohm and Haas Company, Philadelphia, PA), two 
objectives were sought from this experiment. The first was to determine if post reaction or 
transesterification during methanol recovery occurred. Glycerol release during methanol recovery is an 
indication that insufficient mixing occurs during transesterification in the reactor vessel. Inadequate 
reaction could be due to the fact that the only agitation of the WVO, sodium hydroxide and methanol 
mixture was in the pump as previously discussed; however, increasing the reaction time from one hour 
to three hours may have nullified insufficient agitation. The second objective of this experiment was to 
determine the dry wash resin?s efficiency in removing free glycerin from biodiesel. AMBERLITE? 
BD10DRY was advertised by the manufacturer to remove, among other things, free glycerin. Analysis of 
biodiesel pre- and post-washing could determine if this resin removed free glycerin from the biodiesel.  
In addition to transesterification steps mentioned in Section 3.5 using the Biodiesel Logic 
biodiesel processor and after methanol recovery was performed, biodiesel was transferred to the dry 
wash vessel (figure 3.5). Once the temperature of the biodiesel was <38 oC the dry wash procedure was 
performed. The manufacturer recommended optimal washing temperature below 38 oC and an 
integrated thermocouple prevents washing at temperatures exceeding 38 oC. A column inside the 
washing tank of the Biodiesel Logic biodiesel processor held approximately 18 kg of AMBERLITE? 
BD10DRY. Biodiesel was pumped from the dry wash vessel into the top of the column containing the 
resin with maximum outlet pressure into the column of 155 mm Hg with the process performed over 
70 
 
four hours. A 50-g sample of biodiesel post dry wash from each replication was collected for quality 
analysis. Upon completion, the biodiesel is said to be a ?finished product? and pumped from the 
processor into a storage vessel for future use. The exact experimental design used in Section 3.5 was 
used for quality assessment. Samples collected for this analysis are outlined in table 3.1 and were ?raw 
BD?, ?BD post MeOH recovery? and ?BD post dry wash.?  
3.7.2 Degummed Oils 
Degummed oil samples from Section 3.6 outlined above were transesterified to determine 
quality of biodiesel produced as a result of the specific degumming method. The transesterification 
procedure was determined from published literature and frequently used by researchers. Methanol was 
used as the alcohol and sodium hydroxide (NaOH) as the catalyst for transesterification. Procedures 
were based on the work of Freedman et al. (1984a). Chemical concentrations, reaction time and 
temperature are outlined below. 
? Molar ratio of methanol to oil of 6:1 
? One wt.% NaOH 
? 60 oC reaction temperature 
? One-hour reaction time 
Approximately 95 g of each oil sample was placed in an erlenmeyer flask and capped with a 
rubber stopper. The samples were placed in a New Brunswick Scientific, Innova 40 incubator shaker 
(Edison, NJ) and agitated at 100 rpms until the desired temperature of 60 oC was reached. In the 
meantime, a sodium hydroxide and methanol mixture was produced. The mixture was then added to 
the heated oil and the mixture was agitated at 200 rpm for one hour, maintaining a temperature of 60 
oC. After the reaction, the biodiesel/glycerin mixture was allowed to cool to room temperature and 
gravitationally settle overnight before decanting. The glycerol released by the reaction was removed.  
Like the samples above, a GC was used to analyze these samples as well with conversion 
efficiency being calculated. As a secondary analysis, samples will be analyzed for phosphorus as 
71 
 
transesterification has the potential to remove a portion of the phospholipids. Three replicates of each 
oil type/degumming method combination were transesterified and are outlined in table 3.3 with the 
exception of three replicates of WVO added as a control and for comparison purposes. 
Biodiesel samples from the above two experiments were analyzed using a gas chromatograph 
(GC) with a modified method designed by an Auburn University Biosystems Engineering post doctoral 
fellow to measure free glycerin, monoglyceride, diglyceride, and triglyceride in biodiesel. Thus, allowing 
for calculation of conversion efficiency, weight percentage of free glycerin and weight percentage of 
total glycerin. The latter two are requirements of ASTM Standard D6751 (2007) and must be <0.02 wt% 
and <0.24 wt%, respectively to adhere to the standard.  
To determine concentrations of free glycerin and total glycerin in the experimental biodiesel 
samples, 0.1 g of each sample were measured using a RADWAG WAX 110 (North Miami Beach, FL) 
balance that records to the ten-thousandths decimal place and placed in a 10-ml glass vial with a 
sealable cap. A volume of 100-?l of each standard solution, (S) ? (-) ? 1,2,4-Butanetriol (Internal 
Standard 1), 1,2,3-Tridecanolyglycerol (Internal Standard 2) and N-methyl-N-trimethylsilyltri-
fluoracetamide (MSTFA), was added to the biodiesel sample, respectively. The solution was allowed to 
sit for 15 minutes while the MSTFA digested the biodiesel sample. Next, approximately 8 ml of heptanes 
were added to the mixture and sub-samples were placed into GC vials for testing. An Agilent 
Technologies gas chromatograph model 7890A (Agilent Technologies, Santa Clara, CA) The GC method 
used was ASTM Standard D 6854-00 (2006) with the exception of a split/splitless inlet in splitless mode 
at a temperature of 320 oC. To ensure water content in WVO did not affect or inhibit transesterification, 
a volumetric Karl Fischer titration was performed on all samples. ASTM Standard E 203-01 (2001) was 
adhered to for water analysis. 
72 
 
3.8 Mechanical Extruder Efficiency Analysis 
A wide array of mechanical screw presses, designed to extrude vegetable oil from oilseeds, are 
commercially available. Some cold press extruders require specialized skill to optimize oil output, while 
others demand minimal operator input as most parts are in fixed positions. Currently, there are two 
different mechanical oilseed presses housed in Auburn University?s Biosystems Engineering biodiesel 
laboratory. The Henan Double Elephant Machinery Co., Ltd (Henan, China) model 6YL-120 and Karl 
Str?hle GmbH & Co. KG (Dettingen, Germany) SK 60/2 presses are rated, by the manufacturer, at 
capacities of 6 tonnes and 720 kg of seed throughput per 24-hour period, respectively. To evaluate the 
capacity and primarily compare oil extraction efficiencies, tests were conducted with canola near 
optimal moisture content for oil extraction, 6.5%wb for canola. Soybean and sunflower were also 
assessed on the Henan Double Elephant Machinery Co., Ltd model 6YL-120. A known mass of oilseed 
was extruded in the mechanical screw presses with three replications completed. From these tests, the 
following mean data was calculated and used to evaluate press performance: 
? Average oilseed processed (kg/hr) 
? Average volume of oil produced (L/hr) 
? Average mass of oilseeds in versus volume 
of oil out (kg/L) 
? Extraction efficiency (oil out/theoretical oil 
available, %) 
3.9 Economic Analysis of Small-Scale Biodiesel Production Systems 
3.9.1 Auburn University?s Biosystems Engineering Biodiesel Production System 
Currently, in Auburn University?s Biosystems Engineering Department, an ongoing project exists 
in which staff employees collect WVO from the three dining facilities on Auburn?s main campus and 
process this oil into biodiesel. This system closely parallels one that might exist on-farm and actual 
economics are valuable in assessing the feasibility of an on-farm biodiesel production system. 
73 
 
The system consisted of a part-time staff member who collects WVO from campus locations and 
processes biodiesel. At each dining facility, several 30-gallon drums are placed on spill pallets and dining 
facility staff empty used oil into the barrels. Spill pallets and 30-gallon drums (figure 3.6) are required by 
Auburn?s Risk Management division which may not be used for an on-farm process.  
 
Figure 3.6. Spill pallet with dolly ramp and 30-gallon drums required by Auburn University Risk 
Management. 
 
WVO drums at the dining facilities are checked and collected approximately eight times per 
month with the oil transported to the Biosystems engineering biodiesel laboratory. Once in the 
laboratory, the oil was placed into settling containers (figure 3.7) so particulates from the frying process 
and any small amount of water can gravitationally settle. Each settling tank had its own pumping system 
and either tanks? contents could be pumped into the biodiesel processor if desired. 
Once the oil has settled for approximately one week, depending upon ambient temperatures, it 
was pumped from the primary settling tank into the secondary settling tank and allowed to settle for an 
additional week. Next, the WVO was loaded into the biodiesel processor (figure 3.5) and processed in 
208-L batches according to transesterification procedures outlined in Section 3.5 and Section 3.6.  After 
the biodiesel dry wash with AMBERLITE? BD10, it was pumped into a storage tank (figure 3.8) for future 
use. 
 
74 
 
Figure 3.7. Primary (left) and secondary (right) WVO settling tanks. 
 
In addition to the aforementioned components, there are fixed and variable costs associated 
with the process including: chemicals, PPE (personal protective equipment), spill containment and clean-
up equipment, labor, and power requirements. Costs of all components, equipment, processes, and 
power requirements were evaluated and factored into the economic assessment. Mr. Christian 
Brodbeck, Research Engineer III, who oversees the biodiesel production system and Mr. Jonathan 
Griffith, Biosystems engineering technician, were consulted to acquire actual numbers for collection, 
processing and equipment costs and labor. Typical on-farm labor hourly wage was established by ACES 
(2011). All fixed costs were given a 10-year life expectancy and straight-line depreciation was used to 
determine a yearly cost. Lifetime maintenance costs were determined for fixed cost items by calculating 
15% of the total cost and dividing by 10 years to determine a yearly maintenance cost. The 2010 total 
yearly operating cost was divided by total oil collected for 2010 and a cost per gallon was determined. 
75 
 
Figure 3.8. Storage tank for processed biodiesel. 
 
3.9.2 Small-scale, On-farm Biodiesel Production System 
Since a small-scale, on-farm biodiesel production system closely paralleling the Auburn 
University Biosystems engineering biodiesel production system may be feasible; all costs that apply 
were extrapolated from the previous economic assessment to oilseed crop production data obtained 
from Section 3.2. From this irrigation experiment, soybeans and canola oilseed data were evaluated.  
Data required for economic assessment was adapted from University of Idaho (2010) and 
included crop yield, oil content, seed storage cost, No. 1 petroleum diesel fuel cost and mechanical press 
equipment cost plus operating cost. Since the co-product of mechanical pressing of oilseeds is valued as 
a livestock feed ration supplement, comparable livestock feed prices were gathered as well. Opportunity 
costs of the oilseeds were used for the economic assessment as it was determined that if a farmer did 
not produce biodiesel from the oilseeds, he or she would sell the oilseeds through traditional 
commodity markets.  
Once oilseed costs were obtained and yield and oil content determined for each respective 
oilseed, a cost per gallon of oil was calculated. The oil cost per gallon, in conjunction with the processing 
76 
 
cost from the previous economic assessment and mechanical press operating cost and fixed cost with 
straight-line depreciation were used to determine a production cost per gallon of biodiesel. Factored 
into the assessment was a deduction for feed cost, generated by the utilization of oilseed cake as 
livestock feed supplementation. Biodiesel production cost on a per volume basis was assessed for 2010 
monthly averages of commodity market prices of oilseeds and meal. The production costs were 
compared to No. 1 petroleum diesel fuel costs (ACES, 2011).  
3.10 Statistical Analysis 
3.10.1 Irrigation Experiment for a Non-traditional Energy Crop Rotation 
The trial was designed as a randomized complete block (r=4) with a split plot split block 
restriction on randomization, where levels of the factor crop rotation were assigned to mainplots, 
irrigation level to subplots, and crop year was treated as a repeated measures because of the nature of 
the experiment. For purposes of this study a separate analysis was conducted for each crop because the 
experiment was too new to be able to investigate rotation effects; the 2nd full cycle of rotation 2 was 
completed in autumn 2010. The statistical model thus was based on a repeated measures design within 
a randomized complete block design. 
Mixed models procedures as implemented in SAS  PROC GLIMMIX were used to analyze the 
data, where irrigation treatment, year, and the two-way interaction were fixed effects. Random effects 
were block, block*irrigation treatment, and block*year. We used R-side modeling to provide for an 
adequate residual variance structure based on a Corrected Akaike?s Information Criterion (AICC). A first-
order autoregressive structure that allowed for heterogeneous variances among experimental units 
[ARH(1)] was appropriate for cotton response variables. No correlated error structure was detected for 
soybean and canola response variables. This was expected given that these crops were grown on the 
same experimental unit only every other year. Given that yields varied drastically between crop years 
77 
 
we employed the group option to account for heterogeneous variances between years, again employing 
AICC to evaluate model fit. 
The first step in evaluating the results for a given response variable was to determine if the 
irrigation treatment x year interaction was significant (  = 0.1). If it was significant then the SLICEDIFF 
option with a simulation adjustment for multiple comparisons (  = 0.1) was used to compare irrigation 
treatments within crop years. If the interaction was not significant then the main effect for irrigation 
treatment was evaluated. Again treatments were compared using the simulation option to guard against 
an inflated Type I error. Whenever possible the response to irrigation was also modeled using a 
regression approach within a mixed models framework.  
3.10.2 Vegetable Oil Storage Experiment 
An analysis of variance (ANOVA) was conducted using SAS?s General Linear Model (GLM) to 
determine possible statistical differences among treatment means for each oil type in Section 3.4. 
Means were assigned a letter relative to significant differences based on oil type and method of 
phosphorus removal. Means of the four replications in Section 3.4.1 were plotted (FFA vs. time) and a 
linear regression analysis was performed, computing a linear equation to predict triglyceride hydrolysis 
over time. Coefficient of determination was also computed to determine strength of the equation. 
Mean optical density (absorbance) versus time was graphed using MS Excel at the 550-nm wavelength 
for each oil type and control samples (Section 3.4.2) to determine microbial accumulation in the oil 
samples over time. Linear regression analysis of absorbance versus bacteria concentration for positive 
control samples of oil and water was also conducted with Pearson?s Correlation Coefficient calculated to 
determine linearity among different concentrations.  
78 
 
3.10.3 Methanol Recovery Experiment 
Treatment means of methanol recovery in Section 3.5 were computed to determine differences 
in methanol recovery efficiency (methanol recovered from raw biodiesel and residual methanol in 
biodiesel post recovery). Means were also generated to determine methanol remaining in glycerol once 
drained from raw biodiesel. Values were then computed for money saved in recovered methanol to be 
recycled and money lost in unrecovered methanol.  
3.10.4 Biodiesel Quality Experiment 
An analysis of variance (ANOVA) was conducted using SAS?s General Linear Model (GLM) to 
determine possible statistical differences among treatment means for biodiesel quality and AMBERLITE 
BD10 effectiveness in removing free glycerol in Section 3.7.1. Means were assigned a letter relative to 
significant differences based on treatment and biodiesel sampling time. The ANOVA procedure was also 
used in Section 3.7.2 to compare means of phosphorus removal methods for soybean oil. Means were 
assigned a letter relative to significant differences of mean phosphorus removal methods for each oil 
type. Because canola oil transesterification failed for several treatments, unsummarized data was 
presented. 
3.10.5 Mechanical Extruder Efficiency Analysis 
Data from both screw presses were collected measuring oilseed into press and volume of oil 
out. Time for a mass of seeds or volume of oil to be processed or collected was measured as well. From 
this data, processing rates and oil output rates could be computed. Knowing the percentage of oil in 
specific seeds, extraction efficiency was then calculated based on available oil. It was assumed that only 
80% of total seed oil was available to be extracted using mechanical means. Data from both presses 
were compared. 
79 
 
3.10.6 Economic Analysis of Small-Scale Biodiesel Production Systems 
Economic analysis of Section 3.9.1 was analyzed in MS Excel by first computing total volume of 
oil collected on a yearly basis and the associated transportation cost. Next, equipment costs for the 
collection site and biodiesel production and operating costs were summed, respectively. Each fixed cost 
component was then given a life expectancy and straight-line depreciation was used to determine a 
yearly cost. Variable costs were calculated for the year based on the volume of oil collected. The total 
yearly cost was divided by the total volume of oil processed and the cost per volume was determined. 
The model structure from Section 3.9.1 was extrapolated to Section 3.9.2 but was adapted to fit an on-
farm scenario with components of collection site equipment and portions of the spill containment 
equipment omitted. In Section 3.9.2, commodity prices were determined from commodity trade reports 
and a model was generated on an acre basis, taking into account oilseed yield, soyhull pellet price and 
No.1 diesel fuel price. Yearly costs were calculated with opportunity costs of oilseeds used as the value 
of the oilseeds. Oilseed cake generated from the extrusion process was given the current market value 
of soyhull pellets and was recommended to be used as livestock feed ration supplementation. This 
model was designed in MS Excel to be used by a farmer to assist in predicting his cost per volume of 
biodiesel production and equipment needed.   
 
 
 
 
 
 
 
 
80 
 
 
CHAPTER FOUR  
  RESULTS AND DISCUSSION 
4.1 Introduction 
This chapter reports the results of this study. These results are intended to enhance the 
understanding of small-scale, on-farm biodiesel production facilities from environmental, feasibility, 
quality and economic standpoints. Variable irrigation rates on energy crop production was analyzed 
from both environmental and quality standpoints in determining if seed quality and constituent 
concentrations were dictated by irrigation levels. Mechanical processing of oilseeds was analyzed for 
efficiency with collected oil analyzed to ultimately determine best management practices in small-scale 
biodiesel production to produce consistent quality biofuel. Biodiesel procedures and processes were 
analyzed for quality measures and process efficiency. Finally, the economics of production systems were 
estimated to determine feasibility and returns on investments at the farm level.   
4.2 Irrigation Experiment for a Non-traditional Energy Crop Rotation 
4.2.1 Cotton  
Seedcotton yield results showed increased yield with increased irrigation treatments for 2008 
and 2010 data (figure 4.1). Given that cotton plots received only 13 cm of seasonal rainfall in 2010 but 
29 cm in 2008, the linear regression trend line for 2008 cotton yield data was expected to have been 
above that of 2010. Total seasonal rainfall at TVREC in 2009 was 32 cm, which was slightly above the 82-
year average of 29 cm. However, lower than expected yields for 2009 treatments were attributed to boll 
shedding due to cool weather and high moisture at the end of the season.  
81 
 
Protein concentration was significantly different among irrigation treatments for the years of 
the study (figure 4.2). A slight trend was measured for protein concentrations with respect to irrigation 
treatments in that protein decreased with increasing irrigation levels. Protein levels were reported on a 
dry matter basis and ranged from 20.5% in 2009 (100% irrigation treatment) to 27.3% in 2010 (25% 
irrigation treatment). A seven percent protein difference appeared substantial when considering the 
oilseed cake would be used as livestock feed ration supplementation. However, the overall objective of 
this experiment was to determine how irrigation might affect biodiesel yield and quality. Protein was a 
secondary constituent, but yield, the primary constituent effecting biodiesel output, showed favorable 
results at higher irrigation levels. Therefore, a sacrifice of protein concentrations would be 
recommended in an effort to increase overall biodiesel yield.  
Figure 4.1. 2008 through 2010 cotton yield results. 
 
82 
 
Figure 4.2. 2008 through 2010 cotton protein results. 
 
Figure 4.3. Cottonseed germination inside the boll and prior to harvest, 2009. 
 
The cool weather and high moisture at the end of the season not only effected yield, but also 
FFA concentrations. FFA levels experienced in 2008 and 2010 were well below 1% for all treatments with 
yearly averages of 0.6% and 0.4%, respectively. The effect of irrigation on FFA was subtle compared to 
the biodiesel output limiting factor of yield. For this reason, irrigation treatment recommendations 
83 
 
would be based on yield and not FFA responses. However, the 2009 yearly FFA mean of over 9% was 
about 18 times higher than 2008 and 2010 means (figure 4.4). Elevated FFA values were attributed to 
high moisture at the end of the season. The high moisture not only caused boll shedding, but induced 
seed germination in open cotton bolls (figure 4.3). Late seasonal rainfall resulted in harvest being 
postponed several weeks after cotton bolls opened. Cottonseeds were exposed to excessive moisture 
for a prolonged time period and the lipase enzyme in the cottonseed was activated, beginning the 
germination process. This enzyme initiated the breakdown or hydrolysis of triglycerides in the 
cottonseed. Through this triglyceride hydrolysis process, energy was released and utilized by the 
emerging cotton plant with the intention of providing life sustaining nutrients until the root system was 
established and nutrient uptake along with photosynthesis could begin. The germination process was 
detrimental in terms of biodiesel production from cottonseeds. With levels of greater than 9%, base 
catalyzed transesterification alone would be difficult and a quality biodiesel fuel would almost be 
impossible to produce. Most likely, an acid catalyzed esterification process would be necessary to 
convert the FFA to esters, thus neutralizing the remaining oil. After the acid catalyzed esterification or 
acid pretreatment, base catalyzed transesterification could proceed as normal. The acid esterification 
step would incur substantially more processing costs to a small-scale biodiesel operation. In the event 
this scenario was experienced on-farm and cotton harvest was prolonged with bolls opened and late 
season rainfall experienced, a farmer would gain higher returns by selling the seed at the gin.  
Cottonseed oil yield in all years of the study resulted in no significant differences among 
treatments. However as with many of the other parameters, a significant interaction did exist among 
years. Because year is a non-repeatable variable and considered a ?nuisance? factor, year interactions 
were not discussed. Table 4.1 shows yearly oil concentration means with only 2% difference between 
lowest and highest years. In terms of biodiesel production, yield was the limiting variable that 
significantly affected theoretical biodiesel yield. Biodiesel yield, as a result of cottonseed yield response 
84 
 
to irrigation was lowest and highest in 2010 for 0% and 100% treatment means, correspondingly. 
Irrigation treatments of 0% and 100% pan evaporation adjusted for crop canopy cover resulted in mean 
theoretical biodiesel yields of 374 L/ha and 620 L/ha, respectively. However, theoretical biodiesel yields 
in 2009 were not computed due to high FFA concentrations.  
Figure 4.4. 2008 through 2010 cottonseed FFA results. 
 
 
Table 4.1 2008 through 2010 Cottonseed oil results. 
Year LSmean (%d.b.)* SE 
2008 20.8 0.22 
2009 22.4 0.22 
2010 22.8 0.22 
*d.b. denotes dry matter basis. 
85 
 
4.2.2 Soybeans 
Significant differences among treatments were found in soybean for oil and yield, with 
treatment x year interactions significant except in protein and FFA. Seasonal rainfall (June to August) 
was 29 cm for soybeans in 2008 but only 13 cm for 2010 soybeans and was valuable in explaining yearly 
differences for many of the soybean parameters. Figure 4.5 highlights yield results from 2008 and 2010 
soybeans. Overall higher yields for all treatments in 2008 were attributed to the seasonal rainfall. 
However, limited rainfall in 2010 greatly accentuated yield responses to irrigation with the 125% 
treatment resulting in highest mean yield (4,054 kg/ha). Seed yield proved to be the determining factor  
Figure 4.5. 2008 & 2010 soybean yield results.  
 
86 
 
in biodiesel yield and in years where seasonal rainfall was low, irrigation at 125% pan evaporation 
adjusted for crop canopy could increase biodiesel yield from 196 L/ha (0% treatment mean) to almost 
743 L/ha, as it did in 2010.   
Figure 4.6 illustrates lack of interaction between years for protein with quadratic regression 
lines parallel. Protein was reported on a dry matter basis (d.b.) and increased rainfall in 2008 seemed to 
explain the difference in quadratic regression lines for protein levels in soybean. Although a difference 
existed, protein means for 2008 and 2010 only differed by 1.5% which was thought to be negligible in 
terms of using oilseed cake for livestock feed ration supplementation. However, based on the regression 
curve, a trend seemed to exist for both years in which 0% treatment means resulted in slightly higher 
protein concentrations than other irrigation treatments.  
Figure 4.6. 2008 & 2010 soybean protein results. 
 
 
87 
 
Oil content showed significant response to irrigation treatments in 2010 (figure 4.7). Given the 
low seasonal rainfall in 2010, oil content effects due to irrigation were heightened and the 75% irrigation 
treatment resulted in highest mean oil content. Irrigation treatments of 100% and 125% resulted in 
similar mean oil levels to the 75% treatment with only 0.3% difference among the three highest 
treatment means. There was an approximate 2.4% difference between lowest and highest oil 
concentration means for the six experimental treatments in 2010. If yield was normalized, this would 
equate to approximately 2.4% more biodiesel per hectare. For the yearly average yield of 2763 kg/ha 
and average oil content of 20.0%, a difference of 59 L/ha would result from 2.4% more oil.   
Figure 4.7. 2008 & 2010 soybean oil results. 
 
FFA values for all treatments and both years were not significantly different. 2008 and 2010 
mean FFA concentrations were 0.4% and 0.6%, respectively. These values were well below the accepted 
limit for base catalyzed transesterification (1.0%) and suggested no issues with biodiesel production as a 
88 
 
result of FFA. No soybeans were grown in the experimental energy crop rotation in 2009. However, 
soybeans were obtained from a farmer in the geographic vicinity of the experimental plots for use in 
another study. The FFA concentrations of these soybeans were on the order of 20 times higher than 
those reported in this section. High FFA levels were attributed to conditions experienced in 2009 cotton 
crop. If soybeans are exposed to these extremely wet conditions once the beans are fully formed, base 
catalyzed transesterification will most likely be difficult to effectively perform, thus resulting in poor 
quality fuel or conversion failure.   
4.2.3 Canola 
Table 4.2 highlights results of canola to crop rotation. Because canola was grown as a winter 
crop, no irrigation was applied post emergence. Differences were expected to be as a result of rotation; 
thus, no effects as a result of irrigation treatments were found during the study. For this experiment, 
canola was grown two different years, and no significant differences were attributed to the fact that the 
experiment was too recently established to investigate effects of rotation. Therefore, LSmeans and 
standard error were reported on a yearly basis for each parameter. 
Average canola yield in 2010 was over double 2008 mean yield. Rainfall data during the 2007-
2008 canola growing season (November to May) was 67 cm as opposed to 84 cm for 2009-2010 
growing. FFA difference between years was only 0.3% on average and in terms of biodiesel production, 
both years FFA concentrations were acceptable to achieve satisfactory ester conversion. Oil and protein 
differences were similar between years as well. Thus, yield was the limiting factor in biodiesel 
production on an area basis. In 2010, theoretical biodiesel yield was 1,026 L/ha, with only 517 L/ha 
calculated for 2008. Because year was considered a nuisance factor and cannot be repeated, differences 
in canola yield were attributed to rainfall; ultimately resulting in substantial theoretical biodiesel yield 
difference among years. 
 
89 
 
Table 4.2. 2008 & 2010 canola results. 
  2008   2010 
Parameter LSmean SE 
 
LSmean SE 
Plant density, plants/ha 1,388,096 134,497 
 
1,232,827 90,677 
Yield, kg/ha 1293.3 128.1 
 
2673.2 119.9 
FFA, % 0.8 0.1 
 
0.5 0.0 
Oil, dry matter basis, % 44.9 0.3 
 
43.1 0.2 
Protein, dry matter basis, % 24.5 0.1 
 
22.7 0.2 
4.2.4 Irrigation Summary 
Experiments of Sprecht et al. (2001), Sims et al. (1993), and Ibrahim and Kandil (2007) found 
that oil concentration decreased or remained unchanged with irrigation. Results of this study showed 
similar oil concentration ranges, however, oil concentration increased with irrigation during 2010. 
Protein concentrations decreased with increasing irrigation in soybeans and cotton; a finding that 
contrasted Boydak et al. (2002), Bellaloui and Mengistu (2008), and Ibrahim and Kandil (2007). Although 
as with oil concentration, overall percentages were within the range of other experimental studies. Lee 
et al. (2008) and Boydak et al. (2002) found that irrigation had no significant effect on FFA. Conversely, 
Taylor et al. (1991) reported that rainfed conditions increased FFA. Both scenarios were found in this 
study with 2009 cotton showing highest FFA for 0% irrigation. Nevertheless, all researchers agreed that 
yield was positively influenced by irrigation but that cultivar differences, growing season, geographic 
location, and agronomic practices all impact oilseed constituents (Boydak et al., 2002 and Bellaloui and 
Mengistu, 2008). 
4.3 Review of Biodiesel Production Procedures 
Table 4.3 provides a partial list of various biodiesel production methods that have been 
investigated in terms of cost effectiveness while meeting a desired conversion efficiency of 95%. These 
production methods were gathered from various literature in an attempt to determine the most feasible 
method that a farmer could utilize. The table was ordered based on chemical expense with the 
assumption that a farmer?s most inhibiting factor would be production cost. Results were normalized for 
90 
 
a 208-L batch of biodiesel but did not include overhead, equipment, or operational costs. The variability 
of these costs was difficult to ascertain as they may differ from farm to farm. From these results, the 
best-practice method could be interpreted in several ways. No data was included that resulted in an 
ester conversion efficiency lower than 95%, but if conversion efficiency is the most desired factor of 
biodiesel production, 1 wt% sulfuric acid with a 9:1 molar ratio of methanol to oil may be the preferred 
method of production. These levels provided the highest ester conversion efficiency of 99% after a 19-
hour reaction time at a cost of $0.24/L. While this method seems to yield the highest ester 
concentration, it has a relatively long reaction time per batch and may lead to engine corrosion if trace 
amounts of sulfuric acid remain in the biodiesel during use. Several methods reported reaction times as 
short as one hour; however, they are not equal in other regards. Base catalyzed transesterification 
seems to, on average, require the shortest reaction times.  
 
Table 4.3. High ester yielding biodiesel production procedures with low chemical costs. 
Catalyst 
Amount 
(%) Alcohol 
Amount 
(MR)* 
Reaction 
time (hr) 
Conversion 
rate (%) 
Chemical 
cost/liter ($)? Researcher? 
NaOH 1 methyl 6:1 4 >96 0.16 a 0.3 ethyl 12:1 1 97.2 1.31 d 
KOH 
1.4 methyl 6:1 4 >96 0.21 b 
1 ethyl 12:1 1 95.6 1.35 d 
1.07 20:1 NA 98 2.22 e 
H2SO4 
1 
methyl 
9:1 19 99 0.24 c 
3 6:1 96 95.1 0.25 c 
5 6:1 
48 
95 0.33 c 
3 20:1 96 0.56 c 
3 30:1 98.4 0.79 c 
3 ethyl 6:1 48 95.8 0.77 c 
*MR denotes molar ratio of alcohol to oil 
?based on 2010 chemical costs in Alabama 
?Freedman et al., 1984aa; Coteron et al., 1997b; Canakci and Van Gerpen, 1999c; Kucek et al., 
2007d; Joshi et al., 2008e 
 
 
Sodium hydroxide or potassium hydroxide at an amount of 0.3% and 1%, respectively, coupled 
with ethyl alcohol at a molar ratio of 9:1 seems to require the shortest reaction times that yield >95% 
91 
 
ester conversion with a cost ranging from $1.31 to $1.35/L (price based on 2010 chemical cost). With 
only a 3 hour longer reaction time and an ester conversion efficiency closely correlating to the prior 
method, 1% NaOH with 6:1 molar ratio of methanol to oil or 1.4% KOH with 6:1 molar ratio of methanol 
seems to be the most viable option for an Alabama farmer. The cost associated with each of these 
methods would be $0.16/L or $0.21/L for a 208 L batch, respectively. 
According to Freedman et al. (1984a) a 6:1 molar ratio of alcohol to oil should be used. Ratios 
greater than this value do not increase yields, however, they complicate ester and glycerol recovery, and 
add costs to alcohol recovery. From table 4.3, two methods using a 6:1 molar ratio of methanol to oil 
appeared to be the most economic and yield conversion efficiencies >96%. Freedman and Pryde (1982) 
reported that acid catalysis was much more effective than base catalysis when FFA levels exceed 1%.  
Base catalyzed transesterification seems to be the most feasible method of biodiesel production 
for an average Alabama farmer because of its relative ease and cost. While Joshi et al. (2008) 
determined that FAEE (fatty acid ethyl ester) may have enhanced low temperature properties in 
comparison to FAME (fatty acid methyl ester) and that a maximum yield of 98% ester could be obtained 
at a KOH level of 1.07% wt/wt and ethanol to oil ratio of 20:1, it may not be the most feasible for a 
farmer. This method may work for a farmer who has access to ethanol, but in Alabama, methanol is 
incomparably cheaper with comparable results to that of using ethanol.  
Coteron et al. (1997) found that a KOH level of 1.4% wt/wt produced the optimal ester 
conversion efficiency but any concentration greater, produced lower ester yields because of the 
presence of soaps, which prevents ester layer separation. From a cost perspective, this method was 
feasible, though not the most economical. NaOH seems to perform comparably at a more desirable cost. 
However, ester yield may be increased with a method reported by Bradshaw (1942) in which alcohol 
was added in three or four portions to obtain a 98% ester yield. He found that a three to four step 
reaction could aid in alcohol molar ratio reduction from 4.8:1 to 3.3:1. Bradshaw (1942) along with other 
92 
 
researchers noted that molar ratios greater than 5.25:1 or 6:1 interfered with gravity separation of the 
glycerol and added useless expense to the separation (Fuege and Gros, 1949; Gauglitz and Lehman, 
1963; Lehman and Gauglitz, 1966). These studies also found that NaOH catalyst increased from 0.2%-
0.8% at 6:1 molar ratio of methanol liberated significantly more glycerol yield. Similarly, Freedman et al. 
(1984a) found that 1% sodium hydroxide at FFA concentrations less than 0.5% yielded 96% to 98% 
conversion for methyl alcohol in 1 hr. While this result may not be the case for all oilseeds in this 
experiment, it seems to be a feasible option for soybeans and possibly cottonseed with achievable ester 
yields between 96% and 98% by simply increasing the reaction time several hours. 
Table 4.4 provides a partial list of biodiesel production methods published by researchers that 
was investigated in this study. A complete list can be viewed in Appendix A. These methods met the 
desired criteria of 95% conversion efficiency although all methods are not necessarily recommended as 
best practice methods. When these methods were analyzed by oil type, it was obvious that soybean oil 
has been the most widely researched oil type. That can be attributed to the fact that soybean is the 
most frequently oilseed crop grown in the US for food purposes and thus researched for possible 
biodiesel production.  The highest conversion efficiency (98.4%) was experienced when using a 30:1 
methanol to oil molar ratio and 3.0% sulfuric acid catalyst, a reaction time of 48 hours and a reaction 
temperature of 60 oC. Generally, most studies obtained similar results with a more conservative molar 
ratio of 6:1 and similar practices to this method in terms of reaction temperature and duration. For 
instance, when using a cheaper, less harmful chemical like sodium hydroxide, 98.0% conversion 
efficiency was obtained for soybean oil using 1.0% sodium hydroxide, a 6:1 molar ration of methanol to 
oil, reaction time of 4 hours and reaction temperature of 60 oC. When reducing the reaction time of this 
exact experiment to one hour, only one percent drop in conversion efficiency was experienced. When 
considering the utility requirement for the additional three hours of reaction time, the advantage was 
negated. Likewise, another study used one percent sodium hydroxide, 6:1 molar ratio of methanol to oil, 
93 
 
reaction temperature of 45 oC and reaction time of one hour and found a 97% ester conversion in 
soybean oil. The reduction in temperature of 15 oC for this scenario has the potential to drive the cost of 
processing down even further, excluding the fact that methanol and sodium hydroxide are the least 
expensive input chemicals for transesterification in the southeast US. Similarly, 0.5% sodium methoxide  
Table 4.4. Published biodiesel production procedures based on oil type. 
Oil Type 
Catalyst type 
(wt.%) 
Alcohol 
(MR)* 
Reaction 
time (hr) 
Reaction 
Temp. (C) 
Conversion 
Efficiency (%) Researcher? 
Soybean 
NaOH (0.3) EtOH (12:1) 1 30 96.8 1 70 97.2 
NaOH (0.65) EtOH (9:1) 1 50 95.8 1 
NaOH (1.0) MeOH (6:1) 
0.2 
60 
95.0 
3 0.5 96.0 
1 96.0 
2 
32 
96.0 
3 4 98.0 
20 98.0 
NaOCH3 (0.5%) 
MeOH (5:1) 1 60 95.0 3 
MeOH (6:1) 0.5 60 97.0 3 1 98.0 
KOH (1.0) EtOH (12:1) 1 30 95.1 1 70 95.6 
H2SO4 (1.0) n-BuOH (30:1) 3 114 95.0 3 
H2SO4 (5.0) 
MeOH (6:1) 48 60 95.0 2 96 95.1 
MeOH (20:1) 48 60 97.0 2 
MeOH (30:1) 98.4 
EtOH (6:1) 48 75 95.8 2 
Sunflower  
NaOCH3 (0.5%) MeOH (6:1) 1 60 98.0 3 
 EtOH (3:1) 0.5 75 95.0 3 
 EtOH (6:1) 1 97.0 
 n-BuOH (6:1) 1 114 95.0 3 
Peanut NaOCH3 (0.5%) MeOH (6:1) 1 60 97.0 3 
Cottonseed NaOCH3 (0.5%) MeOH (6:1) 1 60 95.0 3 
Canola KOCH3 (1.59) MeOH (4.5:1) 0.167 50 95.8 4 
*MR denotes molar ratio of alcohol to oil 
?Kucek et al., 20071; Canakci and Van Gerpen, 19992; Freedman et al., 1984a3; Singh et al., 20064 
 
was used for soybean oil transesterification at 6:1 molar ratio of methanol to oil, reaction temperature 
of 60 oC and reaction time of 1 hour with 98% conversion efficiency, however, sodium methoxide was a 
94 
 
bit more cost prohibitive as opposed to sodium hydroxide. Advantages of sodium methoxide will be 
discussed next. 
Sunflower oil was the second most frequent oil type to appear in published literature reviewed. 
Sodium methoxide was used as the catalyst in experiments outlined. It was determined that 98% 
conversion efficiency could be experienced as a result of 0.5% sodium methoxide, 6:1 molar ratio of 
methanol to oil, reaction time of one hour and reaction temperature of 60 oC. These results fair 
favorably to the soybean oil results. Of interest, some researchers chose to use sodium methoxide as 
opposed to sodium hydroxide. When methanol and sodium hydroxide are mixed in a transesterification 
reaction vessel, water can be a product of the reaction to form sodium methoxide as the methanol has 
the potential to be slightly acidic. Water was also a transesterification inhibitor as reported by multiple 
researchers. Freedman et al. (1984a) selected chemical grade sodium methoxide purchased from a 
commercial source as the catalyst in their study because the water had already been removed and was 
not a potential inhibitor of the reaction.  
Several methods of transesterifying peanut, canola and cottonseed oil were investigated and 
can be viewed in Appendix A, however, only one method for each oil type met the 95% conversion 
efficiency threshold required for the developed list. Of particular interest was the catalyst used for 
canola oil in table 4.7. Singh et al. (2006) used potassium methoxide at a concentration of 1.59% and 
methanol at a molar ratio of 4.5:1 to oil. The reaction time was only 10 minutes and the resulting 
conversion efficiency was surprisingly almost 96%. However as previously stated, sodium hydroxide is 
still the cheaper catalyst with similar results experienced at a longer reaction time. A similar reaction, 
which did not qualify for inclusion in table 4.7, used soybean oil with 1.0% sodium hydroxide at a 
reaction temperature of 60 oC, 6:1 molar ratio of methanol to alcohol and reaction time of 6 minutes. 
This method required less time than the previously discussed reaction, but yielded only 94% conversion. 
95 
 
4.4 Vegetable Oil Storage Experiment 
4.4.1 Triglyceride Hydrolysis 
Once soybean oil and canola oil samples were mechanically extruded and WVO collected for this 
experiment, all four replications of each oil type were initially tested for water content. It was known 
that water content could have adverse effects on vegetable oils and could expedite triglyceride 
hydrolysis (FFA accumulation) in the experimental samples. Table 4.5 reports the initial water content 
results. Control samples were also tested for water content after autoclaving for sterilization. These 
results are not shown; however average water content was not significantly different in the control 
samples after they were autoclaved.  
During the approximate nine months of vegetable oil storage in four-liter HDPE sealed 
containers, WVO showed the highest percentage increase of FFA, approximately 68%. Surprisingly, 
soybean oil followed with approximately 50% FFA increase and canola oil resulted in least percent FFA 
increase, 43% (figure 4.8). Statistical analysis of the data revealed that slopes of each oil type were 
significantly different (p<0.0001). Each oil type oxidized at a different rate. Linear regression indicated a 
strong correlation existed for WVO and canola oil. The linear equations of FFA hydrolysis over time can 
be used with confidence in predicting hydrolysis of stored WVO and canola oils in sealed, airtight HDPE 
containers. 
Researchers typically recommend biodiesel feedstock FFA concentrations below 2%. Only 
soybean oil was consistently below 2% for the duration of this experiment. WVO and canola oil FFA 
concentrations initially were 2.8% and 2.3%, respectively. On the other hand, canola oil biodiesel data 
from Section 4.7.2 may suggest that these elevated FFA concentrations in WVO and canola oil could be 
acceptable for base catalyzed transesterification and still yield a relatively high quality fuel. Although 
some replications of canola oil failed during the transesterification experiment, the highest FFA 
96 
 
concentration experienced in this experiment was approximately 40% lower for WVO and 200% lower 
for canola oil than experienced in the degummed oil transesterification experiment.  However, it was 
speculated that additional factors, besides FFA concentration alone, led to transesterification failure in 
canola oil samples in the degummed oil transesterification experiment. Still, all 10 canola oil samples 
that were drawn from the top layer of the semi-sealed storage container with average FFA 
concentration of 6.2% transesterified. 
Table 4.5. Initial water content of samples for the oil storage experiment. 
 
Water (wt%) 
Sample Mean SD 
Canola oil  0.71 0.18 
WVO 1.05 0.54 
Soybean oil 0.84 0.40 
Canola oil control 0.42 0.16 
WVO control 1.30 0.08 
Figure 4.8. FFA accumulation in vegetable oil samples over time. 
 
97 
 
Of noteworthy importance was the difference in FFA concentrations of canola oil in both 
experiments. Although this level was not a factor in the experimental designs of either experiment, it 
was interesting to observe these differences. On average FFA concentration in canola oil during this 
storage experiment did not exceed 3.3%. However, average FFA concentration of the canola oil control 
samples in Section 3.7.2 were 100% greater. These canola oil samples were from the same batch of 
oilseeds, seeds were stored for the same duration, oil was mechanically extruded the same day, and oil 
was stored for the same duration in the same environment. Yet, vegetable oil storage samples were 
stored in sealed, air-tight HDPE containers and oil for the degummed oil transesterification experiment 
was stored in semi-sealed containers that were not as air-tight as the HDPE four-liter jugs.  
4.4.2 Microbial Contamination 
Data analysis for this experiment provides relative absorbance measurements over time. Relative 
absorbance measurements are due to the fact that repeatability is heavily dependent on the volume of 
sample tested. Several tests during this experiment resulted in slightly different volumes tested due to 
pipette models used, thus highly repeatable results were not obtained. However, relative absorbance 
measurements at 550-nm wavelength indicated that over time, absorbance did not change significantly 
(figure 4.9). In vegetable oil samples of soybean, canola, and waste oil, absorbance values remained 
relatively the same and even dropped slightly as storage time progressed. Also, control samples of WVO 
and canola oil resulted in absorbance values not significantly different from non-control samples. This 
result indicated that there was no microorganism activity in the oil samples prior to storage, which was 
an expected result. De-ionized water analysis with the ultra-violet spectrophotometer produced 
consistent results over the storage period. This consistency led to the conclusion that the machine was 
providing steady readings throughout the duration of the experiment.  
 
 
98 
 
Figure 4.9. Experimental sample data from absorbance testing over time. 
 
Figure 4.10 illustrates bacteria positive control analysis at 550-nm wavelength in water and 
WVO. If the experimental samples had contained microorganisms, increasing absorbance peaks at 550 
nm would have been expected that might have correlated somewhat to the positive control graph. Also, 
the equation of the linear regression fit from this graph would have allowed for calculation of the 
concentration of microorganisms in the experimental samples with high coefficient of determination (r2 
0.9491). 
 
 
 
 
99 
 
Figure 4.10. Bacteria positive control sample analysis from absorbance testing  
4.5 Methanol Recovery Efficiency Experiment 
The methanol recovery experiment was conducted with two treatments 1) standard practice 
method of methanol recovery recommended by the manufacturer and 2) vacuum pump assisted 
methanol recovery, creating negative pressure to encourage methanol vapors to move to the condenser 
and be recovered as liquid methanol. To ensure the accuracy and repeatability of the evaporative 
method, a control experiment was performed first. Table 4.6 reports the results from this method. 
Samples similar to experimental samples were chosen as control samples with the tests replicated three 
times. Changes in initial weight and final weight resulted in only 0.38% error for the worst scenario. 
From these results, it was determined that this method could provide reasonably accurate results in 
determining methanol concentrations from experimental samples. 
 
 
100 
 
Table 4.6. Evaporative method reliability for methanol determination. 
 
-------Initial Values------- 
 
Calculated final values 
Rep* RBD oil (g) MeOH wt. (g) Total (g) MeOH (%) Final wt. (g) MeOH wt (g) % Error 
1 37.51 12.46 49.97 24.93 37.53 12.44 0.16 
2 37.60 12.65 50.25 25.17 37.56 12.69 -0.32 
3 37.20 13.09 50.29 26.03 37.15 13.14 -0.38 
Mean 37.44 12.73 50.17 25.38 37.41 12.76 -0.18 
SD 0.21 0.32 0.17 0.58 0.23 0.35 0.30 
* Samples were Refined, Bleached & Deodorized (RBD) canola oil + methanol 
 
Table 4.7 illustrates the average amount of methanol contained in different phases of the 
transesterification reaction, methanol recovered from raw biodiesel and the monetary gain or loss as a 
result of recovery.  It was determined that of the 17.9 L of methanol added to the WVO for 
transesterification, approximately 10.6 L were consumed by the reaction for the formation of fatty acid 
methyl esters. The theoretical consumption of methanol for a 90-L batch is 10.9 L, not significantly 
different from the average calculated consumption. On average, 7.3 L of methanol remained in the 
biodiesel/glycerol mixture with almost 40% of that volume was drained off with the glycerol layer in the 
biodiesel prior to methanol recovery (table 4.7).  
Ideally, methanol should be recovered from the glycerol layer as well to retain maximum 
efficiency in the process and in order to recycle the methanol to future batches. However, from the 
evaporative experiment, glycerol in the absence of methanol became extremely viscous and cleaning 
the round bottomed flask for use with the next evaporative sample was difficult as the glycerin solidified 
and adhered to the flask. Likewise, after it was determined that vacuum pump assisted methanol 
recovery proved beneficial in recovering methanol from biodiesel, the method was employed by Auburn 
University Biosystems engineering staff. Apparently small amounts of glycerol that were not drained 
with the initial glycerol and/or a minute amount of post reaction occurred during methanol recovery. 
After the raw biodiesel cooled from the recovery process, the piping infrastructure became clogged 
101 
 
from this coagulated glycerin and required heating to dislodge the solidified glycerin and free the piping 
system to continue the process.  Methanol recovery prior to draining glycerol appears to be a simple 
resolution to recover maximum amounts of methanol, but for the reasons mentioned earlier, it is not 
recommended. An alternative methanol recovery from glycerol once removed from the reactor would 
be advantageous in recovering excess methanol for recycling.  
Vacuum pump assisted methanol recovery resulted in an average 600% increase in methanol 
recovered as opposed to the standard procedure. Also, this removed residual methanol from the 
biodiesel which would have otherwise lowered the flash point and could have caused safety issues when 
storing and using biodiesel in an engine. Residual methanol in biodiesel was lowered by 380% on 
average when using the vacuum pump assisted methanol recovery.  
Also highlighted in Table 4.7, are the economics of methanol recovery as a result of the two 
treatments. An average methanol savings of $1.28 per 90-L batch was experienced when using vacuum 
pump assistance versus standard recovery procedures. However, an average of $1.39 was lost with the 
standard methanol recovery procedure that could have been recovered with the vacuum pump 
assistance method. The average cost of operating the biodiesel processor for one 208-L batch, typical 
amount processed, with vacuum pump assistance was estimated at $1.36. When considering that $1.28 
was saved with vacuum pump assistance, the cost of processing a single batch can almost be retrieved 
in the recovered methanol that can be recycled for future use.  
 
 
 
 
 
 
102 
 
Table 4.7. Volumetric balance and economic analysis of methanol recovery experiment. 
  
 
Treatment  
  
 
Standard Vacuum Difference 
Methanol in glycerol (L) Mean 4.31 4.45 0.14 SD 0.18 0.35 
Methanol recovered from raw 
biodiesel (L) 
Mean 0.41 2.46 2.05 
SD 0.18 0.22 
Residual methanol in biodiesel 
post recovery (L) 
Mean 3.04 0.80 -2.24 
SD 0.15 0.14 
Dollars lost in unrecovered 
methanol*  
Mean $1.89 $0.50 ($1.39) 
SD $0.09 $0.09 
Dollars saved in recovered 
methanol* 
Mean $0.25 $1.53 $1.28 
SD $0.11 $0.14 
*Power usage not calculated but considered negligible. 
 
 
4.6 Phosphorus Removal from Vegetable Oil Experiment 
 Both crude extruded soybean and canola oils were degummed with one of three 
experimental treatments: hot water, citric acid or anion exchange resin. Oils in this experiment were 
extruded and stored in semi-sealed HDPE containers for approximately 7-8 months before degumming 
to simulate an actual on-farm scenario. There was a control group to verify phosphorus removal 
efficiency. Table 4.8 provides the results of phosphorus removal from canola oil. The canola control 
mean phosphorus content was 51 ppm, which was not significantly different than degumming 
treatments of hot water and anion exchange resin. However, degumming canola oil with citric acid 
proved to consistently decrease phosphorus content in canola oil by almost 50%. Lowest levels of 
phosphorus using citric acid treatment can be attributed to the fact that hot water and citric acid were 
used for the citric acid treatment as outlined by Brekke (1980). Hot water has been believed to remove 
hydratable phospholipids of the vegetale oil whereas citric acid will remove non-hydratable 
phospholipids. When hot water and citric acid methods are compared, it appeared a larger majority of 
the phospholipids removed from the canola oil were non-hydratable and as a result were removed with 
citric acid and not in the hot water treatment. ASTM Standard D 6751 (2007) requires phosphorus 
103 
 
content in biodiesel to be ?10 ppm and citric acid treatment alone remained unsatisfactory in meeting 
the standard requirements.  
Table 4.8. Results of phosphorus removal from canola oil. 
Treatment 
Phosphorus content (ppm) 
Mean? SD 
Control 51 a 1 
Citric acid 26 b 2 
Hot water 49 a 3 
Anion exchange resin 47 a 1 
? Means designated by the same letter are not significantly different (P<0.05). 
 Phosphorus removal in soybean oil seemed to be more favorable in oil responses to treatments 
(table 4.9). However, the soybean oil control mean was initially lower than canola oil. Each treatment 
was significantly different than the others, indicating that each had a significant impact on phosphorus 
removal within the soybean oil. As with canola oil, if hot water and citric acid treatments are compared, 
citric acid outperformed the hot water treatment. Likewise, this difference can be attributed to the fact 
that the citric acid treatment called for hot water and citric acid, removing both hydratable and non-
hydratable phospholipids. The lowest phosphorus content (24 ppm) was attained with the anion 
exchange resin, unlike canola oil; however citric acid results for each oil type had similar means.  
It was unclear why the anion exchange resin was successful at removing almost five times more 
phosphorus in soybean oil as compared to canola oil. The anion exchange resin was advertised by the 
manufacturer as being capable of removing proteins, phospholipids and cations. Cations can exist as FFA 
and FFA values in experimental canola oil samples were almost 1700% higher than experimental 
soybean oil samples. However, the anion exchange resin was successful in reducing FFA in soybean and 
canola oils by 30% and 22%, respectively. FFA differences were a result of 7-8 months of storage in semi-
sealed HDPE containers, simulating an on-farm scenario. Anion exchange resin performance differences 
between oil types may possibly be attributed to higher FFA concentrations in canola oil, thus resulting in 
104 
 
poor phosphorus removal from canola oil. Protein content in each oil type was not known, but 
suspected to have an insignificant impact on anion exchange resin performance.  
Table 4.9. Results of phosphorus removal from soybean oil and biodiesel. 
Treatment 
Phosphorus content 
in oil (ppm) 
Phosphorus content 
in biodiesel (ppm) Difference 
(ppm) Mean? SD Mean? SD 
Control 45 a 1 24 ab 1 21 
Citric acid 27 c 0 23 ab 1 4 
Hot water 32 b 1 29 a 5 3 
Anion exchange resin 24 d 1 22 b 0 2 
? Means designated by the same letter are not significantly different within columns (P<0.05). 
 Samples of WVO collected from Auburn University dining facilities were also tested for 
phosphorus content. These oils were commercially refined, bleached, and deodorized (RBD) before use 
as frying oils and phospholipids should have been substantially reduced in these processes. Results 
showed phosphorus content in WVO averaged 37 ppm with a standard deviation of 3 ppm. Because no 
oil samples were attained and tested for phosphorus content before frying, no concrete conclusions can 
be drawn as to where phosphorus was introduced into the WVO. However, it could have been the result 
of phosphorus transfer from food fried in the oils. The initial concentration of phosphorus in WVO, 
before frying and after commercially degumming (RBD), should have been much less than 10 ppm 
because phospholipids in transported oils can cause clogging and/or build-up issues in pipelines or tank 
walls, respectively. 
4.7 Biodiesel Quality Experiment 
4.7.1 Biodiesel Logic, Inc Biodiesel Processor 
This experiment was designed as an addendum to Section 4.5 and therefore data presented in 
table 4.10 was compiled in the same manner as the methanol recovery experiment. As discussed in 
Chapter Three, proper mixing and interaction among WVO, methanol and sodium hydroxide was 
105 
 
theorized to be insufficient for the current configuration of this processor. The data in table 4.10 
suggests that theory is plausible. Average mono-glyceride and di-glyceride values reported are fairly 
small but suggested complete transesterification of these constituents did not occur. Likewise, average 
tri-glyceride values were higher than mono-glyceride or di-glyceride values, indicating that on average, 
2% to 3% of the mass of WVO was subjected to no transesterification.  
ASTM Standard D 6751 (2007) requires total glycerin and free glycerin not exceed 0.24% mass 
and 0.02% mass, respectively.  None of the treatments or resulting sample averages collected at various 
stages during the transesterification process met the requirement for total glycerin set forth by ASTM 
Standard D 6751. However, free glycerin requirements were met for samples collected after dry washing 
with AMBERLITE? BD10DRY. This resin, as advertised by the manufacturer, should remove free glycerin 
among other constituents from biodiesel. This data supports manufacturer claims as free glycerin values 
were well below maximum limitations of ASTM Standard D 6751 (2007). Conversion efficiencies 
averaged around 96% and took into account mono-glyceride, di-glyceride and tri-glyceride values for 
each respective replication. Further, water content and FFA in WVO have the ability to inhibit complete 
transesterification. Therefore, samples of WVO from all samples were tested for water using ASTM 
Standard E 203-01 (2001) and FFA using ASTM Standard D 9555-95 (2008). Results concluded that water 
content in all sample averages was less than 0.2% suggesting no adverse effects on transesterification 
due to water content in the WVO. FFA values for all oil samples ranged from 1.9% to 2.3%. 
 
 
 
 
 
 
 
 
 
 
 
Table 4.10. Quality of biodiesel produced from the Biodiesel Logic, Inc. processor. 
Treatment* 
Biodiesel 
sample 
Conversion 
(wt%) 
Total Glycerin 
(wt%) 
Free Glycerin 
(wt%) 
Mono-
glycerides (wt%) 
Di-glycerides 
(wt%) 
Tri-glycerides 
(wt%) 
Mean SD Mean SD Mean SD Mean SD Mean SD Mean SD 
Standard Raw biodiesel 97.38 a 1.42 0.38 a 0.20 0.07 ab 0.04 0.09 b 0.06 0.58 a 0.29 2.00 a 1.11 
Vacuum 97.31 a 1.51 0.49 a 0.21 0.17 a 0.06 0.09 b 0.04 0.55 a 0.21 2.09 a 1.33 
Standard Post methanol 
recovery 
97.47 a 1.24 0.32 a 0.24 0.09 ab 0.09 0.17 a 0.05 0.63 a 0.26 1.79 a 0.97 
Vacuum 96.31 a 2.08 0.57 a 0.21 0.05 ab 0.04 0.12 ab 0.03 0.69 a 0.21 2.94 a 1.90 
Standard Post dry wash 95.63 a 2.73 0.51 a 0.31 0.00 b 0.00 0.06 b 0.02 0.95 a 0.45 3.41 a 2.30 
Vacuum 96.02 a  0.88 0.47 a 0.10 0.01 b 0.01 0.07 b 0.03 0.77 a 0.23 3.20 a 0.81 
Means assigned the same letter within columns are not significant (P<0.05). 
*Treatments correspond to section 4.5. 
  
10
6 
107 
 
4.7.2 Degummed Oils 
There were three degumming methods selected for this experiment based on safety and 
practicality for a farmer. Table 4.9 highlights phosphorus concentrations in soybean biodiesel as a result 
of transesterification. As expected, phosphorus concentrations decreased. Greatest difference in 
phosphorus concentration was between soybean control oil and biodiesel. However, the phosphorus 
concentration of the soybean control biodiesel was not significantly different from other treatments. 
Degummed oil phosphorus concentrations were significantly different from the control treatment, but 
biodiesel phosphorus concentrations did not follow this trend. Although, hot water degummed soybean 
oil resulted in highest phosphorus concentration next to the control and biodiesel from this degummed 
soybean oil followed suit and exhibited highest phosphorus concentration. Likewise, anion resin 
degummed oil resulted in lowest phosphorus concentration as did biodiesel from anion resin 
degummed oil. Nonetheless, minimum ASTM D 6751 (2007) requirements for phosphorus concentration 
were not met. All mean phosphorus concentrations of biodiesel samples were between 22 ppm and 29 
ppm. This may suggest that a threshold limitation existed in which transesterification removed 
phosphorus to a certain extent but was ineffective in removing phosphorus beyond that point.  
Because some replications of canola oil did not transesterify, phosphorus results from the 
canola biodiesel data were not summarized but presented in table 4.11. Issues with transesterification 
of canola oil will be discussed further in this section, but anion exchange resin degummed canola 
biodiesel phosphorus concentrations were fairly consistent and lower than the mean of anion exchange 
resin degummed oil suggesting that some phosphorus was removed as a result of transesterification 
that was not removed during degumming alone. Other phosphorus content in biodiesel data was 
sporadic and inconsistent with the phosphorus value reported for citric acid degummed biodiesel higher 
than the mean of the degummed oil itself.  
 
108 
 
 Table 4.11. Phosphorus content in degummed, transesterified canola oil. 
Treatment Rep 
Mean phosphorus content 
in biodiesel (ppm) 
Control 
1 -- 
2 45 
3 50 
Citric acid 
1 32 
2 -- 
3 -- 
Hot water 
1 -- 
2 38 
3 39 
Anion exchange resin 
1 43 
2 43 
3 41 
 
Data in table 4.12 partially summarizes results of soybean biodiesel quality from degummed 
vegetable oils tested in this experiment. FFA concentrations for soybean oil were consistent, on average, 
for degumming methods of citric acid and hot water, however, the average FFA concentration for the 
control and anion resin degummed samples were lower and significantly different from the previous 
two methods. Water was known to aid in hydrolysis of triglycerides, but FFA accumulation is typically 
experienced in the presence of heat as with frying oils. Lower average FFA concentration in the control 
sample was attributed to the fact that no water was introduced to the samples as in citric acid and hot 
water degummed oil samples. On the other hand, AMBERSEP? BD 19 is advertised to remove cations, 
proteins and phospholipids. If FFA existed in the oil as a cation, the lesser value experienced, 0.20%, can 
be justified based on manufacturer performance specifications of the anion resin. Because samples in 
this experiment were produced on a micro scale and with 200 rpm agitation, adequate interaction 
among oil, methanol and sodium hydroxide are believed to have resulted in higher conversion 
efficiencies, 99.6% on average for soybean and WVO, than experienced in the methanol recovery 
biodiesel quality experiment. However, it is important to note that the molar ratio of alcohol to oil was 
6:1 and 1 wt% sodium hydroxide was used in this experiment as opposed to 4.8:1 molar ratio of 
109 
 
methanol to oil and 0.7 wt% sodium hydroxide for the methanol recovery biodiesel quality experiment. 
The latter chemical amounts were recommended by the manufacturer of the Biodiesel Logic, Inc. 
biodiesel processer. Total glycerin concentrations in table 4.12 support the higher conversion 
efficiencies experienced and were considerably lower than those reported for the methanol recovery 
biodiesel quality experiment. Total glycerin average concentrations for each degumming method met 
requirement of ASTM Standard D 6751 (2007) of <0.24 wt.%. Likewise, mono-glyceride and di-glyceride 
values were very low as well. Tri-glyceride values were not even detected during GC analysis, further 
supporting extremely high conversion efficiencies and suggesting nearly complete transesterification of 
the respective vegetable oil. However, free glycerin values were high and did not meet ASTM Standard D 
6751 (2007) requirements. This low level was attributed to the fact that the samples were not washed 
to remove free glycerin before analysis. This result further solidified the importance of washing biodiesel 
to remove free glycerin and other impurities in order to ensure quality.  
WVO was also used as a control in this experiment both because it had previously been 
commercially refined before being used as frying oil and for comparison purposes to results obtained in 
Section 4.5. This oil was acquired from dining facilities just as the feedstock used in the methanol 
recovery biodiesel quality experiment. Even though FFA concentration was 2.3% on average, conversion 
efficiency was not inhibited and the oil performed similarly to soybean oil in terms of transesterification.  
This result provides further justification to the fact that agitation and interaction among constituents in 
the Biodiesel Logic, Inc. processor were inadequate.  
Canola oil was also tested in this experiment under the exact conditions of soybean oil. It was 
mechanically extruded in the same time frame as soybean oil, stored in the same location and in 
containers of the same material. Also, both oils were stored for the same time interval, approximately 
eight months, before degumming and transesterification experiments ensued. However, results from 
canola oil were not as consistent as those obtained from soybean. In all degumming practices, except 
110 
 
with AMBERSEP? BD19, at least one replication failed to transesterify and either solidified in the 
erlenmeyer flask or appeared unchanged as a result of transesterification. Although, when the oil 
appeared unchanged, half the amount of original methanol and sodium hydroxide could be added to the 
oil, the reaction performed again, and glycerol separation occurred. For this reason, averages were not 
calculated from this experiment and the data was reported unsummarized in table 4.13. However, 
important trends and findings can still be inferred from this data. 
Of note were the extremely high FFA concentrations of all replications. Hydrolysis due to 
extended storage in non-air tight HDPE containers was attributed to excessive hydrolysis; however, 
hydrolysis in soybean oil was substantially less than canola. For canola oil, just as in soybean, 
AMBERSEP? BD19 consistently lowered FFA values and all replications of canola oil degummed with 
AMBERSEP? BD19 transesterified with excellent quality results. Although FFA values were three times 
higher than researcher recommended base catalyzed transesterification ceiling limits, samples that did 
transesterify resulted in nearly complete conversion with exception to replication three of the canola 
control oil in which only 62% of triglycerides converted to fatty acid methyl esters. 
The exact reason canola oil replications did not consistently transesterify is unknown. However, 
once this problem was realized, measures were taken to attempt to answer this question. Samples 
collected from the storage container to be used in the degumming and transesterification experiments 
were collected from near the bottom of the container. Therefore, oil stratification was considered as a 
culprit of inconsistency in the data. It is speculated that proteins, gums and other impurities with greater 
density than oil moved towards the bottom of the storage container due to gravity (Burkhalter, 1976) 
and may have resulted in transesterification failure of the canola oil. Thereby, 10, 95-g canola oil 
samples were collected from the remaining oil stored in the bottom of the original canola oil storage 
container. Likewise, 10, 95-g canola oil samples were collected from another bucket of the exact same 
oil, mechanically extruded at the same time and stored in the same container type and for the same 
111 
 
time duration. However, oil in this bucket had not yet been used for experimental analysis and the 
samples were drawn from the top layer of oil. FFA concentration was tested for each oil sample from 
both buckets with an average of 6.62% for samples drawn from the bottom of the storage container and 
6.24% for samples collected from the top layer of the other storage container of canola oil. 
Transesterification procedures were conducted on these 20 samples just as in previous micro-scale 
transesterification experiments. All 10 samples from the top layer of the second canola oil storage 
container consistently transesterified. However, the 10 samples drawn from the bottom layer of the first 
canola oil storage container failed to transesterify. Although there was a minor difference in FFA 
concentration, transesterification failure may suggest that oil stratification occurred in the canola oil 
rendering the lower layer unusable for biodiesel production.  
 
 
Table 4.12 Quality of biodiesel produced from degummed vegetable oils (soybean and WVO). 
Oil Treatment 
Oil FFA (%) 
Conversion 
(wt%) 
Total Glycerin 
(wt%) 
Free Glycerin 
(wt%) 
Mono-glyceride 
(wt%) 
Di-glyceride 
(wt%) 
Tri-glyceride 
(wt%) 
Mean SD Mean SD Mean SD Mean SD Mean SD Mean SD Mean SD 
Soybean 
Citric acid 0.38 a 0.00 99.84 a 0.03 0.06 a 0.07 0.68 a 1.07 0.07 a 0.06 0.05 a 0.01 0.00 a 0.00 
Hot water 0.38 a 0.00 99.88 a 0.09 0.07 a 0.08 0.78 a 1.19 0.06 a 0.05 0.06 a 0.00 0.00 a 0.00 
AMBERSEP
? BD19 0.20 b 0.04 99.88 a 0.01 0.10 a 0.11 1.32 a 1.56 0.08 a 0.01 0.03 b 0.01 0.00 a 0.00 
Control 0.26 b 0.04 99.87 a 0.00 0.06 a 0.08 0.71 a 1.18 0.01 a 0.00 0.05 b 0.00 0.00 a 0.00 
WVO Control 2.32 0.09 99.87 0.01 0.01 0.00 0.03 0.00 0.04 0.00 0.05 0.01 0.00 0.00 
Means assigned the same letter within a column are not significant (P<0.05). 
WVO not included in statistical analysis of soybean biodiesel quality. 
 
Table 4.13. Quality of biodiesel produced from degummed vegetable oils (canola). 
Treatment Rep 
Oil FFA 
(%) 
Conversion 
(wt%) 
Total Glycerin 
(wt%) 
Free Glycerin 
(wt%) 
Mono-
glyceride 
(wt%) 
Di-glyceride 
(wt%) 
Tri-glyceride 
(wt%) 
Mean SD Mean SD Mean SD Mean SD Mean SD Mean SD 
Citric acid 
1 6.59 99.88 0.00 0.03 0.00 0.23 0.04 0.08 0.02 0.02 0.01 0.01 0.00 
2 6.67 -- -- -- -- -- -- -- -- -- -- -- -- 
3 7.20 -- -- -- -- -- -- -- -- -- -- -- -- 
Hot water 
1 6.59 -- -- -- -- -- -- -- -- -- -- -- -- 
2 6.67 99.54 0.00 0.07 0.00 0.27 0.06 0.11 0.00 0.07 0.00 0.17 0.02 
3 6.74 99.84 0.07 0.06 0.07 0.72 0.99 0.09 0.00 0.03 0.00 0.03 0.01 
AMBERSEP? BD19 
1 5.13 98.44 0.02 0.24 0.02 0.95 0.17 0.15 0.00 0.15 0.01 0.75 0.04 
2 5.52 98.07 0.07 0.37 0.06 2.29 0.96 0.15 0.00 0.19 0.00 0.95 0.02 
3 5.59 99.10 0.02 0.01 0.00 0.05 0.01 0.01 0.00 0.01 0.00 0.04 0.00 
Control 
1 6.51 -- -- -- -- -- -- -- -- -- -- -- -- 
2 6.82 95.57 0.05 0.70 0.04 2.82 0.04 0.08 0.01 0.51 0.04 2.18 0.18 
3 6.51 61.77 0.01 4.95 0.01 2.20 0.21 4.83 0.51 4.21 0.24 18.07 0.04 
*Data not included in this table is the result of sample failure during transesterification.
11
2 
113 
 
4.8 Mechanical Extruder Efficiency Analysis 
Mean mechanical screw press assessment data is outlined in table 4.14. The Henan Double 
Elephant mechanical screw press (1) was evaluated with three different oilseeds and soybean resulted in 
highest oil extraction efficiency. That is, the highest percentage of oil relative to total seed oil was 
extracted from soybean. Mechanical screw presses have been estimated to be capable of extracting up 
to 80 ? 85% of the total seed oil, thus results for soybean are favorable. However, sunflower and canola 
results were considerably lower in terms of oil extraction efficiency. Several factors were attributed to 
the lower results observed. For the Henan Double Elephant press, operator aptitude heavily dictates oil 
output. There are several movable components of this press that adjust position of the screw and 
spacing between the plates. Figure 4.11 shows an operator positioning the screw. These parts must be 
loosened to begin processing and when the mechanical screw press warms up, the parts are tightened 
to optimal operating conditions as perceived by the operator. Of course, these parameters are specific 
for different oilseeds and may be based on several factors (e.g. particle size, density and moisture 
content). 
Table 4.14. Henan Double Elephant model 6YL-120 (1) and Karl Str?hle SK60/2 (2) mechanical 
screw press assessment summary. 
Mechanical 
Screw Press Oilseed 
Moisture 
Content 
(%w.b.)* 
Oilseed 
input 
(kg/hr) 
Oil output 
(L/hr) 
Oilseeds 
in/oil out 
(kg/L) 
Oil 
Extraction 
Efficiency (%) 
1 
Sunflower NA 67.3 17.9 3.8 48.4 
Soybean 12.6 109.2 15.7 7.0 77.3 
Canola 6.9 150.4 22.4 6.7 34.1 
2 Canola 6.8 47.8 10.2 4.7 48.5 
* w.b. denotes wet basis. 
Sunflower moisture content was not available (NA).     
 
On the other hand, the Karl Str?hle mechanical screw press has fixed components and requires 
minimal user knowledge. Oilseeds are slowly fed into the press to bring it up to operating temperature, 
114 
 
and then the oilseed can be freely fed into the press from the hopper bag figure 4.12. This machine has 
a dual augering system and both sides can be used simultaneously. Augers for this press are oilseed 
specific and it is possible to press two different oilseed types simultaneously. In table 4.14, canola was 
the only oilseed type reported. If comparisons are made for extraction efficiencies for canola between 
the two mechanical screw presses, the Karl Str?hle press outperforms the Henan Double Elephant by 
approximately 41%. Again, it is speculated that the increased extraction efficiency is due to operational 
parameters. At some level and with hours of experience, extraction efficiencies of a devoted operator 
on the Henan Double Elephant may exceed those of the Karl Str?hle. However, it is not known how long 
this learning curve might take and still how much valuable oil might be lost in the process. 
Figure 4.11. Operator adjusting screw position on a Henan Double Elephant 6YL-120 mechanical screw 
press for optimal oil output. 
 
Another advantage that the Karl Strahle press holds over the Henan Double Elephant is operator 
efficiency. During testing and after machine warm-up, the only operator input was to empty oil and cake 
catchment containers and refill the hopper. The Henan Double Elephant required the manufacturing of a 
separate hopper and auger system that continually fed the press (figure 4.13). 
115 
 
 
Figure 4.12. Karl Strahle SK60/2 mechanical screw press. 
 
Figure 4.13. Hopper and augering system designed and built for the Henan Double Elephant 
mechanical screw press. 
116 
 
4.9 Economic Analysis of Small-scale Biodiesel Production Systems 
4.9.1 Auburn University?s Biosystems Engineering Biodiesel Production System 
Table 4.15 reports the economic analysis of Auburn University?s Biosystems engineering 
biodiesel production system on a yearly basis. For this analysis, collection site equipment at the dining 
facilities on Auburn?s campus cost $145 annually. This equipment was a requirement by the university 
and would not necessarily be required if this exact system were implemented on-farm.  Likewise, partial 
costs of other equipment such as storage equipment and spill containment, would likely not be incurred 
for an on-farm setup. The storage equipment cost for this analysis was calculated from list prices for 
new storage tanks, however Biosystems Engineering staff were able to acquire the storage tanks for less 
than the analysis reports. For this reason, it was suspected that a farmer could likely acquire necessary 
items in a similar manner, lowering the overall cost/volume of the biodiesel. 
The economic analysis also illustrated that the highest annual cost was incurred for the biodiesel 
processor. The processor was purchased by Auburn University for $17,000 with an educator?s discount. 
A 10-year straight-line depreciation was used for this analysis, but it is likely that, if properly maintained, 
the biodiesel processor could easily last beyond 10 years. A longer life would decrease production cost. 
Labor was the second highest cost of the analysis and was calculated using hourly labor cost published 
by ACES (2011). Again, labor costs could vary from farm-to-farm and cause minor fluctuation in overall 
cost per gallon of biodiesel production. Transportation cost was estimated using approximate mileage 
traveled while collecting WVO and the standard mileage rate charged for Auburn University vehicles. 
Nevertheless for Auburn University?s Biosystems engineering biodiesel production system, it was 
estimated that biodiesel is produced annually at a cost of $2.21 per gallon ($0.58/L). At the writing of 
this thesis, Alabama Cooperative Extension System Profit Profiles reported that current Alabama farm 
diesel fuel prices range from $3.29 to $3.52 per gallon for quantities purchased greater than 1000 
117 
 
gallons (ACES, 2011). If Auburn University?s current biodiesel production system was implemented on-
farm at the current annual production rate of 3,300 gallons per year, the production cost of $2.21 per 
gallon would currently save farmers on average $1.21 per gallon and the current total biodiesel volume 
produced would save farmers almost $4,000 for the year. Approximately 2.7 gallons of methanol is 
currently lost when glycerol is drained from the reactor. If this methanol were recovered for recycling in 
addition to methanol recovered from the raw biodiesel, approximately $385 per year would be saved 
bringing the yearly savings to almost $4,400 by using biodiesel as opposed to petroleum diesel.  
Table 4.15. Annual economic analysis for Auburn University?s Biosystems Engineering 
biodiesel production system. 
  
      
Estimate 
  Collection Site Equipment 
  
$144 
  
      
  
  Biodiesel Production Equipment 
  
  
  
 
Storage Equipment 
   
$485 
  
 
Biodiesel Processor 
   
$2,531 
  
 
Processing Chemicals & Related Items 
 
$1,498 
  
 
Spill Containment & Clean-up Items 
 
$210 
  
Transportation cost 
    
$943 
  
      
  
  Operating Costs 
   
  
  
 
Labor  
    
$1,450 
  
 
Power 
    
$27 
          WVO collected (gal) 
    
 3,300 
        Total Yearly Cost $7,289 
Cost per gallon $2.21 
*Calculated with straight-line depreciation 
?All costs are USD 
     
4.9.2 Small-scale, On-farm Biodiesel Production System 
An economic model was developed for a small-scale, on-farm biodiesel production scenario 
(Table. 4.16) based on Section 4.9.1. Biodiesel production costs were used with oilseed processing costs 
118 
 
and commodity prices (opportunity costs) factored in as well. Some costs of spill containment and 
collection site equipment were omitted because they were requirements of Auburn University?s Risk 
Management division and were not a requirement of the on-farm set-up. Oilseed yields were taken 
from mean experimental yields for 2008-2010 from Section 4.2. Oilseed cake generated from 
mechanically pressed oilseeds was given a weight basis value equivalent to soyhull pellets, as was 
assumed to be fed to livestock and partially replace the need to purchase soyhull pellets.  
The Input section of the model was designed to be used by a farmer so that they could enter 
actual values from his operation. They would start by entering production acres of soybean and/or 
canola that he intended to use for biodiesel production followed by a yearly volume of WVO that he 
planned to collect and process. Next, he or she would enter anticipated crop yields that should be based 
on yield history. Commodity prices are entered next. While prices are highly variable, contract or future 
commodity prices will provide a market value. Finally, they would input the value of soyhull pellets. 
From these parameters, estimates will be generated for total biodiesel yield, meal produced, commodity 
price, and meal value. If WVO is a factor of the model for the year, transportation cost for acquiring the 
WVO will be estimated as well.  
A subsection of the model will also recommend needed equipment for the biodiesel operation. The 
Recommended equipment subsection (table 4.17) includes suggestions for processor capacity based on 
commercially available biodiesel processors offered by Biodiesel Logic, Inc., an estimate of batches 
produced per week based on oilseed production area and volume of WVO entered, an estimate of oil 
storage containers needed per two week time period, drums needed for storing the glycerol co-product 
of transesterification, and AMBERLITE? BD10Dry needed per year. Assumptions were made that no 
more than four batches of biodiesel would be made per week, oil needed two weeks for particulates to 
settle, and glycerol would be disposed of or fed to livestock within two weeks. 
 
 
119 
 
Table 4.16. Annual economic model for small-scale, on-farm biodiesel production. 
  
    
Input 
  Oilseed Production (ac) 
  
  
  
 
Soybean 
  
50 
  
 
Canola 
  
200 
  Waste Vegetable Oil (WVO) (gal) 
  
2,000 
  Oilseed Yield (bu/ac) 
  
  
  
 
Soybean 
  
52.26 
  
 
Canola 
  
35.42 
  Commodity Price ($/bu) 
  
  
  
 
Soybean 
  
$13.70 
  
 
Canola 
  
$13.31 
  Soyhull pellets ($/ton) 
  
$180 
          Model 
  Estimated Biodiesel Produced (gal) 
 
    
  
 
Soybean 
  
2,826 
  
 
Canola 
  
15,301 
  
 
WVO 
  
2,000 
  Estimated Meal Produced (ton) 
  
  
  
 
Soybean 
  
68 
  
 
Canola 
  
120 
  Estimated Total Commodity Value ($) 
  
  
  
 
Soybean 
  
$35,798 
  
 
Canola 
  
$94,288 
  Estimated Total Meal Value ($) 
  
  
  
 
Soybean  
  
$12,203 
  
 
Canola 
  
$21,550 
  Estimated WVO Transportation Cost ($) 
  
$580 
  Oilseed Extrusion 
  
  
  
 
Mechanical Screw Press 
  
$1,200 
  Biodiesel Production  
  
  
  
 
Storage Equipment 
  
$328 
  
 
Biodiesel Processor 
  
$6,186 
  
 
Processing Chemicals & Related Items 
  
$9,010 
  
 
Spill Containment & Clean-up Items 
  
$143 
  Operating Costs 
  
  
  
 
Labor  
  
$15,483 
  
 
Power 
  
$202 
Total Biodiesel Produced (gal)     20,128 
Total Yearly Cost     $129,465 
Cost per gallon     $6.43 
*Calculated with straight-line depreciation 
?All costs are USD 
   
120 
 
Table 4.17 Small-scale, on-farm biodiesel production system recommended equipment.  
Recommended equipment Estimate 
Processor capacity (gal)* 275 
biodiesel batches processed per week? 2 
275-gal oil storage totes 3 
55-gal drums for glycerol collection 3 
AMBERLITE? BD10Dry needed per year 2 
*Biodiesel processor based on commercially available models from Biodiesel Logic, Inc. 
?Based on maximum of four batches per week. 
 
Fixed costs of the mechanical screw press, biodiesel processor, and associated equipment were 
assigned a life expectancy and straight-line depreciation was used to determine annual costs. Processing 
chemicals were given 2010 values and labor cost was calculated based on quantity of oilseeds to 
process, biodiesel batches to produce, and an estimated farm laborer wage (ACES, 2011). Power cost 
was projected based on kilowatt hours required by the mechanical screw press and biodiesel processor 
to process estimated oilseeds and biodiesel batches. Highest costs were determined for commodity 
opportunity cost, processing chemical costs, and labor. 
Figures 4.14, 4.15, and 4.16 illustrate biodiesel production costs per volume. Soybean and canola 
biodiesel production costs were determined on an acreage basis and highlighted fluctuations of 
biodiesel production costs for the past year based on commodity market trends. Biodiesel produced 
from soybeans showed highest production costs and was attributed to higher commodity prices. 
Economy of scale was favorable for this model in that an increase in production acres lowered the 
overall production cost of biodiesel on a volume basis. WVO utilized for biodiesel resulted in lowest 
production cost as compared to soybean and canola. This was attributed to the fact that WVO was 
assumed to essentially be a free feedstock and the only cost associated with WVO was transportation. 
Increasing volumes of WVO lowered the overall cost of production; 5,000 gallons was estimated to cost 
$1.40/gal. Lowest production costs for soybeans and canola were experienced for monthly commodity 
121 
 
prices of April, 2010 and with 500 acres of production. They were determined to be $6.81/gal and 
$4.07/gal, respectively.  
Figure 4.14. Soybean biodiesel production cost based on historical commodity prices and production 
area. 
 
Table 4.17 provides economic profits or losses for soybean producers in Alabama from 2006 to 
2010 (ACES, 2010). An average loss of $101.22/acre was experienced by soybean producers in 2010 
when selling crops through traditional commodity markets. When this profit/loss data was entered into 
the model with production acreage of 500 acres, a loss of $91.22/acre was calculated. If in 2010, a 
farmer produced biodiesel with 500 acres of soybeans as opposed to selling them through traditional 
commodity markets, they would have decreased his net losses by $10.00/ac. Figure 4.17 showed that 
the breakeven losses were at a soybean price of approximately $11.40/bu meaning that for 2010, a 
farmer would have had an economic advantage by producing biodiesel when soybean prices were below 
122 
 
$11.40/bu. If soybean prices had risen above $11.40/bu, they would have benefitted most by selling the 
crop through commodity markets.  However, for soybean prices below $15.09/bu, he or she would have 
still suffered a loss. 
Figure 4.15. Canola biodiesel production cost based on historical commodity prices and production 
area. 
 
In order for small-scale, on-farm biodiesel production to be economical, net losses must be less 
when retaining the oilseeds on farm and producing biodiesel than when selling crops through traditional 
commodity markets. In 2010, an average reduction in losses of $10.00/ac could have been experienced 
with small-scale, on-farm biodiesel production. Although, the profit/loss estimates in table 4.18 
illustrate how volatile commodity markets have been over the past five years. From this data, it is 
evident that loss reductions or profits will vary substantially from year-to-year. However, Paulson and 
123 
 
Ginder (2007) and Kenkel and Holcomb (2006) alluded to the fact that one aspect of recent oilseed 
market volatility may have been brought about by increased interest in biodiesel production. By keeping 
oilseeds grown for biodiesel production on-farm, they never enter the commodity market. If small-scale, 
on-farm biodiesel production systems were widely accepted and practiced and portions of crops were 
not sold through trading venues, markets may eventually stabilize.  
Figure 4.16. WVO biodiesel production cost. 
 
Table 4.18. 2006-2010 Soybean Profit/Loss Economics (ACES, 2011). 
Year 
Mean cost of 
production/ac 
Mean yield 
(bu/ac) 
Mean price 
($/bu) 
Profit/Loss 
($/ac) 
2006 238.73 20 6.85 -110.73 
2007 262.51 21 11.40 -23.11 
2008 309.76 35 10.30 50.74 
2009 394.38 40 10.40 21.62 
2010 392.42 26 11.20 -101.22 
 
124 
 
Figure 4.17. 2010 soybean commodity value vs. biodiesel production profit/loss breakeven point. 
 
  The feasibility of small-scale biodiesel production at the farm level was justifiable in Alabama 
from 2010 soybean economic data alone. Because canola is not a frequently grown crop in the state, no 
profit/loss data was available. However, considering that canola contains twice the oil content of 
soybean, but is grown as a winter crop with fewer required inputs, biodiesel yield would be higher but 
cost of crop production would be lower, respectively. Thus, the breakeven point would be somewhat 
different from soybeans. Nonetheless, cost of biodiesel production from canola was $2.50/gal to 
$3.00/gal cheaper than soybean biodiesel. Reduced production cost, coupled with a winter growing 
season, make canola appealing for Alabama farmers who wish to supplement their fuel needs with 
biodiesel. If WVO was added to on-farm biodiesel feedstock, cost of production would be further 
reduced and even has the potential to approach current petroleum fuel prices. 
To investigate the response of biodiesel cost of production to reductions in model parameters, a 
simple sensitivity analysis was conducted (table 4.19). Four of the highest costs within the model were 
reduced from 5% to 50% with cost of production calculated for each individually. Then, a collective cost 
125 
 
of production was calculated for all four parameters at the respective percent reduction. This analysis 
revealed that a cost reduction of just above 30% for the four parameters would result in biodiesel cost 
of production equaling current farm petroleum diesel fuel prices reported by ACES. Major reductions in 
cost of production were attributed largely to commodity price reduction. While reduction in commodity 
prices by 30% is not likely, labor and processor cost reductions may be feasible for Alabama farmers. 
Reduction of labor cost could be accomplished by increasing automation and efficiency of oil extraction 
from oilseeds. Processor cost could be reduced if a farmer designed and fabricated his or her own. 
These reductions could easily range from 30% to 50% or higher for a mechanically inclined farmer. 
However, labor and processor cost were not found to be major cost reducing parameters in lowering 
cost of production of biodiesel.  
Table 4.19. Biodiesel cost of production sensitivity analysis. 
Reduction in 
Cost (%) 
-------Cost of Production ($/gal)------- Collective Cost of 
Labor Processor Canola Soybeans Production ($/gal) 
5% $6.39 $6.42 $6.14 $6.31 $5.97 
10% $6.36 $6.40 $5.86 $6.19 $5.51 
20% $6.28 $6.37 $5.28 $5.96 $4.59 
30% $6.20 $6.34 $4.71 $5.72 $3.67 
40% $6.12 $6.31 $4.13 $5.48 $2.75 
50% $6.05 $6.28 $3.55 $5.24 $1.82 
 
 
126 
 
 
 
 
CHAPTER FIVE 
SUMMARY AND CONCLUSIONS 
5.1 Summary 
The goal of this research was to facilitate understanding of an integrated small-scale, on-farm 
biodiesel production system and potential of minimizing variability associated with oilseed production 
and processing, biodiesel quality and volatile farm input costs for an Alabama farm. The experiments 
performed, data collected, and analysis of results suggested that farmers could have reduced 2010 
profit losses by $24.70/ha, on average by implementing this system. 
A non-traditional bioenergy crop rotation experiment was initiated and crop constituent 
responses (i.e. oil, protein, and oil FFA) to varying levels of irrigation were assessed in the first 
experiment. Results indicated that over the course of the three years of the study, soybean and cotton 
yields significantly improved as a result of increasing levels of irrigation. Most notable results were 
experienced in 2010 when total seasonal rainfall was 13.4 cm and responses to irrigation accounted for 
380% and 166% increases in theoretical biodiesel yield in soybeans and cotton, respectively. Canola was 
grown as a winter crop with no irrigation applied after emergence and although the experiment was too 
new to determine rotational effects, 2010 theoretical biodiesel yield was 200% higher than 2008 due to 
an additional 17 cm of rainfall received during the growing season.  
Oil concentrations of the three crops varied 2% to 3% with experimental averages of 19%, 44% 
and 22% for soybeans, canola and cotton, respectively reported on a dry matter basis. Slight trends 
existed in which oil concentration increased with increasing irrigation. This result favors well with crop 
yield responses to irrigation because the major limiting factor of theoretical biodiesel output was 
127 
 
determined to be crop yield. Increased yield and oil output as a product of increasing irrigation will 
further accentuate theoretical biodiesel yield. However, protein concentration showed slight trends to 
decrease with increasing irrigation levels and differed by an average of 20% among the three crops with 
highest protein concentrations found in soybean of approximately 36%.  
In most oilseed crop experiments, FFA levels remained below one percent which was 
complimentary for biodiesel production. However, in 2009 FFA concentrations in cottonseed were 20 
times higher, averaging 9% among irrigation treatments. Elevated FFA was attributed to excessive end of 
the season rainfall and prolonged seed exposure to moisture after the bolls opened. Harvest was 
delayed due to wet field conditions with seeds beginning to germinate and oils undergoing hydrolysis. 
Oilseeds subjected to these conditions will not be suitable for biodiesel production and should be sold 
through traditional commodity markets or, in the case of cottonseeds, traded in return for cotton 
ginning. 
The biodiesel production procedure literature review revealed base catalyzed transesterification 
to be the least expensive and safest method of producing biodiesel. Methanol at an alcohol to oil    
molar ratio of 6:1, 1 wt.% sodium hydroxide, reaction time of four hours and reaction temperature of 60 
oC resulted in >96% ester conversion at a chemical cost of $0.16/L. However, very similar results were 
obtained at reaction duration of one hour. Similarly, oil types of soybean, sunflower, peanut, canola, and 
cottonseed were reviewed in published literature with base catalyzed transesterification procedures 
comparable to the one stated above yielding >95% ester conversion. 
Canola, soybean and WVO stored in HDPE containers over an approximate nine month period 
resulted in varying triglyceride hydrolysis rates but no noticeable microbial activity. WVO and canola 
oxidized 68% and 43% with FFA levels reaching 4.7% and 3.3%, respectively. On the other hand, soybean 
oil oxidized 50% but resulted in only 0.3% FFA. After nine months of storage in a sealed HDPE container, 
128 
 
soybean oil was be most favorable for base catalyzed biodiesel production in terms of FFA. WVO and 
canola oil may be transesterified, but an acid pretreatment may be necessary for optimal ester yield.  
Absorbance data from soybean, canola, and WVO oils resulted in similar mean readings over 
time as control samples. Optical density of the samples over time, while a relative measurement, 
showed no increases at the 550-nm wavelength indicating no microbial activity. Unlike experimental 
samples, bacteria positive control samples revealed substantial absorbance increase at 550-nm 
wavelength with optical density readings increasing with each incremental increase of bacteria 
concentration.  
Methanol recovery efficiency in the Biodiesel Logic, Inc processor was increased by 600% with 
assistance of a vacuum pump. Average savings of $1.28 per 90-L batch were found when using the 
vacuum pump and residual methanol in biodiesel was decreased from 3.04 L to 0.8 L. This reduction in 
residual methanol not only makes the end product safer to handle, store and transport, but also 
recovers sufficient methanol for future use to compensate for power requirements of upcoming 
batches.  
Phosphorus removal from vegetable oils was greatest with the citric acid method in canola oil 
and anion exchange resin in soybean oil. Control samples of canola oil and soybean oil contained 51 
ppm and 45 ppm and phosphorus levels were reduced to 26 ppm and 24 ppm, respectively. Even after 
transesterification of the degummed oils, soybean biodiesel from the anion exchange degumming 
treatment resulted in 22 ppm phosphorus, which was the experimentally lowest level reached. 
However, ASTM Standard D 6751 (2007) requires no more than 10 ppm phosphorus and none of the 
degumming treatments or biodiesel produced from the degummed oil achieved this level. 
Biodiesel quality from the Biodiesel Logic, Inc. biodiesel processor resulted in an average 96.7% 
ester conversion. This conversion efficiency was better than expected considering oil/methanol 
interaction was poor due to no active mixing within the reactor vessel. Mean free glycerin was reduced 
129 
 
in each contiguous sampling. This result indicated that some post transesterification reaction took place 
during methanol recovery, but almost all free glycerin was removed during dry washing. The 
AMBERLITE? BD10Dry resin used to refine the biodiesel worked well with ASTM Standard D 6751 (2007) 
being met in terms of free glycerin requirements. When comparing biodiesel quality from this 
experiment to biodiesel produced on a bench scale from degummed soybean and canola oils, ester 
conversions were significantly higher on average for the bench scale experiment (99.5%). The ester 
conversion result from the bench scale experiment further solidified the fact that poor methanol/oil 
interaction was experienced in the Biodiesel Logic, Inc. processor. However, not all samples of canola oil 
successfully transesterified from the degumming experiment.  Anion exchange resin degummed samples 
of canola oil transesterified successfully. The resin manufacturer stated that the resin was effective in 
removing proteins, phospholipids and cations. FFA concentrations were high with proteins and cations 
attributed to transesterification failure in non anion resin degummed samples.  
Oil extraction efficiency varied from crop-to-crop with the Henan Double Elephant mechanical 
screw press. Operator aptitude is crucial with this press as it has several movable components that must 
be optimized to maximize oil output. An extraction efficiency of 77% was achieved with soybeans, 
however other oilseeds of sunflower and canola resulted in only 48% and 34% extraction efficiency, 
respectively. The Karl Str?hle mechanical screw press resulted in 49% oil extraction efficiency which was 
144% higher than the Henan Double Elephant press. This increase in oil output was attributed to the fact 
that the Karl Str?hle press contained fixed parts with less of an operator learning curve. 
Economic analysis of Auburn University?s Biosystems Engineering small-scale biodiesel 
production system revealed an annual WVO processing volume of 3,300 gal. Parameters of 
transportation cost, equipment, labor and power requirements were assessed and a yearly production 
cost of $2.21/gal was determined. The data from this system was extrapolated to a small-scale on-farm 
biodiesel production system scenario and a prediction model developed. It was determined that 
130 
 
soybean resulted in the highest production cost, followed by canola and WVO, respectively. Increased 
production cost was attributed to higher commodity prices and differences in oil concentrations 
between crops. WVO was assumed to be a free feedstock with the only associated cost of the oil being 
transportation. Even though soybean resulted in highest biodiesel production cost, an economic gain of 
$10.00/ac resulted from producing biodiesel with soybeans grown on-farm as opposed to selling them 
through traditional commodity venues. An average acreage loss of $101.22 was experienced by Alabama 
soybean producers in 2010, but only $91.22/ac loss would have been experienced if the soybean crop 
had been used for biodiesel production. No tax credits or subsidies were taken into account in this 
scenario. 
From this study, we learned that irrigation improved theoretical biodiesel yield in oilseed crops 
by increasing overall crop yields. Seasonal rainfall influenced crop yields as well and irrigation was most 
valuable in years of limited precipitation. Simple methods of degumming crude vegetable oil were not 
effective in meeting ASTM requirements, but satisfactory biodiesel conversion efficiencies were 
obtained with proper oil/methanol interaction. Vacuum pump assistance during methanol recovery was 
imperative to achieve maximum return and a safe end product. In conclusion, a small-scale, on-farm 
biodiesel production system can provide quality biodiesel at an economic advantage, especially during 
periods of market volatility as experienced in recent years and while supplementing both on-farm fuel 
and livestock feed requirements. But most importantly, this system provides Alabama farmers a viable 
alternative to selling crops through traditional commodity venues  in order to maximize returns and 
allows them the option of deciding the most beneficial means of crop utilization during periods of 
uncertainty.  
 
131 
 
5.2 General Conclusions 
The conclusions of this research are as follows: 
1) Oilseed constituents were impacted by varying levels of irrigation. In terms of biodiesel 
production, which was the focus of this study, oilseed yield was the limiting factor for biodiesel output 
and soybean and cotton crops responded positively to increasing levels of irrigation. The 125% pan 
evaporation treatment adjusted for percent crop canopy resulted in highest yield in years when rainfall 
was limited. Because canola was grown as a winter crop, irrigation was only applied through emergence. 
However, increased rainfall during the canola growing season significantly increased yield, which in turn 
increased theoretical biodiesel output.  Similarly, oil concentration was increased with increasing 
irrigation and closely paralleled yield responses, thus resulting favorably to biodiesel output. Protein 
showed a slight negative trend with increasing irrigation, but overall FFA was not affected at a 
substantial level by irrigation to effect biodiesel output. However, in years when excessive end of season 
rainfall is experienced, care should be taken when considering utilizing oilseed crops grown for biodiesel 
production. FFA levels unsuitable for base catalyzed transesterification were experienced as a result of 
high moisture coupled with late harvest. When this scenario is experienced, selling crops through 
traditional commodity markets would be strongly advised.   
2) A base catalyzed transesterification procedure using a 6:1 molar ratio of methanol to oil, 1 
wt.% sodium hydroxide, reaction time and temperature of one hour and 60 oC was recommended based 
on production procedure literature review and cost of biodiesel production associated with this method. 
According to published literature, this method resulted in satisfactory biodiesel quality at least chemical 
expense. While phosphorus removal procedures were simplistic and safe for an on-farm system, ASTM 
requirements were not met. However, to ensure consistent, quality transesterification results of stored 
vegetable oils, especially canola oil, AMBERSEP? BD19 may be used. Degumming before storage is 
recommended as this has the potential to increase phosphorus removal efficiency. In terms of storage, 
132 
 
oils should be stored in air-tight containers, out of sunlight and in cool temperatures to minimize 
hydrolysis. If stored properly, soybean oil will be easily transesterified after a nine month storage 
interval. Canola oil and WVO stored in proper conditions for nine months may effectively convert to 
biodiesel, but bench scale transesterification tests are recommended, prior to batch reactions, to 
determine whether a batch reaction will satisfactorily convert. If crude oils are stored long-term without 
first degumming, the bottom portion of oil should not be used for biodiesel production without a bench 
scale test to ensure conversion.   
3) Small-scale, on-farm biodiesel production can be feasible, even economical during years of 
farm input market volatility. A system such as this not only minimizes potential profit losses during 
market fluctuations, but also provides security for the future of the operation. Fuel and livestock feed 
requirements can be effectively supplemented and farmers have a feasible alternative to selling crops 
through traditional markets when economics are favorable. Biodiesel production from soybeans 
resulted in higher production cost due to elevated commodity price. However, a biodiesel production 
system where more canola is utilized as opposed to soybeans and along with WVO, production costs will 
begin to approach current petroleum fuel prices. Further, canola will be grown as a winter crop and not 
impact potential profits of crops grown during the conventional cropping season.      
5.3 Future Research 
Crude vegetable oil degumming experiments were ineffective in meeting ASTM threshold 
requirements for phosphorus. However, citric acid showed the most promise in canola oil. Future 
research should include experimentation with varying concentrations of an aqueous citric acid solution 
to determine if phosphorus requirements can be obtained. Likewise, canola oil was complicated to 
transesterify after approximately eight months of storage. Gums and proteins were attributed to 
transesterification difficulty and oil stratification was suspected as well. Future research should focus on 
133 
 
determining the magnitude of oil stratification, if degumming practices prior to storage can minimize 
stratification, and quantify proteins and cations removed from canola oil when using AMBERSEP? BD19. 
These research strategies are expected to increase consistency in canola oil transesterification.  
Poor methanol/oil interaction was determined in the Biodiesel Logic, Inc. processor. This poor 
interaction was believed to be the cause of lower conversion efficiencies as compared to bench scale 
reactions. Implementation and testing of an active mixing process will be valuable in determining if 
increased ester formation can be achieved. A simple paddle mixer may be employed and is expected to 
increase yields.  
Of most importance is decreasing biodiesel production costs in a small-scale, on-farm system. 
Commodity prices contributed most to overall price per volume of biodiesel produced. However, this 
factor cannot be changed by the system operator.  Likewise, chemical cost was another major expense, 
but the most economically favorable procedures were employed. Labor was the second largest expense. 
The largest portion of labor was required for mechanically extruding oil from the seeds. Future research 
should include means of automating the oilseed extrusion process in order to lower labor cost. Although 
the biodiesel processor was a fixed cost, it was substantial. Farmers typically have a great deal of 
mechanical aptitude and future research and efforts could focus on developing a biodiesel processor 
from materials a farmer may be able to easily and cheaply access. However this reactor would ideally 
contain timing relays to automate the reaction processes, safety measures to prevent pressure induced 
explosions, and an effective methanol recovery system.   
 
 
 
 
 
134 
 
 
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136 
 
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137 
 
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138 
 
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139 
 
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140 
 
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141 
 
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142 
 
 
 
 
 
APPENDIX A 
 
BIODIESEL PRODUCTION PROCEDURES AND CONVERSION EFFICIENCY 
REVIEW 
143 
 
A.1 Table of various transesterification techniques in published literature. 
Oil Type 
Catalyst        
(wt%) 
Alcohol to oil 
(molar ratio) 
Reaction 
time (hr) 
Reaction 
Temp. (oC) 
Ester yield 
(%)*? Researcher? 
Soybean 
NaOH (0.3) 
EtOH (6:1) 1 30 NPS 1 70 
EtOH (12:1) 1 30 96.8 1 70 97.2 
NaOH (0.65) EtOH (9:1) 1 50 95.8 1 
NaOH (1.0) 
EtOH (6:1) 1 70 86.1 1 
EtOH (12:1) 30 91.3 1 
MeOH (3:1) 
0.017 
60 
20.0 
4 
0.033 28.0 
0.067 33.0 
0.5 60.0 
1 62.0 
MeOH (6:1) 
0.1 
32 
64.0 
4 
0.2 79.0 
0.5 89.0 
1 92.0 
2 96.0 
4 98.0 
20 98.0 
0.1 
45 
87.0 
4 0.2 91.0 0.5 93.0 
1 97.0 
0.017 
60 
86.0 
4 
0.033 89.0 
0.067 90.0 
0.1 94.0 
0.2 95.0 
0.5 96.0 
1 96.0 
NaOCH3 (0.5%) 
MeOH (1:1) 
1 60 
41.0 
4 
MeOH (2:1) 69.0 
MeOH (3:1) 84.0 
MeOH (4:1) 93 
MeOH (5:1) 95.0 
MeOH (6:1) 
0.017 
60 
82.0 
4 
0.033 88.0 
0.067 90.0 
0.5 97.0 
1 98.0 
KOH (1.0) EtOH (6:1) 1 30 92.5 1 
144 
 
70 93.3 
EtOH (12:1) 1 30 95.1 1 70 95.6 
Soybean 
H2SO4 (1.0) 
EtOH (30:1) 
1 
75 
9.0 
4 2 20.0 5 58.0 
20 90.0 
MeOH (6:1) 48 60 72.7 2 
MeOH (30:1) 
1 
60 
5.0 
4 
2 5.0 
5 8.0 
20 50.0 
30 55.0 
45 82.0 
50 90.0 
69 92.0 
n-BuOH 
(30:1) 
0.1 
114 
10.0 
4 
0.2 15 
0.5 43.0 
1 70.0 
2 86.0 
3 95.0 
4 94.0 
H2SO4 (3.0) 
EtOH (6:1) 48 75 95.8 2 
MeOH (3.3:1) 48 60 76.5 2 
MeOH (3.9:1) 81.0 
MeOH (6:1) 48 
25 8.3 
2 45 57.2 
60 88.7 
96 60 95.1 2 
MeOH (20:1) 48 60 97.0 2 
MeOH (30:1) 98.4 
n-BuOH (6:1) 48 110 92.1 2 
2-PrOH (6:1) 48 75 92.9 2 
H2SO4 (5.0) MeOH (6:1) 48 60 95.0 2 
 
 
 
 
 
 
Sunflower 
 
 
 
 
 
 
 
 
 
 
 
NaOCH3 (0.5%) 
 
 
 
 
 
EtOH (3:1) 
0.017 
75 
63.0 
4 
0.033 68.0 
0.067 70.0 
0.5 76.0 
1 80.0 
EtOH (6:1) 
0.017 
75 
75.0 
4 
0.033 82.0 
0.067 83.0 
0.5 95.0 
1 97.0 
MeOH (1:1) 1 60 35.0 4 
MeOH (2:1) 68.0 
145 
 
 
 
 
 
 
 
 
 
 
 
 
Sunflower 
 
 
 
 
 
 
 
 
 
 
 
 
NaOCH3 (0.5%) 
 
MeOH (3:1) 
0.017 
60 
60.0 
4 
0.033 66.0 
0.067 70.0 
0.5 77.0 
1 82.0 
MeOH (4:1) 1 60 90.0 4 
MeOH (6:1) 
0.017 
60 
78.0 
4 
0.033 85.0 
0.067 86.0 
0.5 94.0 
1 98.0 
n-BuOH (3:1) 
0.017 
114 
76.0 
4 
0.033 75.0 
0.067 76.0 
0.5 77.0 
1 88.0 
n-BuOH (6:1) 
0.017 
114 
90.0 
4 
0.033 89.5 
0.067 91.0 
0.5 93.0 
1 95.0 
Canola 
KOH (1.4) MeOH (4.6:1) 0.5 Room temp 91.9 3 
KOH (0.63) 
MeOH (3.0) 0.167 
40 67.90 
5 NaOH (0.90) 53.24 KOCH
3 (2.38) 50 94.27 
NaOCH3 (1.22) 60 89.13 
NaOCH3 (1.84) 
MeOH (4.5) 0.167 
40 90.44 
5 
KOH (1.27) 
50 
91.27 
KOCH3 (1.59) 95.80 
NaOH (0.45) 82.50 
KOCH3 (0.79) 60 92.23 
KOCH3 (1.59) 
MeOH (6.0) 0.167 
40 91.79 5 
NaOCH3 (0.61) 50 85.41 
KOH (1.90) 60 89.27 5 
NaOH (1.35) 90.19 
Cottonseed NaOCH3 (0.5%) 
MeOH (2:1) 
1 60 
58.0 
4 MeOH (3:1) 74.0 
MeOH (4:1) 87.0 
MeOH (6:1) 
0.017 
60 
53.0 
4 
0.033 68.0 
0.067 74.0 
0.5 88.0 
1 95.0 
Peanut NaOCH3 (0.5%) 
MeOH (2:1) 1 60 62.0 4 
MeOH (3:1)   78.0  
MeOH (4:1)   87.0  
MeOH (6:1) 0.017 60 53.0 4 
146 
 
0.033 68.0 
0.067 75 
0.5 91.0 
1 97.0 
*NPS ? No Phase Separation 
?Not all ester yields presented are final yields. Some were measured during reaction. 
?Kucek et al. (2007)1; Canakci and Van Gerpen (1999)2; Dorado et al. (2004)3; Freedman et al. (1984a)4; 
Singh et al. (2006)5  
 
 
 
 
  
147 
 
 
 
 
APPENDIX B 
 
EXPERIMENTAL EQUIPMENT AND CHEMICAL SPECIFICATIONS 
  
148 
 
B.1 Chemyx? Nexus Model 6000 syringe pump 
 
Weight 6 kg 
 
Flow rate: 
 
 Minimum: 1.56 pl/min (0.5 ?l syringe) 
 Maximum: 200 ml/min (2000 ml syringe) 
 
Step resolution: 
 
0.046 ?m 
 
Accuracy: 
 
? 0.35% 
 
Reproducibility: 
 
? 0.05% 
 
Computer control: 
 
RS232 COM port 
 Windows XP, Vista, 7; Mac OS X v10 or later 
 
Power requirements: 
 
100-240 VAC: 50/60 Hz 
  
 
 
 
149 
 
B.2 Gast Manufacturing, Inc. vacuum pump model DOA-P704-AA 
 
Power 115VAC / 4.2 A / 60 Hz / 0.1 kW 
Size: 20 x 17 x 27 cm 
Maximum pressure: 3103 mm Hg 
Max vacuum: 84 kPag 
  
  
  
 
 
 
 
 
 
150 
 
B.3 New Brunswick Scientific Innova 40 incubator shaker  
 
Temperature:   
 Range: 5oC to 80oC 
 Control: ?0.1 oC 
 Uniformity: ?0.25 oC at 37 oC 
Speed Range: 25 ? 500 rpm (?1 ppm) 
  
  
 
 
 
 
151 
 
B.4 Molecular Devices Spectramax M2 Microplate Reader  
 
Wavelength range: 
 
200 - 1000 nm 
Wavelength accuracy:  
 
?2.0 nm 
Wavelength selection:  
 
Monochromator, tunable  in 1.0-nm increments 
Wavelength bandwidth:  
 
? 4.0 nm 
Wavelength repeatability:  
 
?0.2 nm 
Photometric resolution:  
 
0.001 OD 
Photometric accuracy (microplate): 
 
< ?0.006 OD ?1.0%, 0?2 OD 
Photometric range: 
 
0?4.0 OD 
Plate formats:  
 
6, 12, 24, 48, 96, 384 wells 
 
Light source:  Xenon flash lamp (1 joule/flash) 
 
Detector:  Photomultiplier (R-3896) 
 
Read time:  
 
 
96-well: 
 
Abs 18 sec., FI 15 sec. 
  
384-well: 
 
Abs 49 sec., FI 45 sec. 
152 
 
B.5 Agilent Technologies Gas Chromatograph model 7890A 
source: www.chem.agilent.com 
Detector: FID 
Column: 10-m Restek biodiesel column with guard column 
  
  
  
  
 
153 
 
B.6 Henan Double Elephant Machinery Co., Ltd (Henan, China) model 6YL-120  
 
 
Capacity: 6 T per 24 hours 
Power: 15kW / 3? / 1440 rpm 
Size: 1970 x 700 x 780 mm 
Weight: 680 kg 
  
  
  
  
 
 
 
 
 
154 
 
B.7 Karl Str?hle GmbH & Co. KG (Dettingen, Germany) SK 60/2 
 
 
Capacity: 720 kg/day 
Power: 220 VAC / 1? / 60 Hz 
Size: 1300 x 2300 mm 
Weight: 194 kg 
  
  
  
  
 
 
 
 
 
155 
 
B.8 Biodiesel Logic, Inc. BDL-55-SS-A 
 
Capacity: 208 L per 8 hours 
Filter: 14 micron 
Vessel material: 304 SS 
Power: 110/220 VAC / 1? / 60 Hz / 30 A circuit 
Heater assembly: 1? / 316 SS 
Pumps: 3 / 220 VAC  / 0.4 kW 
Weight: 907 kg 
Dimensions: 183 cm x 66 cm x 213 cm 
  
  
 
 
 
 
156 
 
B.9 AMBERSEP? BD 19 
157 
 
 
158 
 
B.10 AMBERLITE? BD10DRY? Biodiesel Purification Technology 
Users Guide - Laboratory Trials 
TO LAB TEST A SAMPLE OF AMBERLITE? BD10DRY? PURIFICATION MEDIA 
To test Amberlite? BD10DRY? purification media we encourage you to use the "contaminated 
biodiesel" that has been well-separated from the glycerol phase after your transesterification process. 
This biodiesel should not be dried and preferably contains methanol traces. From experience, this phase 
usually contains less than 400 ppm of impurity cations from soap and catalyst. You can also prepare test 
solutions using pure biodiesel spiked with catalyst. 
 a) Column Purification Mode 
Amberlite? BD10DRY? is a specialty polymer media that is most 
effective at removing impurities and glycerol traces when used in a "column 
mode". For lab testing, a glass column with roughly a 10:1 height:width ratio is 
recommended. A suitable column can be obtained from Ace Glass (Vineland, 
NJ; part number 5820-30; 25mm (1 inch) diameter by 300 mm (12 inch) 
length). A bed support must be used to keep the polymer beads in the column 
while allowing the biodiesel to easily flow through. Generally, glass frits or a 
stainless steel screen can be used for this purpose where the openings in the 
screen are no greater than about 0.18mm (80 mesh). 
 The column should be loaded about 1/4 full with biodiesel and the 
Amberlite? BD10DRY? media is then poured into the column. The presence of 
a liquid "heel" in the column helps to ensure uniform packing of the beads as 
they are loaded which minimizes possible channeling or flow distribution 
irregularities.  
The column should be filled no more than 1/3 full with Amberlite? BD10DRY? 
media in order to allow for expansion of the resin as it removes impurities. 
Avoid contacting the media with pure methanol or water as this will cause it to 
swell up to 3 times in volume and the glass column can shatter if insufficient 
void space is left at the top of the column. 
A liquid head of at least 25 mm (1 inch) should be maintained above 
the resin to prevent air infiltration into the bed. Gravity feed can then be used 
to pass contaminated biodiesel through the bed, although a pump will allow more precise control of 
flowrate. 
The flowrate of the biodiesel in the column should be around 3 L/hour of biodiesel per kg of 
Amberlite? BD10DRY? in the column (3.0 liters/hour per kg). With gravity flow, the flowrate can be 
easily controlled by using a pinchcock on a rubber hose connected to the column outlet fitting. The 
biodiesel recovered at the bottom of the column can then be analyzed. 
  
159 
 
b) Batch Purification Mode 
Amberlite? BD10DRY? technology will yield the best results (lowest concentration of catalyst, 
soap, and glycerol in the treated biodiesel) when used in a column purification mode (see above). Use in 
a batch purification mode is therefore not recommended. It is, however, a quick and easy way to 
demonstrate the performance of Amberlite? BD10DRY? technology. 
?  Pour 10 g of Amberlite? BD10DRY? media into a glass vessel (e.g. a beaker).  
?  Add 100 mL of contaminated biodiesel and stir gently for one hour.  
?  Decant the biodiesel and analyze. 
INDUSTRIAL USAGE  
 For a continuous process, it is recommended to 
install two columns to eliminate downtime when replacing 
spent resin. Please note that introducing a second column 
will not impact your consumption of Amberlite BD10DRY. 
The two columns are switched by valves to alternate 
between "lead" and "lag" treatment positions. 
 A Rohm and Haas Technical representative will work with 
you to select a column design best suited to your process.  
A column design sketch based on 10 MM gallons (37.8 MM 
liters) per year throughput is enclosed. 
  CAUTION! Amberlite? BD10DRY? technology is 
designed to be used in a water-free process. The polymer beads will swell to up to 3 x their original 
volume as they absorb water. The beads will also swell over their life cycle as they remove impurities, 
including methanol and glycerol. It is therefore strongly recommended to leave sufficient void space in 
the columns. 
 Extra glycerol removing capacity 
Even higher capacity for glycerol can be achieved by 
adding one or two extra columns of Amberlite? BD10DRY? 
media dedicated exclusively to glycerol removal, as shown 
in the following figure.  
 These additional glycerol removal columns are 
inserted just after the phase separation step. Note that the 
Amberlite? BD10DRY? media purchased for these columns 
will not impact your overall resin consumption. Why? 
Because when the glycerol removal columns are initially 
installed they will remove not only glycerol but also catalyst 
and soap. Therefore consumption of resin in the 
downstream purification columns will be saved. 
160 
 
 When the media in a glycerol removal column is saturated with glycerol, the column can be 
egenerated by rinsing with methanol. The methanol solution coming off the column can then be sent 
back to the transesterification unit where the methanol is recycled. The interest in having two columns 
dedicated to glycerol removal is that the plant can continue to operate while the first column is being 
regenerated. These columns can thus be regenerated several times to reestablish their glycerol 
removing capacity. However their capacity to remove soap and catalyst is finite; in the steady state of 
operation they will only serve to remove glycerol. Under industrial conditions, the Amberlite? 
BD10DRY? media in the glycerol columns is usually replaced from time to time by the spent resin in the 
downstream purification columns. 
161 
 
B.11 Labconco? Purifier Biological Safety Cabinet 
Source: www.labconco.com 
 
? Nominal inflow velocity of 105 fpm (0.5 m/sec) 
? Nominal downflow velocity of 55 fpm (0.3 m/sec) 
?  Interior-mounted, line-of-sight LCD information center with "Filter Life Remaining" bar graph, status 
line for alarm conditions and alerts to warn when filter life diminishes to 20%, 10% and 0% 
?  Filter monitoring system consisting of an electronically commutated motor (ECM) that delivers a 
precise volume of air as required and automatically adjusts as filters load without relying on airflow 
sensors 
?  Smart-Start? System that allows the user to program start up and shut down operation 
?  Built-in interval or elapsed timer for experiment monitoring or UV light control (on models with UV 
light) 
? Touchpad control on right-hand side post for manual activation of blower, light, timer, audible alarm 
mute and menu selection 
? Radiused type 304 stainless steel interior and removable, seamless, dished work surface with lift out 
knobs 
? Contain-Air? Negative Pressure Channel 
? Class 5 conditions per ISO 14644-1 and 2 (formerly Class 100) 
? Supply and exhaust 99.99% efficient HEPA filters 
? Two electrical duplex receptacles, (single outlets on 230 volt models), located one on each side, with 
ground fault interruption and stainless steel splash covers 
? Fully-closing, clear 1/4" tempered safety glass sash with two sash handles; counterbalanced, anti-
racking mechanism; and 10? slope 
? 29" sash opening viewing height 
? Bright, 100 foot-candle, glare-free fluorescent lighting located outside the contaminated work area 
? Intrinsically safe negative pressure design 
? 10" diameter exhaust outlet 
 
162 
 
  
 
 
 
APPENDIX C 
 
OIL STORAGE EXPERIMENT REGRESSION TABLES. 
                                
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
163 
 
C.1 WVO linear regression analysis for hydrolysis rate. 
The GLM Procedure 
 
Dependent Variable: FFA 
 
                                               Sum of 
       Source                      DF         Squares     Mean Square    F Value    Pr > F 
 
       Model                        1      3.31350000      3.31350000     190.03    <.0001 
 
       Error                        7      0.12205556      0.01743651 
 
       Corrected Total              8      3.43555556 
 
 
                       R-Square     Coeff Var      Root MSE      FFA Mean 
 
                       0.964473      3.495372      0.132047      3.777778 
 
 
       Source                      DF       Type I SS     Mean Square    F Value    Pr > F 
 
       time                         1      3.31350000      3.31350000     190.03    <.0001 
 
 
       Source                      DF     Type III SS     Mean Square    F Value    Pr > F 
 
       time                         1      3.31350000      3.31350000     190.03    <.0001 
 
 
                                                  Standard 
                Parameter         Estimate           Error    t Value    Pr > |t| 
 
                Intercept      2.602777778      0.09593019      27.13      <.0001 
                time           0.235000000      0.01704724      13.79      <.0001 
 
 
 
 
 
 
 
 
 
164 
 
 C.2. Canola oil linear regression analysis for hydrolysis rate. 
                               The GLM Procedure 
 
Dependent Variable: FFA 
 
                                               Sum of 
       Source                      DF         Squares     Mean Square    F Value    Pr > F 
 
       Model                        1      0.98816667      0.98816667     113.91    <.0001 
 
       Error                        7      0.06072222      0.00867460 
 
       Corrected Total              8      1.04888889 
 
 
                       R-Square     Coeff Var      Root MSE      FFA Mean 
 
                       0.942108      3.339593      0.093138      2.788889 
 
 
       Source                      DF       Type I SS     Mean Square    F Value    Pr > F 
 
       time                         1      0.98816667      0.98816667     113.91    <.0001 
 
 
       Source                      DF     Type III SS     Mean Square    F Value    Pr > F 
 
       time                         1      0.98816667      0.98816667     113.91    <.0001 
 
 
                                                  Standard 
                Parameter         Estimate           Error    t Value    Pr > |t| 
 
                Intercept      2.147222222      0.06766286      31.73      <.0001 
   time           0.128333333      0.01202401      10.67      <.0001 
 
 
 
 
 
 
 
 
 
 
165 
 
 C.3. Soybean oil linear regression analysis for hydrolysis rate. 
 
                                        The GLM Procedure 
 
Dependent Variable: FFA 
 
                                               Sum of 
       Source                      DF         Squares     Mean Square    F Value    Pr > F 
 
       Model                        1      0.01350000      0.01350000       3.29    0.1126 
 
       Error                        7      0.02872222      0.00410317 
 
       Corrected Total              8      0.04222222 
 
 
                       R-Square     Coeff Var      Root MSE      FFA Mean 
 
                       0.319737      25.06540      0.064056      0.255556 
 
 
       Source                      DF       Type I SS     Mean Square    F Value    Pr > F 
 
       time                         1      0.01350000      0.01350000       3.29    0.1126 
 
 
       Source                      DF     Type III SS     Mean Square    F Value    Pr > F 
 
       time                         1      0.01350000      0.01350000       3.29    0.1126 
 
 
                                                  Standard 
                Parameter         Estimate           Error    t Value    Pr > |t| 
 
                Intercept     0.1805555556      0.04653562       3.88      0.0061 
       time          0.0150000000      0.00826960       1.81      0.1126 
 
 
 
 
 
 
 
 
 
166 
 
 C.4 Test for hydrolysis slope homogeneity and interaction. 
 
The GLM Procedure 
 
Dependent Variable: FFA 
 
                                               Sum of 
       Source                      DF         Squares     Mean Square    F Value    Pr > F 
 
       Model                        5     63.72035185     12.74407037    1265.37    <.0001 
 
       Error                       21      0.21150000      0.01007143 
 
       Corrected Total             26     63.93185185 
 
 
                       R-Square     Coeff Var      Root MSE      FFA Mean 
 
                       0.996692      4.413071      0.100357      2.274074 
 
 
       Source                      DF       Type I SS     Mean Square    F Value    Pr > F 
 
       oil                          2     59.40518519     29.70259259    2949.19    <.0001 
       time                         1      2.86272222      2.86272222     284.24    <.0001 
       time*oil                     2      1.45244444      0.72622222      72.11    <.0001 
 
 
       Source                      DF     Type III SS     Mean Square    F Value    Pr > F 
 
       oil                          2      6.27945419      3.13972710     311.75    <.0001 
       time                         1      2.86272222      2.86272222     284.24    <.0001 
       time*oil                     2      1.45244444      0.72622222      72.11    <.0001 
167 
 
The GLM Procedure 
 
Dependent Variable: FFA 
 
                                               Sum of 
       Source                      DF         Squares     Mean Square    F Value    Pr > F 
 
       Model                        5     63.72035185     12.74407037    1265.37    <.0001 
 
       Error                       21      0.21150000      0.01007143 
 
       Corrected Total             26     63.93185185 
 
 
                       R-Square     Coeff Var      Root MSE      FFA Mean 
 
                       0.996692      4.413071      0.100357      2.274074 
 
 
       Source                      DF       Type I SS     Mean Square    F Value    Pr > F 
 
       oil                          2     59.40518519     29.70259259    2949.19    <.0001 
       time                         1      2.86272222      2.86272222     284.24    <.0001 
       time*oil                     2      1.45244444      0.72622222      72.11    <.0001 
 
 
       Source                      DF     Type III SS     Mean Square    F Value    Pr > F 
 
       oil                          2      6.27945419      3.13972710     311.75    <.0001 
       time                         1      2.86272222      2.86272222     284.24    <.0001 
       time*oil                     2      1.45244444      0.72622222      72.11    <.0001 
 
 
                                                           Standard 
      Parameter                            Estimate           Error    t Value    Pr > |t| 
 
      compare soybean vs WVO            -0.10666667      0.01832251      -5.82      <.0001 
      compare soybean vs canola         -0.22000000      0.01832251     -12.01      <.0001 
      compare WVO vs canola             -0.11333333      0.01832251      -6.19      <.0001 
       
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
168 
 
 
 
 
APPENDIX D 
 
IRRIGATION EXPERIMENT RESULTS BY CROP AND YEAR & IRRIGATION 
SCHEDULING SPREADSHEET EXAMPLE 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 D.1 2008 canola results. 
Irr. TRT   
(% Pan)  
Stand count  Dry yield FFA  Oil  Protein Biodiesel 
Production (L/ha) (plant/ha) SD (kg/ha) SD (%) SD (%DB)* SD (%DB)* SD 
0 1,353,881 34,020 1,462 305 1.0 0.6 44.2 1.8 25.3 0.5 575.0 
25 1,210,424 33,026 1,190 391 0.9 0.1 44.3 0.6 25.2 0.3 468.0 
50 1,380,780 49,382 1,595 405 0.8 0.2 45.1 1.3 24.0 0.4 641.0 
75 1,595,966 38,232 1,256 388 0.9 0.3 45.7 0.9 24.2 0.5 510.0 
100 1,595,966 38,892 1,442 710 0.7 0.1 44.9 1.6 23.9 1.2 575.0 
125 1,595,966 80,616 815 271 0.8 0.2 45.0 1.1 24.6 0.8 325.0 
* (%DB) denotes dry basis percentage. 
 
 D.2 2010 canola results. 
Irr. TRT 
(% Pan) 
Stand count  Dry yield FFA  Oil  Protein Biodiesel 
Production (L/ha) (plant/ha) SD (kg/ha) SD (%) SD (%DB)* SD (%DB)* SD 
0 1,226,563  40,677  2,507  159 0.5 0.1 42.9 1.0 22.67 1.18 957 
25 1,200,740  220,626  2,801  223 0.4 0.1 43.2 1.5 22.52 1.13 1077 
50 1,230,867  112,886  2,827  405 0.5 0.1 42.8 0.9 22.83 0.74 1077 
75 1,196,437  225,552  2,623  268 0.6 0.2 43.6 1.3 23.07 0.93 1018 
100 1,213,651  103,766  2,760  467 0.6 0.2 43.1 0.9 22.30 0.69 1059 
125 1,325,549  310,187  2,521  685 0.5 0.1 43.2 0.5 22.69 0.81 969 
* (%DB) denotes dry basis percentage. 
16
9 
 
 
 D.3 2008 rotation 1 cotton results. 
Irr. TRT     
(% pan) 
----------------Cotton Analysis---------------- ----------------------Cottonseed Analysis--------------------- 
Stand count  Seed cotton yield   FFA  Oil Protein  Biodiesel 
Production (L/ha) (plants/ha) SD. (kg/ha) SD  (%) SD (%DB)* SD (%DB)* SD 
0 113,780 8,790 3,115 289 0.5 0.2 19.5 0.7 26.2 0.9 302 
25 115,394 14,284 4,026 470 0.7 0.1 20.6 1.8 24.6 1.8 416 
50 112,166 19,567 3,408 866 0.6 0.2 20.4 1.5 24.9 0.8 339 
75 104,904 18,867 3,256 670 0.5 0.1 20.2 1.4 24.5 1.7 326 
100 111,359 6,847 4,368 227 0.6 0.2 21.7 0.8 21.2 1.1 477 
125 107,325 26,437 4,194 151 0.4 0.0 22.2 0.3 20.7 1.3 473 
* (%DB) denotes dry basis percentage 
 D.4 2009 rotation 1 cotton results. 
Irr. TRT     
(% pan) 
----------------Cotton Analysis---------------- ----------------------Cottonseed Analysis--------------------- 
Stand count  Seed cotton yield   FFA  Oil Protein  Biodiesel 
Production (L/ha) (plants/ha) SD (kg/ha) SD (%) SD (%DB)* SD (%DB)* SD 
0 111,669 26,134 3,015 111 8.1 2.5 22.6 1.1 22.4 1.5 344 
25 101,072 31,045 2,802 157 6.7 2.4 22.1 0.6 22.7 1.7 321 
50 108,408 27,327 2,949 226 10.0 3.5 22.0 0.7 23.3 2.2 327 
75 107,593 37,836 2,632 573 7.8 1.5 22.3 1.8 21.0 1.1 301 
100 119,820 18,894 3,037 107 11.5 2.5 23.2 0.4 20.9 0.6 358 
125 97,812 22,900 2,711 190 10.3 1.3 22.2 1.0 21.1 1.2 306 
* (%DB) denotes dry basis percentage
17
0 
 
 D.5 2009 rotation 2 cotton results. 
Irr. TRT     
(% pan) 
----------------Cotton Analysis---------------- ----------------------Cottonseed Analysis--------------------- 
Stand count  Seed cotton yield   FFA  Oil Protein  Biodiesel 
Production (L/ha) (plants/ha) SD (kg/ha) SD  (%) SD  (%DB)* SD (%DB)* SD 
0 101,888 20,339 3,188 302 14.3 2.6 21.6 1.3 24.4 0.2 333 
25 104,333 13,311 2,962 185 10.5 1.7 21.4 1.1 23.3 1.5 314 
50 96,997 16,915 3,042 225 8.6 2.9 21.4 0.7 23.4 2.2 328 
75 107,593 8,418 3,458 335 12.1 1.7 21.8 1.0 23.3 2.3 291 
100 96,182 22,352 3,005 908 7.5 1.8 22.6 0.7 20.5 2.8 363 
125 92,921 24,615 3,150 549 9.1 5.3 22.5 0.3 22.2 2.0 304 
* (%DB) denotes dry basis percentage 
 D.6 2010 rotation 1 cotton results. 
Irr. TRT     
(% pan) 
----------------Cotton Analysis---------------- ----------------------Cottonseed Analysis--------------------- 
Stand count  Seed cotton yield   FFA  Oil Protein  Biodiesel 
Production (L/ha) (plants/ha) SD (kg/ha) SD  (%) SD  (%DB)* SD (%DB)* SD 
0 122779 5,899 3,122 355 0.4 0.1 22.7 1.2 27.2 1.1 373 
25 121,971 13,289 3,312 529 0.4 0.1 22.1 0.8 27.3 1.7 388 
50 130,049 4,847 4,294 471 0.4 0.1 22.9 1.0 25.7 0.4 523 
75 133,280 17,574 4,442 586 0.4 0.1 22.8 1.0 25.1 1.4 553 
100 130,049 15,011 4,925 340 0.4 0.0 22.8 0.6 24.0 0.3 620 
125 129,241 15,607 4,614 737 0.5 0.0 23.5 1.3 23.5 1.6 576 
* (%DB) denotes dry basis percentage 
17
1 
 
 
 D.7 2008 soybean results. 
Irr. TRT   
(% Pan)  
Stand count  Dry yield FFA  Oil  Protein Biodiesel 
Production (L/ha) (plant/ha) SD  (kg/ha) SD (%) SD (%DB)* SD (%DB)* SD 
0 327,083 37,188 3,627 115 0.4 0.1 18.9 1.02 41.7 0.8 611 
25 337,843 46,816 4,432 560 0.3 0.0 18.8 0.65 41.4 0.5 742 
50 333,539 83,858 5,271 381 0.3 0.0 18.4 0.26 41.0 0.6 864 
75 337,843 55,948 5,042 409 0.4 0.2 18.7 0.38 41.0 0.6 840 
100 342,147 146,979 5,229 102 0.3 0.1 19.2 0.27 41.1 0.1 895 
125 335,691 18,594 5,174 419 0.4 0.1 19.1 0.22 40.9 0.3 880 
* (%DB) denotes dry basis percentage 
 D.8 2010 soybean results. 
Irr. TRT   (% 
Pan)  
Stand count  Dry yield FFA  Oil  Protein Biodiesel 
Production (L/ha) (plant/ha) SD  (kg/ha) SD (%) SD (%DB)* SD (%DB)* SD 
0 256,072 14,700 1,238 137 0.6 0.1 18.6 0.4 36.1 1.1 197 
25 258,224 22,773 2,071 534 0.6 0.0 19.5 0.6 35.1 1.1 349 
50 258,224 11,655 2,411 74 0.5 0.1 19.6 0.7 35.5 0.8 418 
75 260,376 8,241 3,222 329 0.6 0.1 20.9 0.5 34.5 0.5 602 
100 251,768 20,640 3,676 265 0.6 0.1 20.7 0.6 34.9 0.5 681 
125 246,747 4,970 4,018 167 0.6 0.1 20.6 0.1 35.4 0.5 743 
* (%DB) denotes dry basis percentage 
 
 
17
2 
 
 
D.9 Irrigation scheduling spreadsheet example.  (continued on next two pages) 
     
RUN-TIME (min) 
DATE 
AWIS 
PAN (in) CC (in.) % CC 
Rainfall 
(in.) 
25% 25% 25% 50% 50% 50% 75% 75% 
CALC. CUMUL. TARGET CALC. CUMUL. TARGET CALC. CUMUL. 
7/1/2010 0.18 2 5% 0.00 0.15 0.68 0.00 0.30 1.37 0.00 0.46 2.05 
7/2/2010 0.23 2 5% 0.00 0.19 0.88 0.00 0.39 1.75 0.00 0.58 2.63 
7/3/2010 0.26 2 5% 0.00 0.22 1.10 0.00 0.44 2.19 0.00 0.66 3.29 
7/4/2010 0.19 2 5% 0.00 0.16 1.26 0.00 0.32 2.51 0.00 0.48 3.77 
7/5/2010 0.23 2 5% 0.00 0.19 1.45 0.00 0.39 2.90 0.00 0.58 4.35 
7/6/2010 0.26 2 5% 0.00 0.22 1.67 0.00 0.44 3.34 0.00 0.66 5.01 
7/7/2010 0.26 4 10% 0.00 0.44 2.11 0.00 0.88 4.21 0.00 1.31 6.32 
 
            7/15/2010 0.24 8 20% 0.00 0.81 5.92 0.00 1.62 11.85 0.00 2.43 17.77 
7/16/2010 0.29 8 20% 0.00 0.98 6.90 0.00 1.96 13.80 0.00 2.93 20.71 
7/17/2010 0.20 8 20% 0.11 0.67 7.58 0.00 1.35 15.15 0.00 2.02 1.73 
7/18/2010 0.12 8 20% 0.00 0.40 7.98 0.00 0.81 15.96 0.00 1.21 2.94 
7/19/2010 0.19 10 25% 0.01 0.80 8.78 0.00 1.60 17.56 0.00 2.40 5.34 
7/20/2010 0.16 10 25% 0.00 0.67 9.46 0.00 1.35 18.91 0.00 2.02 7.37 
7/21/2010 0.24 10 25% 0.00 1.01 10.47 0.00 2.02 20.93 21.00 3.03 10.40 
7/22/2010 0.30 20 50% 0.00 2.53 12.99 0.00 5.06 4.99 0.00 7.58 17.98 
7/23/2010 0.30 20 50% 0.00 2.53 15.52 0.00 5.06 10.04 0.00 7.58 25.57 
7/24/2010 0.33 20 50% 0.00 2.78 18.30 0.00 5.56 15.61 0.00 8.34 7.91 
7/25/2010 0.37 20 50% 0.00 3.12 21.42 21.00 6.24 21.84 22.00 9.35 17.26 
7/26/2010 0.30 26 65% 0.00 3.29 3.71 0.00 6.57 6.42 0.00 9.86 27.12 
7/27/2010 0.25 26 65% 0.00 2.74 6.45 0.00 5.48 11.89 0.00 8.22 8.34 
7/28/2010 0.22 26 65% 0.00 2.41 8.86 0.00 4.82 16.71 0.00 7.23 15.57 
7/29/2010 0.22 26 65% 0.00 2.41 11.27 0.00 4.82 21.53 22.00 7.23 22.80 
7/30/2010 0.16 30 75% 0.52 2.02 13.29 0.00 4.04 3.58 0.00 6.07 5.87 
7/31/2010 0.25 30 75% 0.00 3.16 16.45 0.00 6.32 9.90 0.00 9.48 15.35 
TOTAL 7.27     1.81     21.00     65.00     
17
3 
 
 
RUN-TIME (min) 
 75% 100% 100% 100% 125% 125% 125% ACTUAL IRRIGATION APPLIED (in) 
TARGET CALC. CUMUL. TARGET CALC. CUMUL. TARGET 25% 50% 75% 100% 125% 
0.00 0.61 2.73 0.00 0.76 3.41 0.00 0.00 0.00 0.00 0.00 0.00 
0.00 0.78 3.51 0.00 0.97 4.38 0.00 0.00 0.00 0.00 0.00 0.00 
0.00 0.88 4.38 0.00 1.10 5.48 0.00 0.00 0.00 0.00 0.00 0.00 
0.00 0.64 5.02 0.00 0.80 6.28 0.00 0.00 0.00 0.00 0.00 0.00 
0.00 0.78 5.80 0.00 0.97 7.25 0.00 0.00 0.00 0.00 0.00 0.00 
0.00 0.88 6.67 0.00 1.10 8.34 0.00 0.00 0.00 0.00 0.00 0.00 
0.00 1.75 8.43 0.00 2.19 10.53 0.00 0.00 0.00 0.00 0.00 0.00 
 
           0.00 3.24 3.70 0.00 4.04 7.62 0.00 0.00 0.00 0.00 0.00 0.00 
21.00 3.91 7.61 0.00 4.89 12.51 0.00 0.00 0.00 0.30 0.00 0.00 
0.00 2.70 10.30 0.00 3.37 15.88 0.00 0.00 0.00 0.00 0.00 0.00 
0.00 1.62 11.92 0.00 2.02 17.90 0.00 0.00 0.00 0.00 0.00 0.00 
0.00 3.20 15.12 0.00 4.00 21.90 22.00 0.00 0.00 0.00 0.00 0.31 
0.00 2.70 17.82 0.00 3.37 3.28 0.00 0.00 0.00 0.00 0.00 0.00 
0.00 4.04 21.87 22.00 5.06 8.33 0.00 0.00 0.30 0.00 0.31 0.00 
0.00 10.11 9.98 0.00 12.64 20.97 21.00 0.00 0.00 0.00 0.00 0.30 
26.00 10.11 20.09 20.00 12.64 12.61 0.00 0.00 0.00 0.37 0.29 0.00 
0.00 11.12 11.21 0.00 13.90 26.52 27.00 0.00 0.00 0.00 0.00 0.39 
0.00 12.47 23.69 24.00 15.59 15.11 0.00 0.30 0.31 0.00 0.34 0.00 
27.00 13.15 12.83 0.00 16.43 31.54 32.00 0.00 0.00 0.39 0.00 0.46 
0.00 10.96 23.79 24.00 13.69 13.23 0.00 0.00 0.00 0.00 0.34 0.00 
0.00 9.64 9.43 0.00 12.05 25.28 25.00 0.00 0.00 0.00 0.00 0.36 
23.00 9.64 19.07 0.00 12.05 12.33 0.00 0.00 0.31 0.33 0.00 0.00 
0.00 8.09 27.16 27.00 10.11 22.45 22.00 0.00 0.00 0.00 0.39 0.31 
0.00 12.64 12.80 0.00 15.80 16.25 0.00 0.00 0.00 0.00 0.00 0.00 
97.00     137.00     171.00 0.30 0.93 1.39 1.96 2.44 
 
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2010 Montly Totals     --------------------- Irrigation Applied (in.) --------------------- 
Sprinkler West Days 
AWIS 
PAN (in.) 
Rainfall 
(in.) 25% 50% 75% 100% 125% 
March 31 2.64 4.13 0.00 0.00 0.00 0.00 0.00 
April 30 5.42 2.52 0.00 0.00 0.00 0.00 0.00 
May 31 4.83 5.41 0.00 0.00 0.00 0.00 0.00 
June 30 5.66 2.24 0.00 0.00 0.00 0.00 0.00 
July 31 7.27 1.81 0.30 0.93 1.39 1.96 2.44 
August 31 7.57 1.22 1.94 3.54 5.33 7.27 9.03 
September 30 7.12 1.08 1.64 3.60 5.19 6.94 8.71 
October 31 0.00 0.00 0.00 0.00 0.00 0.00 0.00 
Season Total 245 40.5 18.4 3.9 8.1 11.9 16.2 20.2 
         Jun-Jul-Aug 92 20.50 5.27 2.24 4.47 6.71 9.23 11.47 
Nov - Apr     26.71           
         Canola (bu/ac, MMC)   44.8 50.0 50.5 46.8 49.3 45.0 
  
       
  
Total water (in/ac, Nov - Apr) 
 
26.7 26.7 26.7 26.7 26.7 26.7 
  
       
  
Water prod, bu/in     1.7 1.9 1.9 1.8 1.8 1.7 
         
         Soybean (bu/ac, MMC)   18.4 30.8 35.9 47.9 54.7 59.8 
  
       
  
Total water (in/ac, May-Sept) 
 
11.8 15.6 19.8 23.7 27.9 31.9 
  
       
  
Water prod (bu/in)   1.6 2.0 1.8 2.0 2.0 1.9 
 
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