The Pennsylvania State University

Earth and Mineral Sciences

College of Energy and Mineral Engineering

A COMPREHENSIVE EVALUATION AND DEVELOPMENT OF

ALTERNATIVE BIODIESEL ANALYTICAL QUALITY TESTING

METHODS

A Thesis in

Energy and Mineral Engineering

by

Ryan A. Johnson

© 2011 Ryan A. Johnson

Submitted in Partial Fulfillment

of the Requirements

for the Degree of

Master of Science

May 2011

ii

The dissertation of Ryan A. Johnson was reviewed and approved* by the following:

André Boehman Professor of Fuel Science and Materials Science and Engineering

Thesis Advisor Joseph M. Perez Senior Research Scientist of Chemical Engineering Sarma V. Pisupati Associate Professor of Energy and Mineral Engineering Matthew M. Kropf Post Doctorate of Engineering Science and Mechanics Yaw D. Yeboah Professor of Energy and Mineral Engineering Head of the Department of Energy and Mineral Engineering

*Signatures are on file in the Graduate School

iii

ABSTRACT

This thesis had as its objective the evaluation of current commercial techniques used to assess and

monitor biodiesel quality in industry. It was also essential to statistically determine the reliability of alternative

tests as compared to current ASTM testing methods. Biodiesel quality assurance is a major cost issue for

many small scale producers, while being a major concern for engine manufacturers. The critical tests for

biodiesel fuel quality, defined by BQ-9000, were deemed the most necessary to develop alternative testing

methods which would benefit the biodiesel industry as a whole.

The commercial analytical methods evaluated in this study include QTA, i-Spec and the pHLip test.

In addition, methods based on spectrophotometry, dielectric spectroscopy and ultrasonic velocity were

developed and explored as potential methods for assessing biodiesel quality. Of all the tests evaluated, most

had the potential of acting as a firewall against poor biodiesel quality fuel, but none were found to be capable

of predicting whether the fuel would meet ASTM specification consistently. While the QTA FT-IR rapid

testing unit can measure most of the critical parameters designated by BQ-9000, it was found that results for

key biodiesel quality parameters did not adequately reproduce ASTM results. Yet, the QTA shows promise

for potentially carrying out nearly full-range biodiesel analysis in one test. The other commercial apparatus,

the i-SPEC Q-100, showed highly insignificant results overall. While the test claims to have high potential,

there were no valid results which indicated so. The spectrophotometer test for total glycerin was found to

have mediocre results but has the potential to be a highly inexpensive method to produce reliable results.

Dielectric spectroscopy measurements of biodiesel did not establish usable trends, but set the foundation for

carrying out experiments in-situ for the monitoring of biodiesel either in the facility or as a standalone

method for total glycerin, methanol and free glycerol. The ultrasonic velocity measurements provided

potentially accurate data for monitoring the biodiesel reaction, but may be limited by being very feedstock

dependent.

iv

TABLE OF CONTENTS

LIST OF FIGURES ............................................................................................................................ viii

LIST OF TABLES ................................................................................................................................. x

ACKNOWLEDGEMENTS .................................................................................................................. xi

Chapter 1. Introduction .......................................................................................................................... 1

1.1. Biodiesel as an Alternative Diesel Fuel ...................................................................................... 1

1.2. Overview of Biodiesel Production .............................................................................................. 2

1.2.1. Base Catalyzed Transesterification .................................................................................... 4

1.2.2. Acid Catalyzed Esterification ............................................................................................. 5

1.3. Biodiesel Quality Standardization .............................................................................................. 6

1.3.1. ASTM Standardization of Biodiesel ................................................................................... 6

1.3.2. US Biodiesel Quality Programs .......................................................................................... 7

Chapter 2. Implications of Biodiesel Properties and Impurities on Engines with a Review of the

Standardized Techniques that Measure Them ..................................................................................... 9

2.1. Biodiesel Standard Parameters and Fuel Property Measurement Methods ........................... 9

2.1.1. Mono-, di-, and triglycerides (bound glycerin) ................................................................. 9

2.1.2. Free Glycerol ...................................................................................................................... 10

2.1.4. Methanol Content / Flash Point ...................................................................................... 13

2.1.6. Acid Number ..................................................................................................................... 17

2.2. Fuel and Physical Properties .................................................................................................... 18

2.2.1. Cold Temperature Properties ........................................................................................... 18

2.2.2. Oxidative Stability ............................................................................................................. 19

2.3. Carryover Elements ................................................................................................................... 20

v

2.3.1. Sulfur .................................................................................................................................. 20

Chapter 3. Commercial Alternative Biodiesel Quality Testing Equipment ........................................ 22

3.1. Introduction to Alternative Testing Methods .......................................................................... 22

3.2. pHLip Test ................................................................................................................................ 22

3.3. Mid Infrared Fourier Transform (FT-IR) – QTA System ...................................................... 24

3.4. i-SPEC™ Q-100 Handheld Biodiesel Analyzer ....................................................................... 25

3.5. Methanol Solubility Test (Jan Warnquist’s Conversion Test) ................................................ 26

3.6. Soap and Catalyst Measurement by Colorimetric Titration ................................................... 27

Chapter 4. Analytical Methods Development ...................................................................................... 29

4.1. Spectrophotometric Analysis of Biodiesel for Bound Glycerol Determination .................... 29

4.1.1. General Principles of Spectrophotometry ........................................................................ 30

4.1.2. TSL230R Light to Frequency Converter .......................................................................... 32

4.2. Dielectric Spectroscopy of Biodiesel in MW Regime ............................................................. 34

4.3. Measurement of the Speed of Ultrasound as a Biodiesel Characterization Technique ....... 37

4.3.1. Ultrasound Fuel Quality Measurement Background ..................................................... 37

4.4. Unique In-Column Injection for Total and Free Glycerol Determination by GC ................ 40

Chapter 5. Results and Discussion of Analytical Fuel Quality Techniques ........................................ 44

5.1. Test Samples .............................................................................................................................. 44

5.1.1. Commercial Biodiesel Samples ........................................................................................ 44

5.1.2. Small Scale Batch Biodiesel Samples from Various Feedstocks .................................... 44

5.2. Past Studies of Correlating Two Instruments ......................................................................... 45

5.2.1. 2004 NREL Survey – Two Rancimat Instruments .......................................................... 45

5.3. Methods for Comparing Two Instruments that Measure the Same Parameter .................... 46

5.4. Alternative Testing Technique Analysis .................................................................................. 48

5.4.1. pHLip Test......................................................................................................................... 48

5.4.1.1. Upper Phase (Glycerin Detection) ............................................................................... 49

5.4.1.2. Lower Phase (Acid Value Detection) ........................................................................... 49

5.4.2. Near Infrared – QTA System ............................................................................................ 51

vi

5.4.2.1. Total Glycerin ................................................................................................................ 51

5.4.2.2. Methanol Content .......................................................................................................... 52

5.4.2.3. Acid Number ................................................................................................................. 54

5.4.2.4. Free Glycerol .................................................................................................................. 55

5.4.3. I-Spec Q100 ........................................................................................................................ 57

5.4.3.1. Total Glycerin ................................................................................................................ 57

5.4.3.2. Methanol Content .......................................................................................................... 59

5.4.3.3. Acid Number ................................................................................................................. 60

5.4.4. Methanol Solubility Test ................................................................................................... 61

5.4.5. Spectrophotometric Analysis of Biodiesel for Bound Glycerol Determination ............ 63

5.4.6. Dielectric Spectroscopy of Biodiesel for Total Glycerin ................................................. 70

5.4.7. Ultrasonic Velocity Measurements in Biodiesel for Bound Glycerol Determination ... 72

5.4.8. Unique In-Column Injection Method for Total and Free Glycerol Determination by

GC 74

5.4.8.1. Total Glycerin ................................................................................................................ 74

5.4.8.2. Free Glycerol .................................................................................................................. 76

Chapter 6. Discussion .......................................................................................................................... 78

Chapter 7. Conclusions and Future Work ........................................................................................... 83

7.1. Qualitative Testing Method ...................................................................................................... 83

7.1.1. pHLip ................................................................................................................................. 83

7.2. Quantitative Testing Methods.................................................................................................. 83

7.2.1. QTA .................................................................................................................................... 83

7.2.1.1. Total Glycerin ................................................................................................................ 84

7.2.1.2. Methanol ........................................................................................................................ 84

7.2.1.3. Acid Number ................................................................................................................. 84

7.2.1.4. Free Glycerol .................................................................................................................. 85

7.2.2. I-Spec .................................................................................................................................. 85

7.2.2.1. Total Glycerin ................................................................................................................ 85

7.2.2.2. Methanol ........................................................................................................................ 85

vii

7.2.2.3. Acid Number ................................................................................................................. 86

7.2.3. Spectrophotometer ............................................................................................................ 86

7.2.4. Dielectric Spectroscopy ..................................................................................................... 86

7.2.5. Ultrasound .......................................................................................................................... 86

7.3. Future Work ............................................................................................................................... 87

Appendix A. Sample Sets for Biodiesel Quality Testing ..................................................................... 89

Appendix B. Raw Data of Analytical Instruments .............................................................................. 94

Appendix C. Calculations for Statistical Representation of Results .................................................... 96

References ........................................................................................................................................... 100

viii

LIST OF FIGURES

Figure 1: Biodiesel Production in the United States (7)

Figure 2: Conventional Biodiesel Production Process

Figure 3: Transesterification Reaction (10)

Figure 4: Representative Chromatogram for Determining Free and Bound Glycerol (36)

Figure 5: Diagram of Pensky-Martens Closed Cup Flash Point Tester (37)

Figure 6: Left (54): pHLip standard test vial – Pass. Center two samples (54): Two samples that fail. Right: Highly Pure FAME (distilled) – Pass. Figure 7: pHLip Linear Color Shift with pH in Acidic Range (54) Figure 8: I-Spec Q100 Handheld Unit (60) Figure 9: 27/3 Bound Glycerol Test Result (Fail Sample)

Figure 10: Biodiesel Spectrophotometer Apparatus

Figure 11: TSL230R Block Diagram (68)

Figure 12: TSL230R Spectral Responsitivty at Various Wavelengths (68)

Figure 13: TSL230R Output Frequency (kHz) as a Function of Irradiance (uW/cm2) (68)

Figure 14: Dielectric Response Mechanisms (75)

Figure 15: Dielectric Storage Permittivity and Loss Permittivity (78)

Figure 16: Dielectric Loss of Canola B100 ( ) vs. Frequency (Hz)

Figure 17: Loss Tangent of Glycerol ( ) vs. Frequency (Hz)

Figure 18: Dielectric Loss of Methanol ( ) vs. Frequency (Hz)

Figure 19: Frequency Sweep of Vegetable Oil from 1.2-1.5 MHz

Figure 20: Ultrasonic Velocity Measurement Apparatus (80)

Figure 21: Free Glycerol Concentration vs. FID Response

Figure 22: Monoolein Concentration vs. FID Response

Figure 23: Diolein Concentration vs. FID Response

Figure 24: Triolein Concentration vs. FID Response

Figure 25: Typical Chromatogram of the Modified GC method for Sample A (sec. 5.1.1)

ix

Figure 26: Bound Glycerol Conversion Curve for Various Feedstocks

Figure 27: Rancimat Value (Bosch) vs. Rancimat Value (SwRI) (23)

Figure 28: QTA vs. ASTM D 6942 for Total Glycerin Measurement

Figure 29: QTA vs. EN 14110 for Methanol Content Measurement

Figure 30: QTA vs. ASTM D 664 for Acid Number Measurement

Figure 31: QTA vs. ASTM D 664 for Free Glycerol Measurement

Figure 32: I-Spec vs. ASTM D 6942 for Total Glycerin Measurement

Figure 33: I-Spec vs. EN 14110 for Methanol Content Measurement

Figure 34: I-Spec vs. ASTM D 974 for Acid Number Measurement

Figure 35: Absorbance vs. Total Glycerin at 10:1 Ratio

Figure 36: Absorbance vs. Total Glycerin at less than 0.5 wt. %, 10:1 ratio

Figure 37: Spectrophotometer (10:1 Ratio) vs. ASTM D 6942 for Total Glycerin

Figure 38: Absorbance (9:1 Ratio) vs. Total Glycerin

Figure 39: Spectrophotometer (9:1 Ratio) vs. ASTM D 6942 for Total Glycerin

Figure 40: Absorbance (8:1 Ratio) vs. ASTM D 6942 for Total Glycerin

Figure 41: Spectrophotometer (8:1 Ratio) vs. ASTM D 6942 for Total Glycerin

Figure 42: Dielectric Loss at 7.47 GHz vs. Bound Glycerol Weight Percentage

Figure 43: Maximum Dielectric Loss vs. Bound Glycerol Weight Percentage

Figure 44: Speed of sound measurements of Canola Biodiesel vs. Temperature (80)

Figure 45: Speed of Sound Measurements at 25oC vs. Bound Glycerol Mass Percentage

Figure 46: Modified GC Method Total Glycerin vs. Commercial Testing Lab Total Glycerin

Figure 47: Modified GC Method Free Glycerol vs. Commercial Testing Lab Free Glycerol

x

LIST OF TABLES

Table 1: Common Fatty Acid Chains

Table 2: Acid Number of Various Vegetable Oil Feedstocks

Table 3: ASTM Biodiesel Quality Limits and Testing Procedures

Table 4: Repeatability and Reproducibility for Total and Free glycerol values Table 5: Repeatability and Reproducibility of Methanol Mass Percentage Values Table 6: Pioneering Experiment for Optical Density Tester of Used Cooking Oil Table 7: pHLip Glycerin Analysis Table 8: pHLip Acid Number Analysis

Table 9: 27/3 Methanol Solubility Test of WVO Samples

Table 10: 81/9 Methanol Solubility Test of WVO Samples

Table 11: Statistical Evaluation of QTA Acid Number Correlation

Table 12: Statistical Evaluation of QTA Free Glycerol Correlation

Table 13: Statistical Evaluation of I-Spec Total Glycerin Correlation

Table 14: Statistical Evaluation of I-Spec Methanol Content Correlation

Table 15: Statistical Evaluation of I-Spec Acid Number Correlation

Table 16: 27/3 Methanol Solubility Test of WVO Samples

Table 17: 81/9 Methanol Solubility Test of WVO Samples

Table 18: Statistical Evaluation of Spectrophotometer (10:1 Ratio) Total Glycerin Correlation

Table 19: Statistical Evaluation of Spectrophotometer (9:1 Ratio) Total Glycerin Correlation

Table 20: Statistical Evaluation of Spectrophotometer (8:1 Ratio) Total Glycerin Correlation

Table 21: Statistical Comparison of Alternative Testing Techniques for Total Glycerin and Free

Glycerol

Table 22: Statistical Comparison of Alternative Testing Techniques for Methanol Content and Acid

Number

xi

ACKNOWLEDGEMENTS

I would like to express my gratitude to my project advisor, Dr. Joseph Perez, for his continued

support and guidance through my undergraduate and graduate career here at Penn State. I want to thank Dr.

Matthew Kropf for his utmost support for my endeavors, where his sincere hands-on assistance and

encompassing knowledge allowed me to complete the work that I could not have accomplished on my own.

Furthermore, Dr. Kropf brought me into his own projects of which I have learned so much from. I would

also like to thank the United Soybean Board for funding for my initial semesters of graduate schooling for

working on the Alternative Testing Methods for Biodiesel Project. Howell Rigley with Knightsbridge Biofuels

was so kind as to devote his time and resources for providing the QTA Infrared data in this thesis.

Furthermore I would like to give my greatest support and appreciation of the Chemical Engineering Biodiesel

Research Group, administered by Dr. Joseph Perez and Dr. Wallis Lloyd, for providing me with the

capabilities to carry out biodiesel reactions in a controlled and safe manner and with extensive analytical

equipment for analyzing vegetable oil and biodiesel fuels throughout this project and beyond.

Chapter 1. Introduction

1.1. Biodiesel as an Alternative Diesel Fuel

Biodiesel is a clean burning, alternative diesel fuel produced domestically from various oil seed crops

or rendered animal fats (1-3). The ideal characteristics of biodiesel fuel for our current transportation

infrastructure make it a promising future alternative energy source. The most common method for producing

biodiesel is through a process known as transesterification, which has been carried out for decades (4-5). By

definition, biodiesel is described in ASTM D 6751-10 as “the mono alkyl esters of long chain fatty acids

derived from renewable lipid feedstocks, such as vegetable oils and animal fats, for use in compression

ignition (diesel) engines” (6).

Recent incentives for producing domestic fuels in the United States with a low carbon footprint from

renewable sources have allowed for the quick inception of biodiesel production, as depicted in Figure 1, with

many states mandating 2-5% blend of biodiesel into petroleum diesel (7). As of December 2009, there are

over 122 active biodiesel facilities capable of producing 2 billion gallons per year. Yet, in 2009, U.S. biodiesel

facilities were only running at 25% capacity, with 506 million gallons produced (8). Some of the reasons for

decreased biodiesel production can be attributed to the low cost of diesel relative to feedstock cost, the loss

of subsidies and tax credits and the stricter limits on biodiesel exports into the lucrative European market.

Figure 1: Biodiesel Production in the United States (7)

31

92

246

477

670

506

0

100

200

300

400

500

600

700

800

2004 2005 2006 2007 2008 2009

Mill

ion

Gal

lon

s o

f B

10

0 P

rod

uce

d

Year

2

1.2. Overview of Biodiesel Production

Conventionally, biodiesel is produced through a transesterification reaction of a triglyceride (animal

fat or vegetable oil) with a short chain alcohol (typically methanol) in the presence of a base catalyst (usually

sodium or potassium methoxide). While there are many different configurations of biodiesel processing

equipment and methods, the method utilized at The Pennsylvania State University (PSU) is depicted in

Figure 2. Since PSU produces biodiesel from used cooking oil (yellow grease), a two-step alkali process is

carried out.

Figure 2: Conventional Biodiesel Production Process

Plant oils, animal fats and used cooking oils (UCO) are the main feedstocks used to produce

biodiesel. Through precedent classifications, fats are defined as a solid at 20 oC, whereas oil is liquid at 20 oC,

even though they are both chemically defined as triglycerides. Animals synthesize fats for their own energy

storage whereas plants synthesize oils for energy requirements of the next generation plant. Oils tend to be

concentrated in seeds and nuts. The molecular structure of oils and fats are triglycerides, also known as

3

acylglycerols. They are esters of three long chain fatty acids connected to a three carbon molecule known as

glycerol. Fatty acids are long chain aliphatic carboxylic acids, ranging from 12-20 carbon atoms in length.

Biodiesel will contain a distribution of fatty acid types, known as a fatty acid profile, which is inherent

of the feedstock used. The chemical and physical properties of the oil and the biodiesel obtained from them

vary in relation to the fatty acid type. Fatty acids can vary in molecular weight and the amount of double

bonds along the aliphatic chain, which is highly dependent upon the vegetable or animal source. Fatty acids

are often abbreviated to define these two characteristics. By convention, fatty acid chains are more easily

denoted as CX:Y, where X is the amount of carbon atoms and Y is the quantity of double bonds. If zero

double bonds are present, such as in palmitic acid shown in Table 1, the fatty acid chain is fully saturated. If

double bonds are present then the fatty acid is considered unsaturated, such as in oleic acid shown in Table 1.

The fatty acid length and degree of unsaturation also reveals some inherent qualities of the fatty acids. If a

fatty acid molecule contains more than two double bonds, it will have better cold-flow properties but is

susceptible to oxidative stability. Subsequently, fatty acid molecules with zero or one double bond will have

poor cold flow properties yet are more stable toward oxidation.

Table 1: Common Fatty Acid Chains (9)

Fatty Acid Acronym (Cx:y)

Formula Mol. Weight (g/mol)

Melting Point (oC)

Palmitic acid C16:0 C16H32O2 256.428 63-64

Stearic acid C18:0 C28H36O2 284.481 70

Oleic acid C18:1 C18H34O2 282.465 16

Lioleic acid C18:2 C18H32O2 280.450 -5

Linolenic Acid C18:3 C18H30O2 278.434 -11

The transesterification reaction occurs step wise, with one fatty acid ester chain being removed first

(forming one mono alkyl ester and a diglyceride), the second fatty acid ester removed next (forming two

mono alkyl esters and a monoglyceride), and lastly, reaction of the third fatty acid ester as shown in Figure 3.

4

Figure 3: Transesterification Reaction (10)

The resulting products are three fatty acid methyl esters (FAME) and glycerin. Glycerin is removed

and further purified into a valuable co-product (11). The desired results of the biodiesel process are to break

down the high molecular weight acyglycerols into individual fatty acids and remove the glycerol chain. This

reduces the viscosity of the fuel to the ASTM specified viscosity range for modern diesel engines. Other

benefits of using FAME include reduced tailpipe combustion emissions (12).

Many different types of alcohols can be used to carry out the transesterification reaction, but

methanol is the most often utilized. Ethanol and iso-propanol have also been used to produce biodiesel with

different pros and cons, such as being able to be produce ethanol from renewable sources yet a major

downside is ethanol’s azeotrope with water causing process difficulties. This thesis will focus only on

biodiesel production using methanol.

Methanol has a relatively low boiling point compared to the other biodiesel processing components

and should be removed FAME and glycerol streams. It can be completely removed by distillation.

Methanol’s low flashpoint also categorizes it as a Class I-b liquid, being highly flammable.

1.2.1. Base Catalyzed Transesterification

In order for the transesterification reaction to be carried out to completion in a timely manner, a base

catalyst is required. The most common bases are sodium and potassium hydroxide (NaOH and KOH). These

5

are then converted to the desired catalyst by adding them to methanol to create a solution of sodium or

potassium methoxide (NaOCH3 or KOCH3).

NaOH + CH3OH NaOCH3 + H2O [1]

The above reaction is actually not desired since the water content of the catalyst can cause

complications during the biodiesel process, such as excess soap formation. The usage of commercially

prepared sodium methoxide solution is ideal because there is no preparation required and no water present.

As determined in literature, 0.5% wt. sodium hydroxide (0.664% wt. sodium methoxide) to oil is used for

catalysis while an additional amount of base catalyst is required to react with any free fatty acids (FFA) into

soap. This excess amount of catalyst is determined by titrating the oil, and if not taken into account a certain

amount of catalyst will be consumed according to the following reaction.

FFA + CH3O-Na Soap + MeOH [2]

The sodium methoxide catalyst is necessary for two reasons. The presence of the base catalyst

increases mass diffusion of the reactants for enhanced reaction rates. The increased mass diffusion is caused

by an increase in alcohol solubility into the less polar oil phase, since alcohol is hardly soluble in oil under

normal conditions (13). The methoxide anion of the catalyst is also responsible for cleaving the original ester

bond linkage located on the acylglycerols.

Since the transesterification reaction is an equilibrium reaction, thus it can proceed in reverse. The

reverse reaction of FAME into acylglycerols is inhibited by adding excess methanol (or ethanol) to force the

reaction to the products. Once the methanol and base methoxide are added to the vegetable oil at elevated

temperature, the stepwise reaction in Figure 3 occurs.

1.2.2. Acid Catalyzed Esterification

According to many sources (14-16), the oil or fat used in alkaline-catalyzed transesterification

reactions should contain no more than 1% FFA. If the FFA content exceeds this threshold, saponification

occurs which hinders separation of the ester from the glyceride, facilitates emulsification, consumes the alkali

6

catalyst, and reduces the yield and formation rate of FAME. In a typical reaction of oil with low (<1%) FFA

content, about .5% by mass NaOH is used as catalyst and to neutralize whatever FFAs may be present. As

shown below in Table 2, many feedstock oils exceed this threshold and other pre-treatment steps must be

carried out

Table 2: Typical Acid Number Range of Various Vegetable Oil Feedstocks (17) Oil Canola Rapeseed Soy Jatropha Cuphea

FFA (% mass in oil) .4 - 1.2 .5 - 1.2 .5 - 1.6 3-14 .09- 5

Many sources available deal with this issue by applying the acid pre-treatment esterification method

which converts FFA into FAME. The reaction is catalyzed by concentrated sulfuric acid (14-15), as shown in

reaction 3,

R1-COOH + R2OH H2O + R1-COO-CH2-R2 [3]

The goal of this step is to reduce the FFA content in the oil to <1% prior to the alkaline-catalyzed

reaction, while inhibiting the breakdown of triglycerides.

1.3. Biodiesel Quality Standardization

1.3.1. ASTM Standardization of Biodiesel

Before biodiesel can be sold as a fuel or blending stock, it must first meet a defined standard to

ensure the fuel does not damage engine components. Therefore, the quality control of biodiesel is a necessity

for the successful commercialization of this fuel and its blends (18).As described previously, the

transesterification reaction that produces unpurified FAME will also contain glycerol, alcohol, catalyst, tri, di-

and monoglycerides as well as free fatty acids (19). The American Society for Testing and Materials (ASTM)

Biodiesel Task Force was formed in 1994 to agree upon the fuel quality requirements of biodiesel (20). Since

then, over 10 iterations of the standard, Table 3, have occurred through collaboration between biodiesel

producers, consumers, researchers and engine manufacturers (6, 21). Due to the misrepresentation of

biodiesel which has sometimes been referred to as pure vegetable oil, a mixture of vegetable oils, esters of

natural oils and mixtures of esters and petrodiesel, the ASTM Biodiesel Task Force decided that a written

description of biodiesel was essential. The Task Force adopted the definition of biodiesel as stated in section

7

1.1. It was also decided to develop a standalone specification for pure biodiesel and for blends of biodiesel

into petrodiesel, D 6751-10 (Table 3) and D 7467, respectively.

Table 3: ASTM D 6751-10 Biodiesel Quality Limits and Testing Procedures (21)

1.3.2. US Biodiesel Quality Programs

The National Biodiesel Board (NBB), a collaboration of biodiesel groups in industry, has been very

influential in developing biodiesel quality programs (22). NBB has supported the necessity of conforming to

ASTM specifications by encouraging its use and adoption. The NBB has taken biodiesel fuel quality one step

further, however, by sponsoring a voluntary fuel supplier certification program called BQ-9000. This

accreditation program provides producers with a “good housekeeping” seal of approval, to leverage their

sales and by increasing the confidence of engine companies and consumers that BQ-9000 certified marketers

will meet ASTM specifications

NREL in collaboration with the National Biodiesel Board has also conducted quality surveys to

assess the quality of fuel being distributed through the United States. The results of three surveys in recent

years summarized key issues of biodiesel quality control.

The main result of the biodiesel quality survey in 2004 (23) was that out of 27 B100 samples, 85%

met all of the required ASTM D 6751-03a parameters. The 4 of samples that failed were either out of spec.

for acid number, total glycerin or phosphorus (one sample exceeding total glycerin limit by 5 fold).

8

Furthermore, it was found that 26 out of 27 samples would fail the EN oxidative stability limit by Rancimat,

requiring an induction period of 6 hours. The main result of the B20 blend quality survey was that 36% of the

samples contained biodiesel contents between 7-98% (outside the range of 18-22%). It was determined that

the cause was that conventional splash blending methods were not providing homogenous mixtures. All B20

blend samples passed quality testing requirements in accordance to D 975.

A subsequent survey was carried out in 2006 which discovered high failure rates against ASTM D

6751, with 59% of the samples not meeting specification (24). The majority of failing samples exceeded the

allowable total glycerin (33%) or did not meet the minimum flash point specification (30%). The samples

obtained were derived from soy, canola, palm, rapeseed or animal fat. The 2006 failure rate was alarming

which showed serious quality control issues. The conclusion of the study influenced the National Biodiesel

Board to stress fuel quality and subsequently released several informational documents to augment fuel

quality programs (25).

NREL surveyed B20 blends once again in 2008 (26) and were tested with the new B6-B20 ASTM

specification, D 7467. In this study, it was found that 40% of 33 samples were not between 18-22% biodiesel,

compared with 36% in 2004. Most samples that were below 18% contained B2, B5 and B10 making it

necessary to enforce pump labeling. No B100 samples were analyzed in 2008.

1.3.2.1. BQ-9000

The BQ-9000 National Biodiesel Accreditation Program is a cooperative and voluntary program

which subjects the facility to test each production lot of B100 with full specification testing under ASTM

6751-08b until there is sufficient confidence that the production process consistently produces fuel that is up

to standard. The quality system addresses many aspects of biodiesel production such as storage, sampling,

testing, blending, shipping, distribution and fuel management practices (27). Once a producer becomes

certified under BQ-9000, each lot of fuel produced is subjected to critical specification testing. Every six

months the facility is subjected to full range ASTM 6751 testing of their fuel, while once a month a

sodium/potassium and calcium/magnesium test shall be run (27).

9

The critical tests were deemed most important for biodiesel reliability. Furthermore, many of the

other testing parameters in D 6751 are not as likely to go out of specification once the process method is

fully developed. The critical tests are as follows (27):

Alcohol control, water and sediment, cloud point, acid number, free glycerin, total glycerin, sulfur,

oxidative stability, visual appearance and cold soak filterability test.

Chapter 2. Implications of Biodiesel Properties and Impurities on Engines with a Review of

the Standardized Techniques that Measure Them

2.1. Biodiesel Standard Parameters and Fuel Property Measurement Methods

The development of internal combustion engines over the past century has resulted from the

complimentary refinement of the engine design and fuel properties. As such, engines have been developed to

utilize the properties of the fuels that were available. Replacement of existing fuels with new fuel formulations

requires understanding the critical fuel properties. To ensure that the new fuels can be used effectively

requires consistent fuel quality. The critical tests for biodiesel fuel quality defined by BQ-9000 were deemed

the most necessary by our group for which to develop and test alternative testing methods. Being able to use

alternative tests for the parameters that need to be tested the most often would alleviate financial burdens on

biodiesel producers, reduce turnaround times for sample analysis all while ensuring biodiesel quality.

Discussed in this section will be some key fuel properties as well as the ASTM and EN methods to measure

these properties, as required in D 6751, Table 3.

2.1.1. Mono-, di-, and triglycerides (bound glycerin)

As seen previously in Figure 3, the biodiesel transesterification process is a three step equilibrium

reaction. The equilibrium constants for each reaction are pushed towards the product by optimizing the

reaction conditions with excess methanol and the correct ratio of alkaline catalyst to triglycerides. Since the

chemical reaction is reversible, there will almost always be left over un-reacted acylglycerols in the final

product which are in the form of mono-, di- and triglycerides. The amounts will depend on process

conditions. There are no commercial separation techniques for removing un-reacted acylglycerols, with one

10

exception that lower concentrations can be achieved if the final ester product is distilled (28). The ASTM test

method uses gas chromatography (GC) to analyze the three types of un-reacted esters and combines them

into a term known as bound glycerol. Bound glycerol accounts for only the glycerol backbone to be counted

toward the impurity concentration. The following calculations show how each mono-, di- and triglycerides is

converted to bound glycerol for quantification (29),

Bound glycerin = GlM, GlD, GlT) [4]

GlM = 0.2591 monoglyceride, mass%) [5]

GlD= 0.1488 diglyceride, mass%) [6]

GlT = 0.1044 triglyceride, mass%) [7]

Where GlM, GlD and GLT are the mass percentage concentrations of monoglycerides, diglycerides

and triglycerides, respectively. It is significant to realize that the fatty acid molecule attached to the glycerol

backbone is the majority of the molecular weight of the un-reacted ester, which is taken into account in

equations 5-7. While ASTM does not set explicit limits for individual partial glycerides, the EN standard does.

Recently it has been found that large proportions of monoglycerides have been the cause of cold weather

issues which gave rise to the importance of implementing the cold soak filtration test into the ASTM

standard (6).

2.1.2. Free Glycerol

As shown in Figure 3, glycerol is a major product of transesterification being approximately 10% by

weight of the biodiesel product. Thus separating glycerol sufficiently is a major concern for fuel quality (30).

Free glycerol in significant concentrations will separate out of the biodiesel either in storage or in the fuel

tank. Due to glycerol’s hydroscopic properties, it will attract other polar compounds such as water,

monoglycerides and soap. The increased concentration of these compounds will augment damage to the

injection system (31). Concentrated free glycerol may also clog up the fuel filter and can result in increased

aldehyde emissions (32).

11

2.1.3. Total Glycerin

For clarity, the ASTM requirement for bound glycerol is combined with the amount of free glycerol

into a term known as total glycerin. This places all glycerol backbone moieties into one category. Biodiesel

fuel that is out of specification for total glycerin can lead to engine coking which will cause the formation of

deposits on injection nozzles, pistons and valves (33). While determining bound glycerol in biodiesel using

GC has been previously proven (34), the GC method was augmented to include the determination of both

free and bound glycerol to suffice for the ASTM standard (35).

2.1.3.1. ASTM D 6584: Standard Test Method for Determination of Free and

Total Glycerin in B-100 Biodiesel Methyl Esters By Gas Chromatography

The ASTM D 6584 standard quantitatively determines the amount of free glycerin in the range of

0.005 to 0.05 mass% and total glycerin in the range of 0.05 to 0.5 mass% by GC. Detection limits are 0.001%

mass% for free glycerin and 0.02% mass% for mono-, di- and triglycerides. The ASTM GC procedure is as

follows (29):

Column: Non-Polar, high-temperature capillary column coated with 95% dimethyl – 5% diphenyl-

polysiloxane stationary phase, 10-15 m length with 0.32 mm inner diameter and 0.1 mm film thickness. A

guard column is recommended for robustness of the column due to potential sample contaminants and high

oven temperatures.

Injection: 1-2μL, Cool on-column injection.

Detection: Flame Ionization, 380 oC

Carrier gas: Helium or Hydrogren, 4mL/min.

Oven Temp.: 50 oC (hold 1 min) to 180 oC @ 15 oC/min (hold 7 min) to 230 oC @ 30 oC/min to 380 oC @

30 oC/min (hold 5 min).

Sample Preparation: Since glycerol and bound glycerol are essentially non-volatile compounds, they need to

be treated with a silyating agent to enable them to be vaporized during the separation. The free hydroxyl

12

groups of the sample are silyated with N-methyl-N-trimethylsilytrifluoracetamide (MSTFA) by shaking for

three minutes and let stand for 20 minutes. The sample mixture is then diluted with heptane to quench the

silyation reaction.

Standardization: Internal standards are utilized to account for any potential interference during the injection

or column degradation. 1,2,4 butanetriol is the standard for free glycerol and 1,2,3 tricaproylglycerol

(tricaprin) is the standard for mono-, di-, and triglyceride.

Calibration: The detector response is calibrated with known concentrations of glycerol, mono-, di- and

triglycerides alongside the internal standards, butanetriol and tricaprin. Five different concentrations are run

to develop a linear calibration curve for each component as well as to designate retention times of each

compound. Furthermore, a mixture of monopalmitin, monostearin and monoolein need to be run to detect

separate retention times to distinguish between saturated and unsaturated monoglycerides. Below is a

chromatogram of a biodiesel sample injection with labeled component peaks.

Calculation: In utilizing the peak areas of each compound and internal standards, as labeled in Figure 4, the

mass percentage of each component is determined by the previously determined calibration curves.

Figure 4: Representative Chromatogram for Determining Free and Bound Glycerol (36)

13

2.1.4. Methanol Content / Flash Point

The determination of the amount of methanol either by GC or flash point ensures that the majority

of methanol used in production is removed from the fuel. Methanol content is of concern due to both fire

safeties during transport and storage as well as the corrosive nature of methanol. Furthermore, methanol

makes biodiesel a toxic substance. Biodiesel does have a beneficial characteristic in that its flashpoint is over

twice that of its petroleum diesel counterpart, with values between 130 oC and 200 oC whereas petroleum

diesel is approximately 64 oC (33). Yet, the high flashpoint of biodiesel will decrease rapidly with increasing

amounts of residual methanol. Since methanol content and flashpoint are correlated, the biodiesel ASTM

requirements allow for either the determination of flashpoint of biodiesel or the mass percentage of

methanol. The ASTM spec. for flashpoint, 130 oC, limits the amount of methanol to approximately 0.1- 0.2%

by mass in the fuel. The removal of residual methanol can be accomplished by distillation or repeated water

wash steps.

2.1.4.1. ASTM D 93: Standard Test Methods for Flash Point by Pensky-

Martens Closed Cup Tester

A key property in determining the flammability of a fuel, and in this case methanol content, is to

determine the fuel’s flash point. The flashpoint is the lowest temperature to which an ignition source applied

above the liquid surface layer will cause the fuel vapors to ignite. The ASTM method D 93 restricts the flash

point of biodiesel to 130 oC, which will ensure that the methanol content is below 0.2% by mass.

The Pensky-Martens is the most widely used flashpoint apparatus and can be run manually or

automatically. The fuel sample is heated at a regulated rate with stirring and a flame is passed over the fuel

sample in certain intervals. When the fuel sample reaches the flashpoint, the fuel vapors will ignite due to the

presence of the flame and air. The ignition is easily detectable by human sight or by a pressure sensor. Figure

5 depicts the flash point apparatus where a sample cup is placed in a heating block with an agitator. A

temperature ramp occurs where an external flame is applied at specific intervals with an adequate air-to-fuel

14

ratio to detect flammable vapors. The Pensky-Martens Closed Cup Flash Point Tester is depicted below in

Figure 5.

Figure 5: Diagram of Pensky-Martens Closed Cup Flash Point Tester (37)

2.1.4.2. EN 14110- Residual Methanol in B100 Biodiesel by Headspace-Gas

Chromatography

An alternative method to measuring flashpoint is to determine the mass percentage of methanol

present in biodiesel by GC (38). The biodiesel ASTM 6751 requirement of methanol is 0.2% by mass carried

out as per EN 14110. The method can determine methanol concentrations from 0.01 to 0.5% mass. If an

automatic headspace injector is available, internal standardization is not required. Manual headspace injection

utilizes a small constant amount of 2-propanol as an internal standard to account for variances in sample

heating, syringe handling and injection.

Column: Non-polar, capillary column, 100% dimethyl polysiloxane.

15

Injection: 250 μL manual headspace injection into split injector (flow rate: 50mL/min).

Detection: Flame Ionization, 240 oC

Carrier gas: Helium.

Oven Temp.: 50 oC, isothermal

Sample Preparation: 5mL of biodiesel and 5uL of 2-propanol are added into a hermetically sealed

headspace vial and heated at 80 oC for 45 minutes. This will allow the vapor phase to reach equilibrium with

the sample mixture.

Standardization: An internal standard is utilized to account for any potential interference during the manual

injection procedure. 5μL of 2-propanol is added to each sample.

Calibration: Three calibration standards are made, all starting with the same prepared biodiesel solution with

no methanol content. The beginning biodiesel blendstock is prepared by either vacuum distillation or multiple

distilled water washing steps with subsequent drying. Known amounts of high purity methanol are mixed into

the blendstock. The blendstock with methanol is then diluted twice for a total of three samples along with

5μL of 2-propanol as an internal standard (internal standard for manual injection only) to produce a 3 point

linear calibration curve. From the linear FID response of methanol, a calibration factor, F, is determined for

use in the following calculation section.

Calculation: In utilizing the peak areas of both methanol and the internal standard 2-propanol , the mass

percentage of methanol is calculated as shown in equation 8,

[8]

where,

F is the calibration factor obtained from the linear response curve

Sm is the peak area of methanol

16

Ci is 2-propanol content added to the sample, expressed in % mass

Si is the peak area of 2-propanol

2.1.5. Water and Sediment

Water can be present in the final biodiesel product either due to water being present in the feedstock

or due to water washing steps. Furthermore, biodiesel is hygroscopic so it can absorb up to 1500 ppm before

it becomes saturated, thus it needs to be reduced well below the limit by drying (28). Water content above

1500 ppm will eventually separate during storage, which is known as free water, and will promote microbial

growth causing eventual sludge and slime formation. The sludge that is formed is then known to block up

fuel filters (28). Furthermore, free water is also associated with hydrolysis reactions, converting biodiesel into

free fatty acids, causing an increased acidity of the fuel which can both block up fuel filters or cause

corrosion. Water can also be a leading cause of corrosion of chromium and zinc parts located in the engine

(31). High water content may also cause poor combustion, plugging and smoking. The respective maximum

concentration of water for fossil diesel fuel is less than half of the values required for biodiesel water content,

but it is easily met due to the non-polar nature of the fuel, thus the water will sink to the bottom of the fuel

tank.

2.1.5.1. ASTM D 1796 – Standard Test Method for Water and Sediment in Fuel

Oils by Centrifuge Method

Water and sediment is a test that “determines the volume of free water and sediment in middle

distillate fuels having viscosities at 40 oC in the range of 1.0-4.1 mm2/s and densities in the range of 700 to

900 kg/m3. The test ensures that there will be no free water present to settle out during storage and also acts

as a firewall for the cleanliness of a fuel by measuring sediment. The described biodiesel quality standard

limits water and sediment to 0.05% by volume (39).

The ASTM test method centrifuges 100mL of biodiesel in a conical centrifuge tube between 500 and

800 relative centrifugal force (rcf) for ten minutes. Water and sediment are visible below the biodiesel layer

and are measured quantitatively.

17

2.1.5.2. EN ISO 12937

While the ASTM D 6751 standard does not utilize EN ISO 12937, this test is carried out at PSU as

well as many other biodiesel production companies. The analytical procedure for the determination of water

in both biodiesel and fossil diesel fuel involves titration, by means of a Karl Fischer Coulometric Titration

apparatus. The basic principle of this procedure is a reaction between I2 and SO2, which only occurs in the

presence of water. Since the water and sediment content is limited to 0.05% by volume, this can be adjusted

for the coloumetric titration to be approximately 500 ppm by weight (40).

2.1.6. Acid Number

The acid number of biodiesel fuel is a measurement of the free fatty acids (FFA) or mineral acids

present in the fuel. It is expressed in mg KOH required to neutralize 1g of sample. The acidity of the fuel can

exceed the limit due to a variety of factors during the production process. Any FFA that is present in the

starting feedstock is converted to soap during the transesterification process. The resulting soap can either be

washed out of the fuel phase or in some cases is reverted back to FFA using ion exchange resins. Also, the

acid treatment of soaps will form FFA. Furthermore, FFA can indicate that the fuel has oxidized past its

stability point or be due to hydrolytic cleavage of ester bonds. FFA content higher than the prescribed limit is

known to clog fuel filters, lead to engine deposits in fuel injector, catalyze polymerization in hot recycling fuel

loops and lead to corrosion (41).

2.1.6.1. ASTM D 974 – Standard Test Method for Acid and Base Number by

Color-Indication Titration

This method does not distinguish between acidity caused by mild carboxylic and strong mineral

acids.

Titration Reagent: 0.1 M alcoholic KOH standardized with pH electrode against oxalic acid to detect

molarity up to 0.0005.

Color indicator: 10 g/L p-Naphtholbenzein Indicator (in titration solution) used for color indication

from orange to green even in highly opaque samples.

18

Titration Solvent: 100:99:1 toluene, isopropanol, water

Procedure

o 100mL of titration solvent is blanked to determine acidity of solvent.

o Approx. 20g of fuel is weighed to the nearest 0.001mg

o Fuel sample is added to 100mL of titration solvent

o 0.5mL of color indicator added to titration mixture.

o Stirred and titrated from red to dark green/brown using micro-burette with 0.05mL

markings.

o Calculation of acid number is as follows

[9]

where,

A= mL KOH solution required for titration

B= KOH soln. required for blank

M = molarity of KOH (0.1 M)

W = weight of sample titrated, g.

2.2. Fuel and Physical Properties

2.2.1. Cold Temperature Properties

Biodiesel can be the source of major fuel reliability issues at low temperature due to the nature of the

fatty acid molecules present in certain feedstocks. Solidification points of FAME depend on chain length and

degree of unsaturation, with long-chain saturated FAME having the least favorable cold-temperature

properties. Thus, biodiesel fuels derived from feedstocks rich in these compounds, such as tallow and palm

methyl esters, may even be problematic at room temperature,

19

The partial solidification due to crystal formation or gelling of the fuel at low temperatures can lead

to blockage of the fuel filter, which can cause issues during both engine start-up and operation. The cloud

point is an important parameter to monitor for biodiesel since biodiesel fuels will cause operational issues at

higher temperatures than petroleum diesel fuel.

2.2.1.1. ASTM D 2500 – Cloud Point of Petroleum Products

The cloud and pour point of biodiesel fuel and biodiesel blends are of particular importance during

cold weather usage, especially for emergency vehicle reliability. As described in ASTM D 2500, the cloud

point is the temperature at which a cloud is formed due to the crystallization of fatty acid chains that will

appear in the liquid upon cooling (44). The cloud point is measured by cooling the sample at a specific rate

and visually inspecting for a haze to begin forming. The pour point stands for the lowest temperature to

which the sample may be cooled while still retaining its fluidity.

2.2.2. Oxidative Stability

Due to the chemical composition of FAME, biodiesel fuel has inherent instability in the presence of

oxygen. Oxidation of FAME is augmented with increased amounts of unsaturated fatty acids, as the

methylene groups adjacent to double bonds are particularly susceptible to radical attack, which is the first step

of fuel oxidation processes (45). The formed hydroperoxides from radical attack may polymerize with other

free radicals to form insoluble sediments and gums, which are associated with fuel filter plugging and deposits

within the injection system and the combustion chamber (46). The products of fuel oxidation are

accompanied by an increase in viscosity. Furthermore, the oxidation of hydroperoxides may also result in the

formation of aldehydes, ketones and short-chain carboxylic acids, which are linked to increased corrosion of

the injection system caused by the low pH (45).

Apart from the fatty acid composition of the feedstock, the content of natural antioxidants, such as

carotenes and tocopherols, has been identified as beneficial components for oxidative stability. In general,

antioxidant concentrations are high in non-distilled fuels prepared from fresh vegetable oils, whereas hardly

any antioxidants are contained in distilled samples or in samples prepared from used frying oil. The addition

20

of synthetic antioxidants has been identified as a viable means of improving oxidative stability. A few that

have been realized for increased oxidative stability are tert-butyl hydrochinone (TBHQ), pyrogallol BHT and

propylgallate (47). Since Rancimat induction period have been found to decrease after prolonged periods of

storage, antioxidants are added in comparatively high concentrations to ensure that fuels will still meet the

specifications when ready for consumption (47).

2.2.2.1. ASTM and EN Biodiesel Stability Methods

2.2.2.1.1. EN 14112 - Rancimat

The standard analytical method for the determination of biodiesel oxidation stability is a method

derived from food chemistry, which known as the Rancimat (48). In the Rancimat procedure a fuel sample is

placed in the presence of elevated temperatures (110°C) and air to accelerate the oxidation process. The

effluent gases are sparged into a cell of distilled water, of which the conductivity is constantly recorded. When

the sample reached a critical point in oxidation, a sharp increase of conductivity can be observed. The period

of time up to this point is called induction period (IP) and is expressed in hours. Systematic tests showed that

Rancimat induction period is well correlated to other biodiesel quality parameters, such as peroxide value,

anisidine value, kinematic viscosity, ester content, acid value, and polymer content (49).

2.3. Carryover Elements

2.3.1. Sulfur

Fuels with high sulfur content have been associated with negative impacts on human health and on

the environment, which is the reason for the current tightening of national limits as per the Clean Air Act.

Vehicles operated on high-sulfur fuels produce more sulfur dioxide and particulate matter (50). Furthermore,

fuels with high sulfur levels may increase engine wear and reduce the efficiency and the life span of oxidation

catalytic converters and/or denitrification after-treatment systems.

Biodiesel fuels are inherently sulfur-free, as only trace amounts of sulfur can be detected from minor

components within the feedstock, such as glucosinolates and contamination of protein material (51).

Secondly, it is also possible that when an acid esterified fuel is produced, sulfuric acid can carry over into the

21

final fuel. Due to FAME’s inherent low sulfur quality, biodiesel exhibits tremendous advantages over

petrodiesel in terms of sulfur dioxide emissions of which may lead to particle-bound mutagenicity (51).

Ultra low-sulfur diesel has been mandated in recent years, but it was found that the resulting fuel

lacks in lubricity, which can lead to injection pump failure. (52) This phenomenon is due to the removal of

nitrogen and oxygen compounds, normally responsible for lubrication, during the desulfurization process.

The addition of small proportions of biodiesel has been found to alleviate the lack in lubricity, due to

FAME’s lubricating qualities.

22

Chapter 3. Commercial Alternative Biodiesel Quality Testing Equipment

3.1. Introduction to Alternative Testing Methods

The United Soybean Board’s Biodiesel Technical Workshop identified the immediate need to

evaluate and develop analytical methods that could potentially be quicker and less expensive for determining

biodiesel quality. It was designated to produce data on current commercial or unidentified analytical

techniques that could be incorporated into D 6751. Furthermore, field tests that provide qualitative results,

which by nature could never be adopted into D 6751, were also targeted. Field tests are becoming a necessity

since they could provide useful and immediate feedback to distributors and users. The benefits of doing so

could boost consumer confidence by ensuring biodiesel reliability.

Our project group also developed and evaluated potential testing techniques that were aimed to

analyze critical parameters of biodiesel quality. In doing so we identified techniques that could either be used

in-situ for current biodiesel production equipment to monitor biodiesel quality in real time, or to be a stand-

alone test to potentially replace ASTM techniques.

3.2. pHLip Test

The pHLip test was developed to provide a qualitative means for detecting off-spec fuel due to a

variety of trace contaminants in a quick and inexpensive manner. The pHLip test is provided by CytoCulture

International Inc (53). While this test can be only used as a firewall since it cannot provide quantitative

results, thus would not be considered relevant to ASTM methods, it can serve as a tool for ensuring low

quality biodiesel does not get used commercially on-road.

Figure 6 depicts the reference vial on far left which indicates how a high quality B100 sample should

appear after shaking and let stand for 10 minutes. The top phase of the vial, being the organic phase, will

contain the biodiesel sample. The phase interface in the middle should be clean and have a mirror finish.

Lastly, the bottom phase, or the polar phase, contains an indicator solution that can change color if the

biodiesel contains an acidic or basic contaminant. The two samples in the center can easily be determined as

23

a sample that failed due to a variety of contaminants. The sample on the right is a BQ9000 commercial

sample of utmost high quality and passed the pHLip test similarly to the reference vial.

Figure 6. Left (54): pHLip standard test vial – Pass. Center two samples (54): Two samples that

fail. Right: Highly Pure FAME (distilled) – Pass.

If the upper phase which contains the biodiesel becomes foggy or opaque, it indicates that the

biodiesel contains contaminants that can readily absorb moisture. These contaminants include bound

glycerides, free glycerol and oxidized esters. Secondly, the middle phase separation can contain emulsions or

debris that indicates monoglycerides or free glycerol contaminates the biodiesel.

The main contaminants that can be observed in the lower polar phase are catalyst contaminants, FFA

or oxidized fuel, which is shown by the color indicator solution. The alkaline catalyst will turn the cherry red

bottom phase to purple. The acidic FFA or oxidized fuel will cause the bottom phase to turn orange to

yellow. The color shift due to pH change is indicated in Figure 7. While it can be beneficial to estimate the

acidity or alkalinity of the fuel, this test can be interpreted differently by different users. The linear change in

color due to pH makes it very hard to determine if a sample is below or above the ASTM limits for acid, as it

will be somewhere between orange and yellow. Alternately, if the bottom phase becomes cloudy or opaque,

this will indicate residual soaps contaminate the biodiesel.

24

Figure 7: pHLip Linear Color Shift with pH in Acidic Range (54)

3.3. Mid Infrared Fourier Transform (FT-IR) – QTA System

The Cognis QTA® (Quality Trait Analysis) System is service providing a rapid, on-site analysis and

management system (55). The system communicates over the internet with the Cognis QTA systems central

processor providing rapid analyses to the user. While FT-IR equipment is widely available, the Cognis QTA

system is unique since it provides sample analysis for the operator.

The QTA system for biodiesel analysis begins by digitizing the infrared spectra of a biodiesel sample

using mid-infrared technology. The algorithm for determining quality of B100 from various feedstocks is

formed though the database containing hundreds of samples.

The QTA On-Demand measures many of the necessary parameters required to assess the quality of a

B100 batch including free and total glycerin, acid number, cloud point, moisture, mono-,di-, and triglycerides,

oxidative stability, sulfur and methanol content. The cost of the system was prohibitive for this study but

samples were analyzed through one of the collaborating laboratories.

Mid-infrared (4000 cm-1 – 625 cm-1) spectroscopy analyzed the vibrational states of compounds

through the covalent bonds between atoms. Previous groups have identified FT-IR as an informative tool to

0

0.5

1

1.5

2

2.5

3

3.0 4.0 5.0 6.0 7.0 8.0

pH

A 5

80n

m C

OL

OR

25

monitor the transesterification reaction (56). Different bonds at different energy states will absorb IR energy

at specific frequencies, which is detected and analyzed by Fast Fourier Transform to produce a spectrum of

absorption. Table 4 depicts various covalent bonds contained in biodiesel fuel which may give rise to

information on concentrations of impurities.

Table 4: Various Functional Groups That May Give Rise to Biodiesel Quality Parameters (57-59)

Compound Biodiesel/ Acylglycerol

Biodiesel Conversion

Glycerol /MeOH

MeOH FFA FFA Unsaturation (Cloud Point)

Functional Group

RCOOR, Aliphatic

Ester Hydroxide

Alcohol RCOOH, carboxylic acid

RCOOH, carboxylic acid

HRC=CRH, Alkene

Bond C=O C-O R-O-H C-O C=O O-H C=C-H

Wave-number

1735 cm-1 1300-1060 cm-1

3600-3200 cm-1

1050 cm-1

1710 cm-1 3000 cm-1 3300-3000 cm-1

3.4. i-SPEC™ Q-100 Handheld Biodiesel Analyzer

The i-SPECTM is a hand held instrument, Figure 8, of Paradigm Sensors (60) that utilizes an electrical

measurement technique known as Impedance Spectroscopy (IS). A small amplitude of AC voltage of varying

frequency is applied to the sample under analysis and the measured response of the individual frequencies are

incorporated into proprietary algorithms which allow analysis of blend composition (2-99%), total glycerin (in

B6-B99 & B100), methanol content (B100), and acid no (B100). The algorithms are the result of correlations

derived between the electrical characteristics of a broad range of biodiesel samples and their corresponding

physio-chemical attributes as determined from appropriate reference standard analyses.

The cost of the unit was prohibited for this study. However, collaboration of Paradigm resulted in

use of a unit. Sample cartridges were purchased for the study. The I-SPEC operates using a single–use test

cartridge that is inserted into the hand-held unit. The sample to be tested is injected into a cartridge after a

26

field calibration is performed on the empty cartridge. Sample size required is approximately 0.5 ml. Test

results are displayed on an LCD screen and can be printed using a built-in IR link.

Figure 8: I-Spec Q100 Handheld Unit (60)

3.5. Methanol Solubility Test (Jan Warnquist’s Conversion Test)

The Jan Warnquist’s conversion test is carried out by many small scale producers to act as a quick

pass/fail test (61). It is highly discussed and revered on many biodiesel community forums and home-brew

process guides. The premise is that bound glycerol, mainly triglycerides, are not soluble in methanol at room

temperature at a vol:vol ratio of 27 parts methanol and 3 parts biodiesel. The test is carried out at 20-25 oC

(room temperature) in a tall glass jar where biodiesel is added to methanol and then shaken vigorously. If any

bound glycerol settles out on the bottom, Figure 9, which is known as a precipitation, then the fuel will fail

ASTM specification. If there is no precipitation, then there is a “high” chance the fuel will pass the 0.24 % wt.

ASTM limit. It is known to work on washed and unwashed biodiesel.

Another aspect to the test is that some B100 mixtures will make the MeOH in the 27/3 test turn

cloudy, or opaque. While the small-scale community says this is due to a multitude of factors that provides no

qualitative information, the experiments run here where bound glycerol was the only impurity showed the

methanol clarity was dependent on conversion.

27

Figure 9: 27/3 Bound Glycerol Test Result (Fail Sample)

Factors that may influence the test making it irreproducible:

Fatty acid profile

o Unsaturated fatty acids may be harder to solubilize in methanol.

Bound glycerol distribution

o Triglycerides will much more easily fall out of solution than monoglycerides, thus a

fuel that has a large amount of monoglycerides may pass the 27/3 test yet still fail

ASTM bound glycerol limits.

3.6. Soap and Catalyst Measurement by Colorimetric Titration

During process optimization and product quality assessment, it can be useful to know the amount of

soap formed, where the catalyst resides, and how effective the washing process is in removing these two

compounds.

A simple titration procedure can be used to measure the amount of soap and catalyst. The titration

procedure consists of titrating biodiesel, wash water or glycerol with a 0.01 N solution of HCl to the

phenolphthalein end point. This gives an estimate of the amount of catalyst. Then, a few drops of

bromophenol blue indicator are added and the titration continued to the color change for that indicator. This

gives an indication of the amount of soap. In the first titration, the HCl neutralizes the alkali catalyst, so when

the phenolphthalein indicates that the solution has become neutral, then all of the catalyst has been measured.

Then, if the titration is continued, the HCl, as a strong acid, begins to split the soap molecules to free fatty

acids and salt. When the pH reaches about 4.5, where the bromophenol blue changes color, then this

28

indicates that the HCl has split all of the soap. It is now lowering the pH, so it has protons to donate since

the soap has all been split. The procedure utilized at Penn State is a modified version of AOCS method Cc

17-79, soap in Oil (62).

29

Chapter 4. Analytical Methods Development

4.1. Spectrophotometric Analysis of Biodiesel for Bound Glycerol Determination

Initial studies were carried out at PSU to optimize the methanol solubility test to no avail, but a

modified version is summarized in Table 6. The optimization of the solubility test found that when the

volumetric proportion of biodiesel to methanol at 1 to 9 was chilled to 10 oC, the mixture becomes turbid

even if there are small quantities of bound glycerol, but will remain clear if the sample below the ASTM limit

of bound glycerol. This phenomenon is hypothesized to take place due to the bound glycerol becoming

emulsified in methanol at decreased temperatures in certain concentrations. An emulsion is defined as a

system of two immiscible liquids, one being dispersed in the other in the form of small droplets (63). The

droplet distribution causing the turbidity depends on several conditions such as temperature, degree of

agitation and the length of time the precipitates are allowed to stand (64)

The proportion of biodiesel to methanol and the temperature were optimized to be able to detect

bound glycerol in and near the ASTM standard for conversion by a function of cloudiness, as shown in Table

5. Carrying out the turbidity experiment at 10 oC and at a methanol to FAME ratio of 9:1, it is very promising

to be able to tell the difference between fuel that passes ASTM standard and fuel that does not. It was of the

essence to be able to convert these values into a quantitative measurement. To do so, use of a light sensor

was considered. The light sensor will function by measuring the degree of absorbance of light as a function of

cloudiness though a disposable spectrophotometric cell.

30

Table 5: Pioneering Experiment for Optical Density Tester of Used Cooking Oil

Conditions: 10oC, 9:1 methanol to B100, shaken vigorously

Bound Glycerol, % wt.

10 min 20min

0.745 Highly cloudy, opaque. No dropout Highly cloudy, opaque. No dropout

0.48 Cloudy, Can see through. No dropout Cloudy, Can see through. No dropout

0.359 Slightly cloudy. No dropout Slightly cloudy. No dropout

0.265 Slightly cloudy. No dropout Faintly cloudy

0.227 Clear Clear

0.165 Clear Clear

Other spectrophotometric techniques were also established for the determination of biodiesel

content in blends (65). The authors concluded that UV spectroscopy was the most reliable wavelength band

for inspection of biodiesel blend, which provided simple, fast and reliable results (65, 66).

4.1.1. General Principles of Spectrophotometry

Many molecules absorb specific wavelengths of radiant energy when monochromatic light passes

through a solution containing such molecules (solutes). In the case of detecting turbidity caused by the

emulsification of bound glycerol in this specific application, the spectrophotometer will actually be calculating

optical density, which is due emulsions of oil and methanol scattering the photons in the light beam rather

than absorbing them. The degree of absorption or scattering is directly proportional to the logarithm of the

concentration of solute as well as the length of light path as described by the Beer-Lambert Law (69),

equation 12. Even though there are spectrophotometers commercially available, it was of the essence to

develop an inexpensive modified version geared toward biodiesel analysis at decreased temperatures.

As can be seen from Figure 10, the incident light from the monochromatic laser diode is initially

diffused through HDPE #2 plastic. The diffuser will provide a wide uniform light beam in order to remove

the potential interference of refractive index of various samples. The light is transmitted through a standard

disposable polystyrene 1 cm spectrophotometric vial containing the sample. Any light that is not absorbed is

31

measured in a light-to-frequency converter and then translated using an inexpensive microcontroller onto a

serial display.

Figure 10: Biodiesel Spectrophotometer Apparatus

The measurement of absorption is made by comparing the intensities of incident ( ) and transmitted

( ) light passing through pure methanol and test solutions, respectively. The term transmittance ( ), is the

ratio of the radiant power transmitted by a sample to the radiant power incident on the sample as shown in

equation 10.

(10)

The Logarithm of the reciprocal of the transmittance is termed absorbance ( ) as shown in

equation 11.

(11)

Fundamentally, there are two laws of colorimetry, Lambert’s Law and Beer’s Law. Lambert’s Law

states that the amount of light absorbed is directly proportional to the logarithm of the length of the light

path. Beer’s Law states that the amount of light absorbed is directly proportional to the logarithm of the

concentration of solute (67). Thus, the combination of the two laws gives

32

(12)

where, is the extinction coefficient of the solute, is the concentration of the solute and is the length of

the light path. There exists a linear relationship between absorbance and concentration of solute when the

path length through the cell is constant. A plot of absorbance ( ) vs. concentration of solute ( will yield a

straight line passing through the origin indicating conformity to the Beer-Lambert Law.

4.1.2. TSL230R Light to Frequency Converter

A block diagram of the light to frequency converter is shown in Figure 12. The light sensor will

convert irradiance into frequency. The square wave produced by the sensor has a 50% duty cycle, allowing for

inexpensive equipment, such as an Arduino© microcontroller, to register the high pulses. The output can be

scaled using S2 and S3 pins shown in Figure 11. The full range of frequency output is from 1 Hz to 1 MHz

depending on irradiance.

Figure 11: TSL230R Block Diagram (68)

Figure 12 depicts the spectral response of sensor. A simple and inexpensive red laser diode was

chosen due to the high response of the light sensor at frequencies near red laser wavelength (650-670nm).

Furthermore, the 600 nm wavelength is typically used for applications in biological samples such as measuring

the optical density to determine cell concentration (69).

33

Figure 12: TSL230R Spectral Responsitivty at Various Wavelengths (68)

Figure 13 depicts the sensitivity control of the light sensor. The sensitivity control will determine

how many receptors are active on the chip at once. The sensitivity can be controlled by S0 and S1 pins as

shown in Figure 11. When set at a high sensitivity, it will be able to detect smaller amounts of light but will

lose the ability to register large amounts of light, and vice-versa for low sensitivity. The full range of irradiance

that can be measured is from 0.001 μW/cm2 to 0.1 W/cm2.

Figure 13: TSL230R Output Frequency (kHz) as a Function of Irradiance (μW/cm2) (68)

The microcontroller was chosen for its capabilities of programming the light sensor as well as

controlling the laser diode. The code used for the spectrophotometer system, for use in open source Arduino

coding system, allowed for optimal light measurements to be obtained using a red laser diode.

34

4.2. Dielectric Spectroscopy of Biodiesel in MW Regime

A dielectric is a material that has the ability to store energy when an electric field is applied, also

known as permittivity (2). Dielectric relaxation spectroscopy (DRS) probes the molecular dipole moment of

materials over a wide frequency range (1 mHz – 30 GHz) (70). Information on chemical structure, molecular

chain length and distribution and detection of contaminates in real time can be obtained (71-73). The relative

permittivity is expressed as a complex function in Equation 8,

(13)

where, ε’ is the real part of the permittivity, which is a measure of how much energy from the

external electric field is stored in the material per cycle. ε’’ is the imaginary part of the permittivity, called the

loss factor, and is a measure of how dissipative a material is when in the presence of an external electric field

per cycle. A useful quantification tool that can also be used is known as the loss tangent, which is the phase

angle between the excitation voltage and the current through the cell, is given by the quotient between the

imaginary and real parts of shown in equation 14 (74),

(14)

There are four different types of dielectric mechanisms over a broad range of frequencies. In respect

to measuring the dielectric loss factor of biodiesel, the dipolar mechanism was studied in the frequency range

of 200 MHz-20 GHz. During the applied electric field, a polar molecule will exhibit the dielectric material to

align and misalign to the electric field causing the permittivity to sharply decrease and the loss factor to peak

at certain frequencies, as shown in Figure 14.

35

Figure 14: Dielectric Response Mechanisms (75)

During isothermal conditions, the dielectric relaxation is identified by a peak of the loss permittivity,

e’’, over a range of frequencies, Figure 15. The peak will correspond to a step decrease in the storage

permittivity, e’.

Figure 15: Dielectric Storage Permittivity and Loss Permittivity (78)

Initial studies on biodiesel and materials that impact biodiesel quality were investigated at PSU along

the whole frequency span of 200 MHz – 20 GHz, shown in Figures 16-18. A broadband vector network

analyzer was utilized for the frequency generation and a coaxial probe is placed into a small beaker containing

the FAME. Initially the coaxial probe is blanked with a short and with air. It has been found in previous

studies that dielectric measurements can be used to detect levels of biodiesel blends (79-80), similar to on-

vehicle analysis of detecting alcohol levels in gasoline which also utilize dielectric spectroscopy (81).

36

Canola B100 of high quality (see section 6.1.2 for purification method) was analyzed over the

frequency spectrum as shown in Figure 16. It was found that B100 exhibits a large dielectric loss, ε’’, at 7.47

GHz. The peak at approximately 7.47 GHz is where the B100 experiences its peak dielectric loss (and

corresponding maximum negative slope for its dielectric constant) from the oscillating external electric field

due to the permanent dipole moment of the FAME. All liquid samples were placed in a vacuum vessel

immediately before analysis to remove air bubbles.

Figure 16: Dielectric Loss of Canola B100 ( ) vs. Frequency (Hz)

Pure glycerol was sparged with nitrogen and subsequently placed in a vacuum chamber to remove air

bubbles and was measured over the frequency range. The loss tangent was found to contain the most

pertinent data, with tangent maxima at both 630 MHz and 16 GHz, as shown in Figure 17. The strong

polarity of glycerol will make this impurity a good candidate for detecting in small concentrations using

dielectric spectroscopy.

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.0E+00 5.0E+09 1.0E+10 1.5E+10 2.0E+10

Die

lect

ric

Loss

(e

'')

Frequency (Hz)

37

Figure 17: Loss Tangent of Glycerol ( ) vs. Frequency (Hz)

Pure methanol was sparged with nitrogen to remove air and measured over the full frequency range.

As is well known, methanol exhibits dielectric loss maxima near 3.2 GHz as shown in Figure 18.

Figure 18: Dielectric Loss of Methanol ( ) vs. Frequency (Hz)

With knowledge of which frequencies contain pertinent dielectric data, repeated runs were carried

out on biodiesel samples produced with various quality as shown in section 6.1.2.

4.3. Measurement of the Speed of Ultrasound as a Biodiesel Characterization Technique

4.3.1. Ultrasound Fuel Quality Measurement Background

It was hypothesized that the speed of sound measurements in various biodiesel samples with

different conversions could be used to determine mass concentrations of unconverted fuel, in this case being

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

0 5E+09 1E+10 1.5E+10 2E+10

Loss

Tan

gen

t, e

"/e

'

Frequency, Hz

0

2

4

6

8

10

12

14

16

0 5E+09 1E+10 1.5E+10 2E+10

Die

lect

ric

Loss

, e"

Frequency, Hz

38

able to mitigate determining bound glycerol content by GC as in section 2.1.3.1 (82-83). While the speed of

sound in liquids is easier to measure than predict in liquids, the fundamental equations 9 and 10, show that

the speed of sound will change with respect to the density and the adiabatic bulk modulus of the liquid as

shown in the following two equations (84-85):

[15]

[16]

Furthermore, mixture laws for the speed of sound show that the speed of sound will change

proportionally to the ratio of the components in the liquid mixture (82), shown by equation 11,

2

1

12

1

n

i

ii

ii

in

i vv

[17]

where, v is the velocity, is the density and is the volume fraction while n is the number of

components. Using these theoretical foundations, it was decided to develop an ultrasonic speed of sound

system to measure the conversion of biodiesel samples.

The speed of sound as a function of temperature was measured in various biodiesel products and raw

vegetable oil, Figure 19. A frequency sweep of 1.2 – 1.5 MHz to the transmitting transducer was applied while

the amplitude response at the receiving element was monitored.

39

Figure 19: Frequency Sweep of Vegetable Oil from 1.2-1.5 MHz

In order to determine the speed of sound, the resonant frequencies were determined by using the

following equations, 12 and 13:

[18]

where r is a resonant frequency, n is a whole number which represents the number of wavelengths

generated in the cavity, c is the speed of sound and L is the length of the cavity. Two separate resonant

frequencies are solved simultaneously to determine the speed of sound as shown in the following equation:

[19]

The apparatus of the ultrasonic speed of sound measurement system, Figure 20, contains the

following components (82):

Labview System

o NI PXI-8196 embedded controller

Function Generator

o NI PXI-5422 16-bit 200 Msamples/second Arbitrary Wave

Oscilloscope

40

o NI PXI-5125 12 bit 200 Msamples/second Digitizer

Transducers

o 130309G, 3.5 MHz, Lead metaniobate

Thermocouple Input

o NI9211 80 mV 24-bit

Figure 20: Ultrasonic Velocity Measurement Apparatus (80)

4.4. Unique In-Column Injection for Total and Free Glycerol Determination by GC

As described in section 2.1.3.1, the conventional method for injecting samples into the GC for free

and total glycerol detection is into a cool on-column injector. Yet, the GC apparatus that our group had

available was only capable of doing split/splitless injections. Thus, the former group in the lab developed a

modified technique where a long needle syringe (9-10inches long) was used to inject the sample into the

column manually at low temperatures.

The method and column type were the same as in ASTM. The column was held at the starting

temperature of 50 oC and the injection into the cool column initiated the run. The first requirement for

method development is standardization. Pre-diluted standards of the ASTM D 6584 were purchased from

Send Transducer

Receive Transducer

Pipe with fluid

Function Generator

Oscilloscope

Thermocouple Input

41

Restek and were utilized to prove this method was sufficient in analyzing these components. Galaxy software

linked to the GC was utilized to establish linear calibration curves for glycerol, monoolein, diolein and

triolein. The curves for each compound showed excellent linearity and y-intercepts near zero. The curves,

shown in Figures 21-24, exceeded the ASTM specification for correlation coefficients of 0.99. The column

type utilized in this study was a Restek MXT-Biodiessel TG w/ Int-Gap, 14 m length, 0.53 mmID, and 0.16

μm df.

Figure 21: Free Glycerol Concentration vs. FID Response

Figure 22: Monoolein Concentration vs. FID Response

y = 1.049956x + 0.012742

R² = 0.9938

0

0.1

0.2

0.3

0.4

0.5

0.6

0 0.1 0.2 0.3 0.4 0.5

Am

t (W

i/W

s)

Rsp (Ai/As)

y = 0.813544x - 0.005878R² = 0.998

00.20.40.60.8

11.21.4

0 0.5 1 1.5 2

Am

t (W

i/W

s)

Rsp (Ai/As)

42

Figure 23: Diolein Concentration vs. FID Response

Figure 24: Triolein Concentration vs. FID Response

Figure 25 shows the typical chromatogram of the modified GC method for Sample A in the test

batch obtained in section 5.1.1. The chromatograph shows optimal resolution of the various components

found in the biodiesel sample for quality analysis. One obvious note is the much higher peak width than the

conventional GC method, which may be due to the operator ineffectively injecting the 1 μL sample onto the

column instead as compared to the optimized cool on-column injection. Furthermore, the order of elution is

the same as the conventional ASTM GC method. The series of samples in sections 5.1.1 and 5.1.2 were

analyzed for free and total glycerin by the modified method. The results of the modified method are outlined

in section 5.4.8.

y = 0.745817x + 0.029705

R² = 0.9992

0

0.2

0.4

0.6

0.8

0 0.2 0.4 0.6 0.8 1A

mt(

Wi/

Ws)

Rsp(Ai/As)

y = 1.279227x + 0.020876

R² = 0.9965

0

0.2

0.4

0.6

0.8

0 0.1 0.2 0.3 0.4 0.5

Am

t(W

i/W

s)

Rsp(Ai/As)

43

Figure 25: Typical Chromatogram of the Modified GC method for Sample A (sec. 5.1.1)

44

Chapter 5. Results and Discussion of Analytical Fuel Quality Techniques

5.1. Test Samples

5.1.1. Commercial Biodiesel Samples

A total of 25 commercial B100 samples were obtained from R. H. Heiden Associates, LLC. The

samples were received in three batches, 7-28, 10-01 and on 11-03-09. The samples represent a wide range of

quality and are typical of the range of products produced by small and large producers of B100. The samples

were prepared from a variety of feedstocks. They represent methyl esters synthesized by generic

transesterification and isolated from regular commercial manufacturing lots due to questionable quality. The

variations in composition are consistent with those seen in past occasional, off-spec, regular submissions to

R.W. Heiden Associates, LLC., and are from numerous locations in the U.S. The R.W. Heiden letter

describing the samples is found in Appendix I.B.

5.1.2. Small Scale Batch Biodiesel Samples from Various Feedstocks

The feedstocks canola, olive and peanut oil were brought to various conversions of FAME by

transesterification using equipment available in the Penn State biodiesel laboratory. The transesterification

reaction was carried out in a one liter batch reactor. The biodiesel product was purified by an initial water

wash then subsequently by Eco2Pure for soap, glycerol, water and methanol removal and finally Purolite ion

exchange resin was used for polishing. Furthermore, a high quality sample of distilled Soybean B100 was

obtained for comparison. Figure 26 below depicts the extent of conversion as analyzed using the modified

GC method described in section 4.4 at Penn State’s analytical lab.

45

Figure 26: Bound Glycerol Conversion Curve for Various Feedstocks

5.2. Past Studies of Correlating Two Instruments

5.2.1. 2004 NREL Survey – Two Rancimat Instruments

The 2004 NREL survey correlated two Rancimat instruments (SwRI and Bosch) which utilized

pertinent comparison methods (23) to this thesis. Figure 27 correlates the average SwRI results

(3 replications for most samples) on the x-axis and the Bosch results (single test) on the y-axis.

Figure 27: Rancimat Value (Bosch) vs. Rancimat Value (SwRI) (23)

0.7552

0.2832

0.1740

0.2229

0.3975

0.2657

0.3035

0.4490

0.3100

0.2977

0.11670.1000

0.2000

0.3000

0.4000

0.5000

0.6000

0.7000

0.8000

0 20 40 60 80 100 120 140 160

Bo

un

d G

lyce

rol (

wt.

%)

Reaction Time (min)

Canola

Peanut

Olive

Peter Cramer

46

The table above was produced and analyzed by NREL in the following fashion. The correlation was

determined by plotting the values against each other and determining the R2 value of the linear trend, being

0.84. The R2 value is termed the correlation coefficient. They also notice that there is bias toward longer

induction times for the samples that tested at Bosch. They also plot the theoretical linear line which is called

the penalty line. These terms and methods will be used throughout the analysis section of this thesis for

correlating two instruments.

5.3. Methods for Comparing Two Instruments that Measure the Same Parameter

Since our group was not able to carry out multiple runs of the same samples on each alternative

testing apparatus, it was of essence to come up with an equivalent way to measure the effectiveness for each

test. Under ideal conditions for instrument qualification, the sample is run multiple times to produce

reproducibility and repeatability formulas for analytical methods. For qualitative measurements, the pass/fail

result in this study can only either agree or disagree with the ASTM method baseline.

For quantitative instrument comparisons scatter plots, correlation coefficients, reproducibility

measurements, bias, and average error will be utilized to determine if the measured values of the alternative

tests suffice to replace ASTM tests.

The scatter plots will have the ASTM test result on the X-axis and the alternative test result on the Y-

axis. The plot is further broken up into four different quadrants in respect to where the ASTM limit for the

substance lies. If a result ends up in the upper left quadrant it is a false fail, lower left it is in agreement, upper

right it is in agreement and in the lower right it is a false pass. A false fail means that the alternative testing

technique deemed the sample failed for the limit of the substance, but the ASTM test indicated that it passed.

A false pass means the alternative testing technique deemed the sample was below the limit, but ASTM test

indicated that it failed. The linearity of the scatter plot will show how well one test or instrument agrees to the

ASTM test shown with a solid black line, known as the correlation coefficient (See Sec. 2.2.2.1). A dotted line

47

is present as a reference to show where the true linear fit would lie, which is known as the penalty line (See

Sec. 2.2.2.1).

ASTM testing methods have repeatability (r) and reproducibility (R) equations associated with each

result to determine if the instrument and/or operator are providing sufficiently reliable results. Repeatability

of a method is defined according to ASTM as “the difference between successive test results obtained by the

same operator with the same apparatus under constant operating conditions and on identical test materials,

which would in the long run in the normal and constant operation of the test method be exceeded only in

one case in twenty” (6). While the repeatability measurement is not applicable to this study for reasons stated

previously (multiple runs with same lab, same operator and same specimen), it will be important for future

studies to carry out tests successively on the same instrument using the same testing material to ensure the

repeatability measurements lay within the ASTM requirements . The definition of reproducibility is similar to

that of repeatability but it takes into account the measurements are two single and independent results on the

same material in different labs by different operators (6). If the two instruments are correctly operated, the

two results of the same material should not exceed the reproducibility value except in one case in twenty.

Thus, 95% of the sample results should fall into the reproducibility limits for the alternative technique to

fulfill the reproducibility requirements. When determining the reproducibility of a method, ASTM requires

the involvement of at least six laboratories, each making a minimum of three measurements on the same test

material (84). Reproducibility equations found in the corresponding ASTM or EN standards are utilized in

this study, shown in Table 6. If an alternative testing method can fulfill these criteria then the ASTM

taskforce would be much more likely to adopt the alternative testing method for biodiesel analysis.

Furthermore, it is important to point out that samples that have ASTM results outside of the limit of

detection (LoD) will be fully omitted for the reproducibility analysis. While a new analytical technique may

have the capability of increasing the LoD, omitting these values for this study is necessary since ASTM

reproducibility requirements are not applicable to results outside of the LoD. The reproducibility at the

ASTM limit, the LoD, and the reproducibility at the LoD for various biodiesel quality parameters are

compiled in Table 6.

48

Table 6: Reproducibility Calculations of Critical ASTM Methods

Biodiesel Fuel Property

Units R Equation Ref

#

R at the ASTM Spec. Limit

Limit of Detection Range

R at low end of LoD

R at high end of LoD

Total Glyerol % mass 0.4928*(X+2.51E-02) [20] 0.131 0.05 – 0.5 .037 0.259

Free Glycerol % mass 0.1082* (X+1E-04) 0.4888

[21] .016 0.005 – 0.05 .008 .025

Methanol % mass 0.221*X + 0.003 [22] 0.047 .01 – 0.5 .005 0.114

Acid Number mg KOH / g Oil

0.141*(X+1) [23] .212 0.1- 150 .155 21.3

Cloud oC --- 3 < 49 --- ---

Sulfur mg/kg 0.5797*X0.75 (less

than 400 ppm) [24] 3.65 1-8000 .4797 1013

Cold Soak Filtration

seconds --- --- <720 (12min)

116 (200sec)

208 (360sec)

Further comparisons of the test include the bias of the instrument to either give a result that is above

or below the ASTM results. This is calculated simply by taking the average difference of the alternative

techniques result to the ASTM result. Lastly, the average error of the instrument will be included for further

comparison, which will tell the overall accuracy of hitting the ASTM value. This is calculated by taking the

absolute value of the difference of the alternative techniques result to the ASTM result and averaging them.

5.4. Alternative Testing Technique Analysis

5.4.1. pHLip Test This test was used on all 25 samples as described in section 4.1. Basically the upper phase of the

pHLip vial, Table 7, detects free and bound glycerol contamination. The lower phase of the pHLip vial, Table

8, detects the pH of the fuel. pHLip results that are highlighted in green denotes a qualitative result that

agreed with the ASTM test result while a sample highlighted in red denotes a qualitative result that disagrees

with the ASTM test result. ASTM total glycerol results highlighted in red indicate the sample was above the

limits required in ASTM D 6751.

49

5.4.1.1. Upper Phase (Glycerin Detection)

As shown in Table 7, the pHLip test was capable of detecting total glycerin very well, with 22 out of

25 correct reports. Two of the fail reports, samples G and O, were borderline samples, in that they were close

to the ASTM limit, and the third fail report was a false fail (Sample K). With 88% of the results agreeing with

the ASTM result, it shows that the pHLip quick test can be a reliable field test for firewalling off-spec samples

for total glycerol at either a blending station or at a fuel pump.

Table 7: pHLip Glycerin Analysis Total Glycerin Comparison (Upper phase of pHLip)

Sample I.D.

pHLip ASTM D 6942, Total glycerin, mass %

A Pass Pass, 0.147

B Pass Pass, 0.149

C Pass Pass, 0.12

D Pass Pass, 0.117

E Pass Pass, 0.213

F Fail Fail, 0.257

G Fail Pass, 0.23

H Pass Pass, 0.091

I Pass Pass, 0.194

J Pass Pass, 0.064

K Fail Pass, 0.115

L Fail Fail, 0.425

M Fail Fail, 0.197

N Fail Fail, 0.311

O Fail Pass, 0.223

P Fail Fail, 0.396

Q Fail Fail, 1.256

R Fail Fail, 0.871

S Fail Fail, 0.322

T Fail Fail, 1.385

U Fail Fail, 0.285

V Fail Fail, 0.27

W Fail Fail, 0.3

X Fail Fail, 0.743

Y Fail Fail, 0.785

5.4.1.2. Lower Phase (Acid Value Detection)

The lower phase of the pHLip vial shifts from cherry red to yellow in a linear fashion with respect to

increasing acid value. Thus, it is very hard to tell whether a sample is pass or fail unless it is clearly cherry red

50

or clearly yellow. Many of the samples that produced an orange lower phase or slightly orange phase are

difficult to diagnose. Furthermore, there were many samples that were approaching the 0.5 mg KOH/g fuel

acid number limit but were not shown as nearing the limit with the pHLip vial. It was deemed that a if the

lower phase reaches yellow, it will be a failing sample. Overall, there were 5 samples that were not able to

adequately detect acid values above the ASTM limit. With 80% of the vials correctly diagnosing the acidity

of the B100 samples, this test is deemed to be a good firewall against samples that are either high in acid

value or have been oxidized.

Table 8: pHLip Acid Number Analysis

Acid Number Comparison

(Lower phase of pHLip)

Sample ID pHLip ASTM D 664,

Acid Number

92-A Red .35

92-B Red .38

92-C Red-orange .29

92-D Red .29

92-E Orange .37

92-F Orange .36

92-G Red .95

92-H Red .24

92-Ii Red .51

92-Jj Red .44

92-Kk Red .39

92-Ll D.Red .33

92-M D.Red .4

92-N Red .28

92-O Red .51

92-P Red .4

92-Q Red-orange .52

92-R Red-orange .51

92-S Yellow .62

92-T Yellow 18.1

92-U Red .2

92-V Red .33

92-W Hazy .35

92-X Red .48

92-Y Red .42

51

5.4.2. Near Infrared – QTA System

The QTA instrument as described in section 4.2 was used to obtain the following quantitative results,

figures 24-27 and tables 9-12, on the 25 samples described in 5.1.1. The tests carried out were total glycerin,

methanol, acid number and free glycerol.

5.4.2.1. Total Glycerin

The QTA test was highly reliable for correctly determining the correct total glycerin value of the

biodiesel fuel, as shown in Figure 28. There were three false passes and one borderline false fail from 25

samples. The false passes and borderline seem to be outliers as the overall trend is very accurate. A general

trend is that the results near the ASTM limit follow the “penalty line” (dotted line) very well, but as the total

glycerin value goes above 0.4, the instrument is biased very negative.

Figure 28: QTA vs. ASTM D 6942 for Total Glycerin Measurement

The statistical data depicted in Table 9 below shows that the QTA system had a negative bias of -

0.075 mass% low, and an average error of 0.054 mass %. The correlation value was relatively high, with a

value of 0.846. Out of the 21 samples in the LoD, 76.19% of them fell into the reproducibility requirements

of the ASTM test. Since the LoD is below 0.5 %, this reproducibility error cannot be attributed to the

R² = 0.8459

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 1.1 1.2 1.3 1.4

QTA

, To

tal G

lyce

rin

wt%

ASTM D 6942, Total Glycerin wt%

False Fail

False Pass

52

increased bias for samples with high values of total glycerol. Thus, the 4 outlier samples were the main cause

of this test not reaching the 95% reproducibility goal.

Table 9: Statistical Evaluation of QTA Total Glycerin Correlation

Parameter QTA Total Glycerin Result

Total Samples 25

Bias -0.075 % total glycerin

Average Error 0.054% total glycerin

False Pass 3

False Fail 0

Correlation Coefficient 0.8459

Total Values in LoD 21

% Reproducibility Pass in LoD 76.19 %

5.4.2.2. Methanol Content

The QTA system was highly reliable in determining methanol content in the biodiesel fuel, Figure 29,

with only 3 false fails and zero false passes from 25 samples. The correlation coefficient for methanol content

was 0.8957, being a very adequate correlation. As shown in Figure 25, there was only 1 significant outlier,

sample T, which was the sample with the highest concentration of bound glycerol, being 1.385 % mass (5.7

fold the limit). Thus, this is indicative that bound glycerol (free glycerol value was low) will adversely affect

the methanol analysis for the QTA. The overall bias was negative especially in the 0.1 – 0.2 mass% range and

above 1%.

53

Figure 29: QTA vs. EN 14110 for Methanol Content Measurement

As shown above, the major outlier, Sample T, could easily be singled out as an inadequate sample.

The following statistical analysis, Table 10, was carried out for all samples except sample T (note correlation

coefficient changes from .8957 to 0.9224). The QTA system had a bias error of -0.039 % mass and an average

error of 0.063%. Furthermore, it should be noted that the reproducibility equation shown in Table 6 has a

required reproducibility of 0.005 at the lower LoD. The values received by our lab from the QTA system

were given with accuracy in the hundredths place, causing most of the lower limit samples determined by the

ASTM test to fail the reproducibility requirement for the QTA result. This reproducibility criteria will need to

be addressed by Cognis. Even with most of the low value samples failing the reproducibility, 47% of 18

values fell within the reproducibility requirement.

R² = 0.8957

0

0.2

0.4

0.6

0.8

1

1.2

1.4

0 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6

QTA

Me

than

ol C

on

ten

t, m

ass

%

EN 14110, Methanol Content, mass %

False Fail

False Pass

54

Table 10: Statistical Evaluation of QTA Methanol Content Correlation

Parameter QTA Methanol Result

Total Samples 24

Bias -0.039 % MeOH

Average Error 0.063% MeOH

False Pass 0

False Fail 2

Correlation Coefficient 0.9224

Total Values in LoD 18

% Reproducibility Pass in LoD 47.06 %

5.4.2.3. Acid Number

The QTA system was not able to detect acid number very well, as shown in Figure 30. The

correlation coefficient was highly low, being 0.422 which is indicative of the large deviation throughout the

range of acid in the study. While there were only 2 false passes (3 borderline), the overall picture is that the

FT-IR method for determining acid value is inadequate.

Figure 30: QTA vs. ASTM D 664 for Acid Number Measurement

R² = 0.422

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1

QTA

Aci

d N

um

be

r, m

g K

OH

/g

ASTM D 664, Acid Number, mg KOH/g

False Fail

False Pass

55

The statistical analysis of the QTA acid number data is shown below in Table 11. While the overall

bias of the system is negative, it can be shown that there is no overall correlation, especially for acid values

approaching the ASTM limit of 0.5 mg KOH /g. The average error is large, at 0.123, being 25% of the

ASTM limit. Out of 24 samples inside of the LoD, 87.5% of the QTA values fell within the reproducibility

limits. While this test had a relatively large average error, the reproducibility of the potentiometric test is

lenient giving this test a good chance of being able to suffice for ASTM testing.

Table 11: Statistical Evaluation of QTA Acid Number Correlation

Parameter QTA Acid Number Result

Total Samples 24

Bias -0.083 mg KOH/g

Average Error 0.123 mg KOH/g

False Pass 2

False Fail 0

Correlation Coefficient 0.422

Total Values in LoD 24

% Reproducibility Pass in LoD 87.5 %

5.4.2.4. Free Glycerol

The QTA system was much more sporadic at determining the stringent standard of free glycerol

content. Values above and below the ASTM limit drifted from the correlation line as shown below in Figure

31. There were many large outliers, especially when the ASTM value increased above 0.013% free glycerol,

which the QTA system was incapable of detecting large amounts of glycerol. the ASTM test to have no

glycerol but significant glycerol was determined for QTA. This could either mean the QTA has a better

LoD, or is getting false readings from other parameters. As shown in Figure 27 there were many outliers, but

2 were more significant than the rest, samples M and L, M being a sample with the highest concentration of

methanol, being 1.59 % mass. Sample L was high in bound (0.34) and free glycerol (.084), but low in

56

methanol. Thus, this is indicative that methanol may adversely affect the free glycerol analysis for the QTA.

There are also many values that were determined by the ASTM test to have no glycerol but significant

glycerol was determined for QTA. This could either mean the QTA has a better LoD, or is getting false

readings from other parameters.

Figure 31: QTA vs. ASTM D 664 for Free Glycerol Measurement

The statistical analysis of the QTA free glycerol data is shown below in Table 12. The overall bias

was significantly negative at -0.007, but without outlier M, it would have been -0.01. A bias on -0.01 is 50% of

the ASTM limit of 0.02%, thus the analysis of free glycerol utilizing QTA needs to be reassessed so that the

values will overall be higher. The average error of 0.013% is also significant, being 65% of the ASTM limit.

For this test to be successful, it will need to narrow the average error, which was mainly seen at very low and

very high free glycerol values. Furthermore, it is important to note that 10 of the 18 samples were outside of

the LoD (most being lower than 0.005 % mass). Out of the 8 samples inside of the LoD, 75% of the QTA

results lied within the reproducibility criteria, which is nearing the goal of 95%.

R² = 0.6313

0

0.005

0.01

0.015

0.02

0.025

0.03

0.035

0.04

0.045

0.05

0 0.01 0.02 0.03 0.04 0.05 0.06 0.07 0.08 0.09

QTA

, Fre

e G

lyce

rol w

t%

ASTM D6942, Free Glycerol wt. %

False Fail

False Pass

57

Table 12: Statistical Evaluation of QTA Free Glycerol Correlation

Parameter QTA Free Glycerol Result

Total Samples 18

Bias -0.007 % free glycerol

Average Error 0.013% free glycerol

False Pass 1

False Fail 1

Correlation Coefficient 0. 6313

Total Values in LoD 8

% Reproducibility Pass in LoD 75 %

5.4.3. I-Spec Q100

The I-Spec instrument as described in section 3.4 and was used to analyze the 25 samples described

in section 5.1.1 for total glycerin, methanol and acid number, Figures 28-30 and Tables 13-15.

5.4.3.1. Total Glycerin

The I-Spec seemed to have a lot of difficulty correctly determining total glycerin, shown in Figure 28.

All of the results were below 0.24 % mass, except for one result being Sample T, with 9 false passes out of 25

runs, with only one correct determination of a fail sample (Sample T). This indicates that the I-Spec unit is

largely unable to detect levels total glycerol. The correlation coefficient of 0.396 is significantly low, also

indicating this test method needs to be reassessed.

58

Figure 32: I-Spec vs. ASTM D 6942 for Total Glycerin Measurement

The statistical analysis of the I-Spec total glycerin data is shown below in Table 13. The average bias

was -0.195% below the ASTM value, yet with outlier sample M, the average bias is -0.26% below the ASTM

value, being 108% of the ASTM limit. Furthermore, it is seen from the average error that this test is

inadequate for measuring total glycerin. 55.55% of the samples in the LoD pass the reproducibility

requirement.

Table 13: Statistical Evaluation of I-Spec Total Glycerin Correlation

Parameter I-SPEC Total Glycerin Result

Total Samples 22

Bias -0.195% total glycerin

Average Error 0.251% total glycerin

False Pass 9

False Fail 0

Correlation Coefficient 0.3956

Total Values in LoD 18

% Reproducibility Pass in LoD 55.55 %

R² = 0.3956

0

0.2

0.4

0.6

0.8

1

1.2

1.4

0 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6

I-Sp

ec,

To

tal G

lyce

rol w

t.%

ASTM D6942, Total Glycerol wt%

False Fail

False Pass

59

5.4.3.2. Methanol Content

As seen in Figure 29, the I-Spec also had little ability for determining the correct methanol content.

The test had 12 false fails out of 25 samples, with only one correct result for a sample that failed. A

correlation coefficient of 0.111 is highly inadequate.

Figure 33: I-Spec vs. EN 14110 for Methanol Content Measurement

The statistical analysis of the I-Spec methanol data is shown below in Table 14. The test was biased

positive above the ASTM by 0.053 mass%, being especially positively biased at low methanol contents.

Furthermore, the average error of the test was 0.205%, deeming this test inadequate for detecting correct

methanol concentrations since the ASTM limit is 0.2% mass. Out of 15 samples in the LoD, only 6.67% of

the I-Spec results were inside of the reproducibility requirements.

R² = 0.1109

0

0.05

0.1

0.15

0.2

0.25

0.3

0.35

0.4

0 0.05 0.1 0.15 0.2 0.25 0.3 0.35 0.4 0.45 0.5

I-Sp

ec

Me

than

ol C

on

ten

t, m

ass

%

ASTM Methanol Content, % mass

False Fail

False Pass

60

Table 14: Statistical Evaluation of I-Spec Methanol Content Correlation

Parameter I-SPEC Methanol Result

Total Samples 21

Bias +0.053 % MeOH

Average Error 0.205% MeOH

False Pass 12

False Fail 0

Correlation Coefficient 0.1109

Total Values in LoD 15

% Reproducibility Pass in LoD 6.67 %

5.4.3.3. Acid Number

As seen above in Figure 30, the I-Spec also had a very hard time correctly determining the correct

acid value, especially at high values above the ASTM limit. The test had 6 false passes and 3 false fails out of

25 samples. As depicted in Figure 30, this test did not correlate at all, with a correlation coefficient of 0.1048.

Figure 34: I-Spec vs. ASTM D 974 for Acid Number Measurement

R² = 0.1048

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1

I-Sp

ec

Aci

d N

um

be

r, m

g K

OH

/g

ASTM D664, Acid Number, mg KOH/g

False Fail

False Pass

61

The statistical analysis of the I-Spec acid number data is shown below in Table 15. Since this test was

unable to detect acid values above the ASTM limit as well as giving a large range of values below the ASTM

limit, the bias and average error were very high, at -0.105 and 0.228, respectively. Out of 23 samples in the

LoD, 43.48 of the I-Spec values fell within the reproducibility requirement.

Table 15: Statistical Evaluation of I-Spec Acid Number Correlation

Parameter I-SPEC Acid Number Result

Total Samples 23

Bias -0.105 mg KOH/g

Average Error 0.228 mg KOH/g

False Pass 6

False Fail 3

Correlation Coefficient 0.1048

Total Values in LoD 23

% Reproducibility Pass in LoD 43.48 %

5.4.4. Methanol Solubility Test

The final solutions obtained from the WVO reaction, as described in Appendix IA, were analyzed

using the 27/3 test at room temperature (RT, 21 oC). An ice bath was optimized to 21 oC since the RT was 25

oC. The first test was carried out in a clear 25mL graduated cylinder. The methanol was brought to 21 oC and

the biodiesel samples depicted in Table 16 were added and shaken vigorously for 5 seconds. Results were

recorded at 10, 20 and 30 minutes. The results are shown in Table 16 below.

62

Table 16: 27/3 Methanol Solubility Test of WVO Samples

Total Glycerin Value (mass%)

10 min 20min 30min

0.745 Clear MeOH, small bead dropped out.

Clear, no more dropped out.

Clear, no more dropped out

0.48 Clear MeOH, no dropout Clear no dropout Clear no dropout

0.359 Clear MeOH, no dropout Clear no dropout Clear no dropout

0.265 Clear MeOH, no dropout Clear no dropout Clear no dropout

0.227 Clear MeOH, no dropout Clear no dropout Clear no dropout

0.184 Clear MeOH, no dropout Clear no dropout Clear no dropout

As shown above, the 27/3 test did not produce significant results for the bound glycerol curve for

WVO from canola oil, where only conversion of 0.745 was detected by the conventional method. In order to

attempt to obtain more qualitative and potentially quantitative data, 100mL centrifuge tubes were utilized and

the MeOH:B100 ratio was increased to 81:9. Placing 81mL MeOH in ASTM Centrifuge graduated test tube,

adding 9mL Biodiesel and shaking vigorously and letting sit at room temperature (21 oC).

As shown in Table 17, the 81:9 test still does not provide useful information at room temperature

until total glycerin values reach 0.359. It was decided to decrease the temperature of the methanol which was

shown to produce more qualitative and potentially quantitative results which is shown in section 6.4.5.

Table 17: 81/9 Methanol Solubility Test of WVO Samples

%mass Total Glycerin

After 10 minutes of settling

0.745 Clear, small beads dropped out of solution and adhered to glass walls near bottom of centrifugal tube.

0.48 Clear, very small dropout of beads.

0.359 Clear, no dropout

0.265 Clear, no dropout

0.227 Clear, no dropout

0.184 Clear, no dropout

0.745 Clear, no dropout

63

5.4.5. Spectrophotometric Analysis of Biodiesel for Bound Glycerol Determination

The quantitative values below were obtained by modifying the method to allow for multi-feedstock

testing by decreasing the methanol to B100 ratio as well as decreasing the temperature. It was found that

decreasing the temperature allowed for more consistent turbidity in samples with low total glycerin content.

This is potentially due to samples having various fatty acid profiles as they are multi-feedstock, whereas

previous experiments were run only on canola B100. The samples utilized for the experiment are defined in

section 6.1. Any samples that were found to have cloud points above 6 oC were omitted from this study since

the bath temperature was chosen to be 6 oC for multi-feedstock testing. Cloud data was supplied from QTA

as shown in Appendix II.

10:1 Ratio Analysis

The following analysis was carried out for the data set of a methanol to B100 ratio of 10:1 using the

spectrophotometer described in section 4.1. All experimental data was repeated 4 times to provide

experimental error analysis. The methodology described here was reproduced for methanol to B100 ratios of

9:1 and 8:1.

As shown in Figure 35, it is difficult to diagnose the results of the test due to the high absorbance of

total glycerin values above 0.5 wt. %. In order to evaluate the test at different conditions in the range of

interest (below 0.5 wt. % total glycerin), the samples above 0.5 wt.% were omitted for the analysis. Thus, the

LoD of the tests is less than 0.5 wt. %. The standard error of the mean of 4 repeated data sets was carried

out to produce the average values with error bars below in Figures 35-36, 38 and 40. This is done by taking

the standard deviation of the 4 repeated values and dividing it by the square root of 4 which provides a

measurement of the experimental error.

64

Figure 35: Absorbance vs. Total Glycerin at 10:1 Ratio

In order to carry out a quantitative analysis, a linear fit was carried out to convert the absorbance

values into total glycerin values. This was done by inserting a linear fit into Figure 36 to determine equation

25 for calculating total glycerin from absorbance values,

[25]

The y absorbance values in Figure 36 were plugged into the equation 25 to derive total glycerin

values. The determined total glycerin values were then plotted against the ASTM total glycerin values to

obtain Figure 37 below. Creating the linear fit in Figure 36 will artificially zero the data in Figure 37 since it is

the same data set. The methodology carried out has essentially zeroed the bias, as seen in table 18. The same

procedure was carried out for FAME to methanol concentrations of 9:1 and 8:1. As shown in Figure 36,

some values have poor reproducibility depicted by the large error bars. This can be due to the emulsions

decreasing over time as the repeated measurements on the same sample were conducted.

0

0.2

0.4

0.6

0.8

1

1.2

1.4

1.6

1.8

0 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6

Ab

sorb

ance

Commercial Biodiesel Testing Lab, D 9648, Total Glycerin wt. %

65

Figure 36: Absorbance vs. Total Glycerin at less than 0.5 wt. %, 10:1 ratio

As shown in Figure 37, there is a mediocre trend for increasing total glycerol. The results at a

methanol to B100 ratio of 10:1 allowed for only 1 false pass and 1 false fail. The correlation coefficient on the

other hand is very low, with a value of 0.532. The overall trend does depict that the degree of emulsification

of oil to methanol is significant enough to quantitatively determine total glycerol.

Figure 37: Spectrophotometer (10:1 Ratio) vs. ASTM D 6942 for Total Glycerin

y = 0.3413x - 0.0039R² = 0.5146

-0.02

0

0.02

0.04

0.06

0.08

0.1

0.12

0.14

0.16

0 0.1 0.2 0.3 0.4 0.5

Ab

sorb

ance

Commercial Biodiesel Testing Lab, D 9648, Total Glycerin wt. %

R² = 0.5316

0

0.1

0.2

0.3

0.4

0.5

0.6

0 0.1 0.2 0.3 0.4 0.5 0.6Spe

ctro

ph

oto

me

ter,

To

tal G

lyce

rin

, wt.

%

Commercial Biodiesel Testing Lab, D 9642, Total Glycerin wt. %

False Fail

False Pass

66

As described previously, carrying out a linear fit on the raw data caused the bias to be zero, which is

the best case scenario. The average error is still significant, with a value of 0.068% total glycerin. Out of the

16 samples in the LoD, 75% of the spectrophotometer results were inside of the reproducibility requirement.

Table 18: Statistical Evaluation of Spectrophotometer (10:1 Ratio) Total Glycerin Correlation

Parameter Light Sensor Total Glycerin Result

Total Samples 16

Bias 0 % total glycerin (set to zero)

Average Error 0.068% total glycerin

False Pass 1

False Fail 1

Correlation Coefficient 0.531

Total Values in LoD 16

% Reproducibility Pass in LoD 75 %

9:1 Ratio Analysis

The results for the 9:1 ratio of methanol to B100 test are shown in Figure 38. Overall, some of the

error bars are not as significant but the trend has slightly decreased in slope. The linear fit was carried out to

calculate total glycerin values as seen in Figure 39.

67

Figure 38: Absorbance (9:1 Ratio) vs. Total Glycerin

When comparing the 10:1 test to the 9:1 test, there seems to be many more difficulties in measuring

low concentrations of total glycerol due to emulsions. The trend has decreased overall and the correlation

coefficient has also been reduced to .3708. There are 5 false fails and 2 false passes that also make these

results look much worse than the 10:1 ratio.

Figure 39: Spectrophotometer (9:1 Ratio) vs. ASTM D 6942 for Total Glycerin

y = 0.1671x + 0.027R² = 0.3708

0

0.01

0.02

0.03

0.04

0.05

0.06

0.07

0.08

0.09

0.1

0 0.05 0.1 0.15 0.2 0.25 0.3 0.35 0.4

Ab

sorb

ance

Commercial Biodiesel Testing Lab, D 9648, Total Glycerin wt. %

R² = 0.3708

-0.2

-0.1

0

0.1

0.2

0.3

0.4

0.5

0.6

0 0.1 0.2 0.3 0.4 0.5 0.6

Spe

ctro

ph

oto

me

ter,

To

tal G

lyce

rin

, wt.

%

Commercial Biodiesel Testing Lab, D 9648, Total Glycerin wt. %

False Fail

False Pass

68

The statistical analysis of the spectrophotometer total glycerin data is shown below in Table 19. In

comparing the 10:1 to 9:1 test further, it is seen that the average error has increased from .068 to 0.078%.

While this is not highly significant, the trend shows that the overall ability for the test to detect total glycerin

should be carried out in dilute methanol. The test had the same reproducibility passes, being 75%.

Table 19: Statistical Evaluation of Spectrophotometer (9:1 Ratio) Total Glycerin Correlation

Parameter Light Sensor Total Glycerin Result

Total Samples 16

Bias 0 % total glycerin (set to zero)

Average Error 0.078% total glycerin

False Pass 2

False Fail 5

Correlation Coefficient 0.371

Total Values in LoD 16

% Reproducibility Pass in LoD 75 %

8:1 Ratio Analysis

In continually increasing the B100 concentration in methanol, the ability of this test to differentiate

small amounts of total glycerin changes gets worse, as shown in Figure 40. The error bars for 8:1 are smaller

than the 9:1 and 10:1 analysis overall.

69

Figure 40: Absorbance (8:1 Ratio) vs. ASTM D 6942 for Total Glycerin

The general trend continues in Figure 41, where the test has little ability to detect total glycerin above

ASTM values. With 2 false fails and 3 false passes, this test is deemed inadequate for determining total

glycerin. The correlation coefficient also drops significantly to 0.1725

Figure 41: Spectrophotometer (8:1 Ratio) vs. ASTM D 6942 for Total Glycerin

y = 0.1539x + 0.0495R² = 0.1725

0

0.02

0.04

0.06

0.08

0.1

0.12

0.14

0.16

0 0.05 0.1 0.15 0.2 0.25 0.3 0.35 0.4

Ab

sorb

ance

Commercial Biodiesel Testing Lab, D 9648, Total Glycerin wt.%

R² = 0.1725

-0.2

-0.1

0

0.1

0.2

0.3

0.4

0.5

0.6

0 0.1 0.2 0.3 0.4 0.5 0.6

Spe

ctro

ph

oto

me

ter,

To

tal G

lyce

rin

, wt.

%

Commercial Biodiesel Testing Lab, D 9648, Total Glycerin wt. %

False Fail

False Pass

70

The statistical analysis of the spectrophotometer total glycerin data is shown below in Table 20. Since

this data did not have an agreeable trend, the average error was very high for the 8:1 data set. Furthermore, 8

of the samples were out of the reproducibility range, for a 50% reproducibility pass.

Table 20: Statistical Evaluation of Spectrophotometer (8:1 Ratio) Total Glycerin Correlation

Parameter Light Sensor Total Glycerin Result

Total Samples 16

Bias 0 % total glycerin (set to zero)

Average Error 0.140% total glycerin

False Pass 5

False Fail 2

Correlation Coefficient 0.1725

Total Values in LoD 16

% Reproducibility Pass in LoD 50.00%

Most of the error bars are still reasonable, but the overall trend is not sufficient in this area, especially

with increasing the B100 concentration. The 10:1 test had the best correlation and best promise for detecting

small amounts of total glycerin. The test can be reassessed at higher methanol to oil ratios, which will cause

the absorbance curve to sharpen at an earlier point. A value of 11:1 or 13:1 may be reasonable and may

provide better data at the region of interest. With a total cost less than $200, the light sensor tester can

provide an economical and accurate measurement of total glycerol in biodiesel samples.

5.4.6. Dielectric Spectroscopy of Biodiesel for Total Glycerin

Eight samples from set 5.1.2 were analyzed using a vector network analyzer with a liquid probe using

30mL sample per run. The complex dielectric properties, being the loss factor (e’’) the relative permittivity

(e’), were determined by carrying out scans at room temperature over a broadband frequency. The frequency

sweep was 200MHz- 20GHz to examine the various dielectric properties of B100 at various conversions.

71

Figure 42 depicts the dielectric loss factor of the various B100 with varying conversion at 7.47 GHz which

was the average loss maximum for the samples. There are two outliers which do not follow the trend.

Figure 42: Dielectric Loss at 7.47 GHz vs. Bound Glycerol Weight Percentage

Figure 42 shows the dielectric loss factor with the same samples except the maximum loss factor

over the frequency range was chosen as the y-axis value. As depicted in figures 42-43, the test is able to

monitor the increasing amounts of total glycerin in samples derived from various feedstocks. The ability of

this test to be carried out with a narrow-band vector network analyzer would allow for only the determination

of the dielectric properties at certain frequencies, allowing for the test to be cut down in cost significantly.

Furthermore, measuring the sample for only a small amount of frequencies will also allow the test to be run

in a short period of time. It will also be of use to look at the dielectric properties of biodiesel with increasing

amounts of methanol and glycerol, as the frequency peaks determined in section 4.2 could contain

information about low quantities of those impurities.

0.5699

0.59460.598

0.59120.5833

0.5831

0.5924

0.6485

0.56

0.57

0.58

0.59

0.6

0.61

0.62

0.63

0.64

0.65

0.66

0.0000 0.1000 0.2000 0.3000 0.4000 0.5000 0.6000 0.7000 0.8000

Die

lect

ric

Loss

(e

'')

Conversion (wt.% bound glycerol)

72

Figure 43: Maximum Dielectric Loss vs. Bound Glycerol Weight Percentage

5.4.7. Ultrasonic Velocity Measurements in Biodiesel for Bound Glycerol

Determination

Speed of sound measurements carried out by measuring the resonant frequencies as described in

section 4.3.1 were carried out on samples described in section 5.1.2. Figure 40 depicts speed of sound

measurements in B100 of various conversions for canola oil. As the temperature of the sample increases, the

speed of sound will decrease in a linear fashion, with R^2 values of .99 reported (Katie Thesis). Thus, the test

can be run in a variety of conditions at different temperature as long as a linear fit is carried out. As shown in

Figure 44, there is a significant decrease in the speed of sound for biodiesel that has higher conversion,

allowing for the calculation of conversion, as depicted in Figure 45.

0.5772

0.5946

0.6012

0.5924

0.5850.5908

0.5934

0.6488

0.57

0.58

0.59

0.6

0.61

0.62

0.63

0.64

0.65

0.66

0.0000 0.1000 0.2000 0.3000 0.4000 0.5000 0.6000 0.7000 0.8000

Die

lect

ric

Loss

(e

'')

Conversion (wt.% bound glycerol)

73

Figure 44: Speed of sound measurements of Canola Biodiesel vs. Temperature (80)

When the speed of sound measurements for the range of data derived from various feedstocks were

initially carried out, no trends were seen. Subsequently, it was found that different fatty acid profiles

corresponding to the feedstock source cause a variation in the speed of sound trend vs. conversion. Thus, the

plot of Figure 45 depicts four different feedstocks of ranging conversion, all with their own trends. Some

feedstocks had more linear trends than others, such as a correlation coefficient of .987 for peanut oil but

0.491 for canola oil. This could be due to a multitude of reasons such as invalid total glycerin results from the

GC or due to sample degradation (as different samples were tested at different times throughout the

semester). Overall, the speed of sound measurements could be a quick and inexpensive method for

monitoring biodiesel quality either in-situ during production or as a standalone testing unit. One thing to be

certain is to ensure that the feedstock is known beforehand so that the correct trend can be chosen for

calculating the impurities. This test may not work efficiently in a multi-feedstock plant for reasons described.

1,370

1,373

1,376

1,379

1,382

1,385

1,388

1,391

1,394

1,397

1,400

1,403

23 24 25 26 27 28

Spe

ed

of

So

un

d (

m/s

)

Temperature (oC)

Canola 0.755 % BG

Canola 0.283 % BG

Canola 0.223 % BG

Canola 0.174 % BG

74

Figure 45: Speed of Sound Measurements at 25oC vs. Bound Glycerol Mass Percentage

5.4.8. Unique In-Column Injection Method for Total and Free Glycerol Determination by GC

Free and bound glycerol measurements were carried out using the unique in-column injection

method described in section 4.4 on the samples found in section 5.1.1. Utilizing the calibration curved

obtained using standards shown in figures 21-24, the free and bound glycerol data was compared to ASTM

values to show the validity of the method. The following sections depict the results of the comparison which

show that this unique test which does not require a cool-on-column injector is valid in many respects.

5.4.8.1. Total Glycerin

As shown below in Figure 46, the correlation of the ASTM method with the unique injection method

for total glycerol determination is highly adequate. With very few outliers, both low conversion and high

conversion samples are comparable.

R² = 0.491

R² = 0.8578

R² = 0.9867

R² = 0.8635

1384

1386

1388

1390

1392

1394

1396

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8

Spe

ed

of

Sou

nd

(m

/s)

Total Glycerol (%wt.)

Canola

Soy

Peanut

Olive

75

Figure 46: Modified GC Method Total Glycerin vs. Commercial Testing Lab Total Glycerin

The table below depicts the statistical evaluation of the unique in-column injection method for

analyzing biodiesel samples for bound glycerol content. The first key note to point out is the significant

negative bias. The bias is mainly due to the broadened peaks which lead to more inaccurate quantification.

The broad peaks of the chromatogram can be caused by imperfect injections and non-uniform heating of the

1 uL injection of biodiesel. The average error is not as significant which may lead the reader to believe this is

an adequate testing procedure for biodiesel. While there were no false passes, 4 false fails occurred with one

major outlier. Surprisingly these false fails were all high biased. The correlation coefficient of 0.9141 shows

this is an ideal method for reproducing ASTM results. With 20 samples in the LoD, 90% of them passed the

reproducibility requirement, which is slightly below the ASTM requirement.

R² = 0.9141

0

0.2

0.4

0.6

0.8

1

1.2

0 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6

PSU

Te

stin

g La

b, D

96

48

, To

tal G

lyce

rin

w

t.%

Commercial Biodiesel Testing Lab, D 9648, Total Glycerin wt.%

False Fail

False Pass

76

Table 19: Statistical Evaluation of Modified GC Total Glycerin Correlation

Parameter PSU Lab Total Glycerin

Total Samples 25

Bias -0.013 % total glycerin

Average Error 0.055% total glycerin

False Pass 0

False Fail 4

Correlation Coefficient 0.9141

Total Values in LoD 20

% Reproducibility Pass in LoD 90 %

5.4.8.2. Free Glycerol

As shown below in Figure 47, the correlation of the ASTM method with the unique injection method

for free glycerol determination is not on par. There are a large amount of outliers and samples do not follow

the correlation line well both above and below the stringent ASTM limit for free glycerol.

Figure 47: Modified GC Method Free Glycerol vs. Commercial Testing Lab Free Glycerol

R² = 0.7132

0

0.02

0.04

0.06

0.08

0.1

0.12

0.14

0 0.02 0.04 0.06 0.08 0.1 0.12 0.14

PSU

Lab

Te

stin

g, D

69

42

Fre

e G

lyce

rol

wt.

%

ASTM D9642, Free Glycerol wt. %

False Fail

False Pass

77

The table below depicts the statistical evaluation of the unique in-column injection method compared

to the ASTM method for determining free glycerol content in biodiesel. While there was very little bias for

this data set, the average error of 0.014% was highly significant deeming this method inadequate for assessing

the limit of 0.02% free glycerol in biodiesel. While the correlation coefficient does not depict this conclusion,

the reproducibility assessment in the LoD showed only 40% of the samples passing the requirement.

Table 20: Statistical Evaluation of Modified GC Free Glycerol Correlation

Parameter PSU Lab Free Glycerol Result

Total Samples 25

Bias +0.007 % free glycerol

Average Error 0.014% free glycerol

False Pass 2

False Fail 4

Correlation Coefficient 0.7132

Total Values in LoD 10

% Reproducibility Pass in LoD 40 %

78

Chapter 6. Discussion

The first assumption of this analysis that should be noted is that the alternative analytical tests were

compared to ASTM tests in which is deemed to be the true value. Since the ASTM test run at the

commercial lab will have experimental error associated with it, it will cause this analysis to be flawed to a

reasonable degree. The second assumption is that if the alternative test deemed the sample to be out of

range or unreadable, the result is discarded since it would not have an associated degree of accuracy with it.

Furthermore, the % pass in the reproducibility range are only of the ASTM results that fell within the limits

of detection for the sample in question. For example, while the QTA apparatus ran 18 samples for free

glycerol, only 8 of those samples fell within the LoD, so 75% of the 8 samples in the LoD agreed with the

reproducibility requirement.

The analysis of the biodiesel samples for total glycerin was carried out using the QTA, I-Spec, PSU in

house GC testing equipment, and the spectrophotometric light sensor, as summarized in Table 21. The

biases for this group were all negative, which may indicate that the commercial ASTM test may have been

positively biased from the true value. Furthermore it is seen that the commercial alternative testing

techniques are biased negative by 0.075 and 0.06 which is approximately 30% of the 0.24% total glycerin

limit while the modified GC method is biased only 5% of the 0.24% limit. The average error shows how

much the QTA system is dramatically better than the I-Spec system for correctly pinpointing the true total

glycerin value, with average error of 0.054% and 0.251% total glycerin, respectively. The spectrophotometer

had a similar average error to the QTA, with a value of 0.068%. While there were no false fails for the

commercial alternative tests, there were 3 false passes for the QTA system and 9 false passes for the I-Spec.

The method of comparison using the correlation coefficient also shows how much more accurate the QTA

system is than the I-Spec, with values of 0.846 and 0.396, respectively. The correlation coefficient of the

spectrophotometer was in between these two commercial tests, with a value of 0.531, which the modified

GC method had a correlation coefficient of 0.914. The goal of the reproducibility check is to have 95% of

the samples fall within the reproducibility limit, but neither of the commercial tests reaches this goal, with

79

76.2% for QTA, 75% for the spectrophotometer and 55.6% for the I-Spec. The modified GC method had

90% of the samples fall within the reproducibility limit showing the potential use of this test as adequate.

The analysis of the biodiesel samples for free glycerol was carried out using the QTA and the

modified GC method, as summarized in Table 21. The results from this section show the difficulty in

measuring very low concentrations of free glycerol in biodiesel fuel. The biases for this group were

significant at 0.007, both being 35% of the 0.02% free glycerol limit. The average errors for both of these

tests were also significant, being approximately 67% of the 0.02 % limit. Out of 18 samples for the QTA,

there was only 1 false pass and 1 false fail. Out of 25 samples for the modified GC method, there were 2

false passes and 4 false fails. The correlation coefficients for these tests show that there still needs to be

modifications to the methods to obtain a better linear fit with the true ASTM value, which are 0.631 for the

QTA system and 0.713 for the modified GC method. Since many of the biodiesel samples fell out of the

LoD for the ASTM test, only 8 samples were applied to the reproducibility assessment for the QTA and 10

to the modified GC method, with % passes of 75 and 40, respectively. While only 8 samples were

successfully assessed with the QTA for free glycerol reproducibility, 75% is a respectable result in respect to

the 95% goal.

80

Table 21: Statistical Comparison of Alternative Testing Techniques for Total Glycerin and Free

Glycerol

Parameter QTA I-Spec PSU

Lab

Light

Sensor

QTA PSU Lab

Total Glycerin Free Glycerol

Total

Samples

25 22 25 16 18 25

Bias -0.075 -0.06 -0.013 0 (set to

zero)

-0.007 +0.007

Average

Error

0.054% 0.251% 0.055% 0.068% 0.013% 0.014%

False Pass 3 9 0 1 1 2

False Fail 0 0 4 1 1 4

Correlation

Coefficient

0.8459 0.3956 0.9141 0.531 0. 6313 0.7132

Total

Values in

LoD

21 18 20 16 8 10

% R Pass

in LoD

76.19% 55.55% 90% 75% 75 % 40 %

The analysis of the biodiesel samples for methanol content was carried out using the QTA and the I-

Spec, as summarized in Table 22. While the QTA fared well overall, the reproducibility limits set by EN

14110 turned out to be very stringent, causing a low score with the reproducibility pass %. Both of the

alternative tests were biased to a significant degree of the 0.2% limit, with the QTA bias being 13.5% and the

I-Spec bias being 26.5% of the limit. The average error of the QTA was even worse with a value of 0.074%

being 37% of the EN limit. The I-Spec’s average error shows that this test does not have the slightest ability

to determine the methanol content of biodiesel fuel, with an average error of 0.205%. While the QTA only

had 3 false passes and 0 false fails, the reproducibility assessment dictates that this test will need further

revisions since it only obtained 41.18% of the samples in the reproducibility range. The I-spec shows 12 false

81

passes implicating that the I-Spec unit does not have the ability to detect increased methanol concentrations

in the range of interest. The correlation coefficient for these tests also shows the large difference of these two

commercial tests in correctly determining these values, with the QTA correlating by 0.896 and the I-Spec

correlating by 0.111.

The analysis of the biodiesel samples for acid number was carried out using the QTA, I-Spec and in

house PSU testing, as summarized in Table 22. The biases for this group were all negative which could

indicate that the commercial ASTM test may have been slightly biased positive. The I-Spec was biased

negative the most with a value of -0.105 and the PSU replication of the ASTM test was biased the least with a

value of -0.057. The average errors for all of the tests were significant, but were much worrying for the

commercial tests than the PSU runs. The QTA test, the I-Spec test and the PSU test were 25%, 46% and

13%, respectively, of the 0.5 limit for acid number. Both the QTA and the PSU lab tests had 4 false passes

and 0 false fails, while the I-Spec had 6 false passes and 3 false fails. The R2 values for both the QTA test and

the PSU test were good, with values of 0.902 and 0.99, respectively. The I-Spec R2 value indicates that it is

not at all linear with increasing acid number, with a value of 0.105. While the QTA system seemed to have

been accurately measuring the ASTM acid value, the ability of the system to fall within the ASTM

reproducibility requirement will need to be addressed to implement this system, with only 33.3% of the

results falling within the required range, which is not close to the 95% requirement. The PSU testing

procedure indicated it will need revamping as well, with 75% of the results falling within the limit. This can be

diagnosed by the bias error, showing that the acid value detected is normally too low, so the operator should

modify the procedure to continue past the titration point that he is accustomed to.

82

Table 22: Statistical Comparison of Alternative Testing Techniques for Methanol Content and Acid

Number

Parameter QTA I-Spec QTA I-Spec PSU

Lab

Methanol Acid Number

Total

Samples

25 21 24 23 25

Bias -0.027 +0.053 -0.083 -0.105 -0.057

Average

Error

0.074% 0.205% 0.123 0.228 0.065

False Pass 0 12 4 6 4

False Fail 3 0 0 3 0

Correlation

Coefficient

0.8957 0.1109 0.9022 0.1048 0.99

Total

Values in

LoD

18 15 24 23 24

% R Pass

in LoD

41.18 % 6.67 % 87.5% 43.48 % 100%

83

Chapter 7. Conclusions and Future Work

7.1. Qualitative Testing Method

7.1.1. pHLip

In conclusion, the pHLip test served as an adequate firewall for samples failing the ASTM limit for

total glycerin. With 56% of the 25 samples failing for total glycerin, the pHLip test correctly failed all of

them. With two false fails that were right near the ASTM limit (+/- 0.017 mass %) there was only 1 pHLip

result that gave a false indication of a passing sample.

On the other hand, the acid assessment turned out to be less efficient, with 20% (5/25) false readings

overall. The color indication method may not be adequate for assessing FFA and oxidized fuel alike. It is the

recommendation of this study to utilize the pHLip test in the field for firewalling off spec samples for total

glycerin. Yet, for the acid value analysis to be sufficient, it may be of use to investigate another field test kit. It

may also be of use for the pHLip test to come with a color chart as reference.

7.2. Quantitative Testing Methods

The independent study of alternative quantitative methods carried out in this thesis did not find any

methods that adhered to the ASTM reproducibility requirements of the tests present in D 6571. Furthermore,

only a portion of the tests contained in D 6571 were addressed. While each of the tests have their own merit

for analyzing constituents contained in biodiesel, at this point in time they cannot replace the high precision

methods in ASTM. The conclusions herewith state how well the methods reproduced ASTM values and

suggestions for making those correlations better.

7.2.1. QTA

This system, which has identified the majority of the critical testing parameters defined by BQ-9000,

could be one of the most useful tools in the biodiesel industry if it becomes more consistent and develops the

84

ability to detect values with higher precision. The following conclusions show that while the system can

correlate well to high precision instruments, out of the 25 samples tested it would not fall within the ASTM

reproducibility requirements.

7.2.1.1. Total Glycerin

The FT-IR analysis of total glycerin was successful, especially at values near the ASTM limit. The

correlation becomes biased negative as total glycerol values exceed 0.4% wt., which may not be of concern

since the LoD of the total glycerin goes up to 0.5% wt. Values below 0.2% wt. were found to be erroneous

which caused some values to exceed the reproducibility requirement. The rapid results of this test and well

correlating results could be a highly useful tool to potentially replace the GC method if the reproducibility is

enhanced.

7.2.1.2. Methanol

The correlation of the FT-IR to GC was overall very accurate. Only a single outlier was observed out

of 25 samples, of which the sample extremely failed bound glycerol. The adverse effect of bound glycerol

toward the methanol determination should be taken into account. Furthermore, the results received for our

analysis had significant digits to the hundredths, while the GC method for methanol detection requires

reproducibility values for low methanol concentrations in the thousandths. If this key issue is addressed, then

the method would have a chance at falling within the GC’s reproducibility requirements.

7.2.1.3. Acid Number

The FT-IR analysis of the acid value of biodiesel fuel had the least amount of correlation compared

with the other FT-IR comparisons. While the ASTM potentiometric method has a wide reproducibility range,

with the QTA scoring 87.5%, the correlation coefficient of 0.422 depicts the wide range of error obtained.

Since the acid number test does not require high precision equipment, it may be the best option to deem the

ASTM method sufficient.

85

7.2.1.4. Free Glycerol

The FT-IR analysis of free glycerol in biodiesel did not correlate very well, especially at low levels of

free glycerol. Furthermore, a large outlier threw off the data, which caused an artificially large reading of free

glycerol. This sample actually had very little free glycerol but 8 fold the limit of methanol. Possibly the impact

of methanol on free glycerol can be addressed. Furthermore, with ASTM values of zero free glycerol content,

the FT-IR detected significant levels of free glycerol. This may be attributed to the methanol as well. Lastly,

when free glycerol content increases, the correlation becomes largely negatively biased.

7.2.2. I-Spec

While the I-Spec has the benefits of being a handheld, in the field biodiesel analyzer with quick

results, the results of this study were largely erroneous. In conclusion, the instrument will need to be

significantly enhanced for the investment cost, operational cost and time involved with it to be beneficial.

7.2.2.1. Total Glycerin

Mostly all of the results of the I-Spec for total glycerol fell between 0.1 and 0.2 mass% total glycerol

independent of the real value. It is concluded that this instrument does not have the ability to give useful data

for total glycerol.

7.2.2.2. Methanol

None of the results of the I-Spec correlated with the GC values for methanol content. Either the I-

Spec gave too low or too high values, indicating that most samples failed while they did not. In conclusion,

this test does not have the ability to give useful data for methanol content.

86

7.2.2.3. Acid Number

Hardly any of the I-Spec acid number results correlated with the ASTM potentiometric method.

With increased acid values, either the instrument gave too low of a value or deemed the sample out of range.

With low acid values, the ranges were quite erroneous.

7.2.3. Spectrophotometer

In this study it was found that emulsions formed due to the presence of total glycerol can be

measured in methanol by a spectrophotometer. The experimental design found that either a 10:1 ratio of

methanol to biodiesel or higher will give the best correlation to the GC method for total glycerin. With the

cost of the system being less than $200 and with correlation data similar to high precision methods, this

instrument will be patented and potentially commercialized. The method was close to meeting the ASTM

requirement for reproducibility, with 75% of the results falling within the required range.

7.2.4. Dielectric Spectroscopy

The potential of DRS to biodiesel quantification is of great potential. The possibility of using

narrowband vector network analyzers at specific frequencies for the detection of bound glycerol, free glycerol

and methanol, could provide a powerful quantification tool. Furthermore, it was shown that this test can

analyze various feedstocks which other low cost tools may have difficulty with. Lastly, DRS for detecting

biodiesel blends could be a highly useful sensor for modern compression ignition engines to tweak the stroke

length and injection pressures.

7.2.5. Ultrasound

While the ultrasonic method proved it was feedstock dependent, a producer could realize these

downsides and still make use of this simple and quick quantification tool. The robustness of the transducers

makes this instrument highly applicable to in-situ fuel quality monitoring. This may be especially beneficial for

producers who use polishing resins where an ultrasonic probe can be placed before and after the polishing

87

media. The probe before the polishing media can ensure that excessive amounts of impurities don’t enter the

media while the probe on the outlet will determine when the media is fully saturated.

7.3. Future Work

While none of the methods conformed to the ASTM reproducibility requirement as it was deemed of

utmost importance for the data provided in this study, future method correlation should be carried out

much differently. Two ASTM methods focus on correlating two instruments, the first being D 6708, A

Statistical Assessment and Improvement of Expected Agreement Between Two Test Methods that Purport

to Measure the Same Property of a Material (84) and secondly to conform testing procedures to E 691,

which is the Standard Practice for Conducting an Interlaboratory Study to Determine the Precision of a Test

Method (85).

As a result of the study, Paradigm has reprogrammed its instrument and should be re-evaluated.

Since the reprogramming can be updated in the instrument’s software as well as for each individual sample

cartridge, the unit at PSU can still be used for future evaluations. An initial study would be to collaborate with

a local biodiesel producer for correlating test specimens that have been evaluated using stringent ASTM

methods.

In utilizing the results of this study and due to the novelty of the testing conditions for the

spectrophotometer, it was deemed sufficient to write a provisional patent application for potential

commercialization. With continued analysis of new samples at higher dilutions of methanol to biodiesel the

test is adequate as a pass/fail with the potential to become a quantitative apparatus. An updated module has

been built for more convenient testing procedures as well as reducing the total cost of the unit to less than

$100 without a microcontroller, and $150 with a microcontroller and associated electronics.

Many states have mandated the use of B2-B5, such as the recent mandate of 2% biodiesel in

Pennsylvania which occurred in 2010. Assurance of quality and accuracy of the blends obtained utilizing

various methods are of a necessity. Studies of instruments capable of measuring biodiesel blends should be

88

conducted, potentially with in-line sensors in engine fuel lines or at the fueling station for highest

convenience to the end consumer.

89

Appendix A. Sample Sets for Biodiesel Quality Testing

A1: 20 Gallon Reaction of WVO from PSU Dining Commons (Set 53)

The goal of this reaction was to various samples throughout the transesterification reaction that are

on-spec and off-spec of the ASTM limits for bound and total glycerin. 66.1 kg of waste vegetable oil (WVO)

was reacted with 100% excess methanol and 0.25% wt. sodium methoxide (NaOCH3) solution. Since the

WVO was derived from canola oil, it was assumed to have mainly oleic and linoleic fatty acids (FA) with an

average MW of 307.014 g/mol. The WVO was filtered with a 600 micron then a 100 micron drum filter

before loading it into the reactor. A representative sample of the feedstock was obtained by mixing it in the

reactor for an acid titration to determine the free fatty acid (FFA) content of the used oil. 3.25mL of 0.025M

NaOH was required to neutralize 1.2g of the oil.

Acid value =mg NaOH / g Oil =(3.25mL)*(40g/mol NaOH)*(0.025M NaOH) =2.708 mg NaOH/g Oil

1.2g Oil

FFA Content = % FFA = (2.708 mg NaOH/g Oil)*(307.014 g/mol Canola FA) *100 = 2.079 % FFA

(1000 mg/g) * ( 40 g/mol NaOH)

The FFA contained in the oil reacts with the alkali catalyst to form sodium soaps, so an extra amount

of catalyst is added to account for the sodium soap production.

(2.079 g FFA/ g Oil) * ( 54 g/mol NaOCH3) = 0.366% extra catalyst added to reaction mixture

307.014 g/mol Canola FA

Initially the WVO was pre-heated to 55 oC and then 80% of the methanol was loaded into the

reactor. Subsequently, a total of 0.866% (0.5% + 0.366%) by weight NaOCH3 was added to the reactor. 0.5%

90

by weight sodium methoxide is the normal amount of catalyst used by our research group and what we have

learned from other small scale producers. Finally the last 20% of the methanol was added to the reactor.

Samples were removed at 50, 60, 80, 90, 100, 110, 120 and 180 minutes. In order to stop the reaction in its

tracks, the sample was titrated and the residual catalyst was neutralized with 1M HCl and then the sample was

placed in an ice bath. It was found from previous experiments that the residual catalyst and heat will continue

the reaction after it is taken from the reactor.

All of the samples were water washed four times, dried at 120 oC for 10 minutes and then run

through 600 mL of ion exchange resin (IOR), PD206 manufactured by Purolite, first by gravity and then the

residual biodiesel was removed by vacuum. Each sample contained less than 300 ppm water as found by

Karl-Fischer Titration.

Conversion Curve

The purified samples obtained from a reaction time of 50-180min were then analyzed by gas

chromatography as per ASTM D 6584. The following reaction curve is shown below where total glycerin

points are labeled with (reaction time, %wt bound glycerol). As shown in the above trend, the conversion

curve from 50-180 minutes is 0.745-0.184 %wt. total glycerin. The main reactant causing low conversion

initially was triglycerides, while monoglycerides content increased throughout the reaction. The last two

samples, 120minutes and 180 minutes would pass the ASTM limit for total glycerin which is 0.24% wt.

91

WVO Conversion Curve from 50-180 Minutes as Measured by ASTM D 6584.

Time of Conversion Total Glycerin

50 .745

80 0.48

90 .359

100 0.265

120 0.227

180 .184

Free Glycerol

The most fully converted sample (180min, 0.184 %wt. bound glycerol) was obtained from a large separatory

funnel where the glycerol is allowed to settle out by gravity for eight or more hours. The first solution for a

stock solution of free glycerol was the crude biodiesel sample that had not been washed. Four more solutions

were created by washing four separate times with a 1:1 vol:vol addition of distilled water and taking samples

180, 0.184120, 0.227

100, 0.265

90, 0.359

80, 0.480

50, 0.745

0.000

0.100

0.200

0.300

0.400

0.500

0.600

0.700

0.800

45 65 85 105 125 145 165 185 205

Bo

un

d G

lyce

rol (

% w

t.)

Time (min)

Total Glycerol

Free Glycerol

Monoglycerides

Diglycerides

Triglycerides

92

out in between each wash. The first wash was carried out at 40 oC to enhance the soap removal. The samples

were then dried at 120 oC for 10 minutes. The last sample was run through IOR to obtain a high quality

standard for bound glycerol and free glycerol. The following free glycerol curve was obtained as per ASTM D

6584.

Figure 34: Final Product (180min) Free Glycerol Curve by Washing Samples

As shown above, the first water wash at 40 oC was very efficient in reducing the free glycerol content

from 0.396 to 0.081 %wt., while the fourth water was and subsequent IOR purification was required to

reduce the free glycerol content below the ASTM limit of 0.02%.

0, 0.396

1, 0.081

2, 0.050 3, 0.039

4, 0.0120.000

0.050

0.100

0.150

0.200

0.250

0.300

0.350

0.400

0.450

0 1 2 3 4 5

Wt.

% F

ree

Gly

cero

l

Number of Water Washes

Wt. % Free Glycerol

93

A.2: Commercial B100 Samples R W HEIDEN ASSOCIATES LLC/JOE PEREZ PSU 093009 USB CONTRACT

SAMPLE GLYERINE % PER ASTM CONVENTION

% MASS

IND TIME

ACID #

NA K

LABEL FREE MONO DI TRI BOUND TOTAL METH HRS MG KOH/G MG/KG MG/KG

PPM PPM A 0.000 0.089 0.056 0.002 0.147 0.147 0.16 3.95 0.35 <1 <1 B 0.001 0.092 0.055 0.001 0.148 0.149 0.17 4.42 0.38 <1 <1 C 0.000 0.079 0.037 0.004 0.120 0.120 0.46 1.21 0.29 <1 <1 D 0.001 0.078 0.036 0.002 0.116 0.117 0.44 1.11 0.29 <1 <1 E 0.005 0.136 0.066 0.007 0.208 0.213 0.18 1.45 0.37 <1 <1 F 0.008 0.163 0.078 0.008 0.249 0.257 0.09 1.12 0.36 <1 <1 G 0.000 0.174 0.052 0.004 0.230 0.230 0.03 2.22 0.95 <1 <1 H 0.000 0.068 0.023 0.000 0.091 0.091 0.19 4.55 0.24 I 0.007 0.156 0.031 0.000 0.187 0.194 0.03 1.38 0.51 J 0.001 0.048 0.015 0.000 0.063 0.064 0.10 1.28 0.44 K 0.003 0.087 0.026 0.000 0.112 0.115 0.02 0.20 0.39 L 0.084 0.199 0.121 0.020 0.341 0.425 0.16 0.60 0.33 M 0.007 0.153 0.037 0.001 0.190 0.197 1.59 0.90 0.40 N 0.112 0.145 0.048 0.006 0.198 0.311 1.11 0.80 0.28 R. HEIDEN 17 299 6860 0.80 0.28

SAMPLE NA K Na + K

H <1 <1 <1

I 1.8 4.3 6.1

J <1 1.8 1.8

K 1.6 4.8 5.4

L 2 194.4 196.4

M 2.3 1.1 3.4

N 2.1 20.0 22.1 Sample Label

GLYERINE % PER ASTM CONVENTION

%MASS

METH

Ind

Time,

Hrs

Acid

No.,

mg

KOH/G

Na,

Mg/KG,

ppm

K,

MG/KG,

ppm FREE MONO DI TRI BOUND TOTAL

0 0.013

0.154 0.046 0.010 0.210 0.223 0.03 3.63 0.51 1.1 3.7

P 0.000 0.096 0.205 0.095 0.210 0.396 0.00 0.22 0.40 1.1 4.4

Q 0.000 0.157 0.302 0.797 1.256 1.256 0.00 0.78 0.52 <1 3.2

R 0.001 0.119 0.081 0.670 0.871 0.871 0.00 0.4 0.51 <1 1.0

S 0.000 0.163 0.137 0.022 0.322 0.322 0.00 0.1 0.62 <1 2.7

T 0.004 0.300 0.307 0.775 1.382 1.385 0.04 0.4 18.12 <1 <1

U 0.024 0.170 0.074 0.017 0.261 0.285 0-16 2.91 0.20 1.9 2.8

V 0.020 0.177 0.068 0.005 0.250 0.270 0.05 1.83 0.33 2 3.1

W 0.042 0.130 0.099 0.029 0.258 0.300 1.08 0.6 0.35 3.1 49.2

X 0.006 0.150 0.190 0.397 0.737 0.743 0.01 2.85 0.48 2.2 <1

Y 0.000 0.120 0.256 0.409 0.785 0.785 0.00 0.97 0.42 1.9 2.4

94

Appendix B. Raw Data of Analytical Instruments

B.1: QTA Raw Data

QTA Raw Data

Sample ID

Total Glycerin

Free Glycerol Methanol

Acid Number

Units %mass %mass %mass mgKOH/g

A 0.12 0.21 0.4

B 0.12 0.21 0.5

C 0.07 0.43 0.2

D 0.1 0.43 0.3

E 0.18 0.18 0.2

F 0.19 0.02 0.2

G 0.2 0 0.6

H 0.16 0.004 0.14 0.2

I 0.21 0.004 0.04 0.3

J 0.17 0.008 0.03 0.3

K 0.24 0.005 0.04 0.1

L 0.37 0.021 0.07 0.2

M 0.22 0.032 0.99 0.2

N 0.29 0.053 1.18 0.1

O 0.23 0.006 0.05 0.4

P 0.15 0.009 0.03 0.4

Q 0.71 0 0.01 0.6

R 0.69 0 0.02 0.6

S 0.21 0.015 0 0.5

T Outlier 0.32 Outlier

U 0.29 0.013 0.13 0.2

V 0.26 0.011 0.07 0.5

W 0.26 0.026 0.82 0.2

X 0.44 0 0.03 0.4

Y 0.4 0 0.02 0.4

95

B.2: I-Spec Raw Data

I-Spec Raw Data

Sample ID

Total Glycerin Methanol

Acid Number

Units %mass %mass mgKOH/g

A 0.13 0.27 0.34

B 0.08 0.33 0.18

C 0.19 0.31 0.4

D 0.09 0.34 0.1

E 0.08 0.29 0.04

F 0.12 0 0.65

G 0.09 0.31 0.16

H 0.13 0.24 0.25

I 0.13 0.21 0.22

J 0.14 0.26 0.2

K 0.14 0.14 0.36

L --out of r --out of r ++out of r

M --out of r ++out of r 0.37

N 3.09 ++out of r 0.71

O 0.12 0.12 0.32

P 0.17 0.27 0.14

Q 0.17 0.33 0.18

R 0.15 0.25 0.25

S 0.22 0.21 0.48

T 1.36 +out of r ++out of r

U 0.13 0.09 0.75

V 0.16 0.1 0.36

W +0ut of r 0 ++out of r

X 0.15 0.19 0.41

Y 0.18 0.27 0.32

96

Appendix C. Calculations for Statistical Representation of Results

1. Total Glycerin

LoD

Rpd Eq

Fail

R = 0.4928*(X+2.51*10^-2)

both

RH rpd val

QTA Diff

ISPEC Diff

PSULab Diff abs qta abs ispec

abs PSULab

A 0.085 -0.027 -0.017 -0.039 0.027 0.017 0.039

B 0.086 -0.029 -0.069 -0.0554 0.029 0.069 0.0554

C 0.072 -0.05 0.07 0.004 0.05 0.07 0.004

D 0.070 -0.017 -0.027 0.001 0.017 0.027 0.001

E 0.117 -0.033 -0.133 -0.093 0.033 0.133 0.093

F 0.139 -0.067 -0.137 0.001 0.067 0.137 0.001

G 0.126 -0.03 -0.14 0.077 0.03 0.14 0.077

H 0.057 0.069 0.039 0.021 0.069 0.039 0.021

I 0.108 0.016 -0.064 -0.015 0.016 0.064 0.015

J 0.044 0.106 0.076 0.082 0.106 0.076 0.082

K 0.069 0.125 0.025 0.246 0.125 0.025 0.246

L 0.222 -0.055 0.013 0.055 0.013

M 0.109 0.023 0.079 0.023 0.079

N 0.166 -0.021 2.779 0.048 0.021 2.779 0.048

O 0.122 0.007 -0.103 0.119 0.007 0.103 0.119

P 0.208 -0.246 -0.226 0.052 0.246 0.226 0.052

Q 0.631 -0.546 -1.086 -0.233

R 0.442 -0.181 -0.721 0.045

S 0.171 -0.112 -0.102 0.006 0.112 0.102 0.006

T 0.695 -0.025 -0.339

U 0.153 0.005 -0.155 -0.035 0.005 0.155 0.035

V 0.145 -0.01 -0.11 0.082 0.01 0.11 0.082

W 0.160 -0.04 -0.022 0.04 0.022

X 0.379 -0.303 -0.593 -0.177

Y 0.399 -0.385 -0.605 -0.186

Total Val 24 22 25

Avg. Error 0.0544

0.251294

0.05452

Total LoD Val 21 18 20

Pass in LoD 16 10 18

Fail in LoD 4 6 2

Bias -0.075 -0.060 -0.013

% pass 76.190 55.556 90.000

97

2. Free Glycerol

rpd Eq LoD

R = 0.1082 * (X + 1*10^-4)^0.4888 Fail

both

RH rpd val

QTA Diff

PSULab Diff abs QTA

abs PSULab

A 0.001 0.011 0.011

B 0.004 0.008 0.008

C 0.001 0.016 0.016

D 0.004 0.023 0.023

E 0.008 0.019 0.019

F 0.010 -0.001 0.001

G 0.001 0.025 0.025

H 0.001 0.004 0 0.004 0

I 0.010 -0.003 0.012 0.003 0.012

J 0.004 0.007 -0.001 0.007 0.001

K 0.006 0.002 -0.003 0.002 0.003

L 0.032 -0.063 0.063 0.063 0.063

M 0.010 0.025 -0.007 0.025 0.007

N 0.037 -0.059 0.016 0.059 0.016

O 0.013 -0.007 0.012 0.007 0.012

P 0.001 0.009 0 0.009 0

Q 0.001 0 0 0 0

R 0.004 -0.001 -0.001 0.001 0.001

S 0.001 0.015 0 0.015 0

T 0.007 -0.004 -0.004 0.004 0.004

U 0.018 -0.011 -0.024 0.011 0.024

V 0.016 -0.009 0.062 0.009 0.062

W 0.023 -0.016 -0.042 0.016 0.042

X 0.009 -0.006 -0.006 0.006 0.006

Y 0.001 0 0 0 0

Total Val 18 25 avg 0.013389 0.01424

Total LoD Val 8 10

Pass in LoD 6 4

Fail in LoD 2 6

Bias -0.007 0.007

% pass 75 40

98

3. Methanol Content

Rpd Eq

LoD

R = 0.221*X + 0.003 Fail

where x is avg of 2 samples both

RH QTA rpd val RH ISPEC rpd Val QTA Diff

ISPEC Diff abs QTA

abs ISPEC

A 0.044 0.051 0.05 0.11 0.05 0.11

B 0.045 0.058 0.04 0.16 0.04 0.16

C 0.101 0.088 -0.03 -0.15 0.03 0.15

D 0.099 0.089 -0.01 -0.1 0.01 0.1

E 0.043 0.055 0 0.11 0 0.11

F 0.015 0.013 -0.07 -0.09 0.07 0.09

G 0.006 0.041 -0.03 0.28 0.03 0.28

H 0.039 0.051 -0.05 0.05 0.05 0.05

I 0.011 0.030 0.01 0.18 0.01 0.18

J 0.017 0.043 -0.07 0.16 0.07 0.16

K 0.010 0.021 0.02 0.12 0.02 0.12

L 0.028 -0.09 0.09

M 0.288 -0.6 0.6

N 0.256 0.07 0.07

O 0.012 0.020 0.02 0.09 0.02 0.09

P 0.006 0.033 0.03 0.27 0.03 0.27

Q 0.004 0.039 0.01 0.33 0.01 0.33

R 0.005 0.031 0.02 0.25 0.02 0.25

S 0.003 0.026 0 0.21 0 0.21

T 0.043 0.28 0.28

U 0.035 0.031 -0.03 -0.07 0.03 0.07

V 0.016 0.020 0.02 0.05 0.02 0.05

W 0.213 0.122 -0.26 -1.08 0.26 1.08

X 0.007 0.025 0.02 0.18 0.02 0.18

Y 0.005 0.033 0.02 0.27 0.02 0.27

Total Val 25 21 avg 0.074 0.205238

Total LoD Val 17 15

Pass in LoD 7 1

Fail in LoD 10 14

Bias -0.027 0.053

99

4. Acid Number

LoD Acid Num Rpd

Fail 0 to .1 0.04

both .1 to 0.5 0.08

.5 and up 15%

RH rpd val

QTA Diff

ISPEC Diff

PSULab Diff abs qta abs ispec abs PSUlab

A 0.08 0.05 -0.01 -0.04 0.05 0.01 0.04

B 0.08 0.12 -0.2 -0.06 0.12 0.2 0.06

C 0.08 -0.09 0.11 0.05 0.09 0.11 0.05

D 0.08 0.01 -0.19 0.03 0.01 0.19 0.03

E 0.08 -0.17 -0.33 0 0.17 0.33 0

F 0.08 -0.16 0.29 0.04 0.16 0.29 0.04

G 0.1425 -0.35 -0.79 -0.11 0.35 0.79 0.11

H 0.08 -0.04 0.01 -0.07 0.04 0.01 0.07

I 0.08 -0.21 -0.29 -0.11 0.21 0.29 0.11

J 0.08 -0.14 -0.24 -0.13 0.14 0.24 0.13

K 0.08 -0.29 -0.03 -0.1 0.29 0.03 0.1

L 0.08 -0.13 -0.08 0.13 0.08

M 0.08 -0.2 -0.03 -0.09 0.2 0.03 0.09

N 0.08 -0.18 0.43 -0.06 0.18 0.43 0.06

O 0.08 -0.11 -0.19 -0.04 0.11 0.19 0.04

P 0.08 0 -0.26 -0.04 0 0.26 0.04

Q 0.08 0.08 -0.34 -0.07 0.08 0.34 0.07

R 0.08 0.09 -0.26 -0.1 0.09 0.26 0.1

S 0.093 -0.12 -0.14 -0.05 0.12 0.14 0.05

T 2.715 -0.2 0.2

U 0.08 0 0.55 -0.06 0 0.55 0.06

V 0.08 0.17 0.03 -0.02 0.17 0.03 0.02

W 0.08 -0.15 -0.35 -0.03 0.15 0.35 0.03

X 0.08 -0.08 -0.07 -0.03 0.08 0.07 0.03

Y 0.08 -0.02 -0.1 -0.02 0.02 0.1 0.02

avg diff 0.123333 0.227826 0.0652

Total Val 24 23 25

Total LoD Val 24 23 24

Pass in LoD 8 6 18

Fail in LoD 9 17 6

Bias -0.083 -0.105 -0.057

% pass 33.333 26.087 75.000

100

References

1. Lang, K., Kalai, A. K., Bakhshi, N. N., Reaney, M.J., Hertz, P.B. (2001). Preparation and characterization of bio-diesels from various bio-oils, Bioresource Technology, 80, 53-62.

2. Knothe, G., Van Gerpen, J., Krahl, J. (2005). The Biodiesel Handbook, AOCS Press, Champaign, Illinois.

3. Mittelbach, M., Remschmidt C. (2004). Biodiesel—The Comprehensive Handbook, published by M. Mittelbach, Karl-Franzens- Universität Graz, Graz, Austria.

4. Peterson, C.L., Reece, D.L., Hammond, B.L., Thompson, J., Beck, S.M. (1997). Processing, Characterization, and Performance of Eight Fuels from Lipids, Applied Engineering in Agriculture, 13, 71–79.

5. Hill, M.E. (1960). Transesterification in Sulfuric Acid, Journal of the American Chemical Society,80 (11),

2866-2868.

6. Standard Specifications for Biodiesel Fuel (B100) Blend Stock for Distillate Fuels, (2010). American Society for Testing and Materials, Philadelphia, PA, ASTM D 6751-10.

7. Biofuels Overview. In The U.S. Energy Information Administration. Retrieved December 6, 2010, from

http://www.eia.doe.gov/cneaf/solar.renewables/page/rea_data/table1_6.html

8. Biodiesel Production Capacity and Production, In The U.S. Energy Information Administration.

Retrieved December 6, 2010, from

http://www.eia.doe.gov/cneaf/solar.renewables/page/biodiesel/table_1.html

9. Van Gerpen, J., Pruszko, R., Clements, D., Shanks, B. (2006). Building a Successful Biodiesel Business,

Biodiesel Basics, pp. 22.

10. Van Gerpen, J., Shanks, B., Pruszko, R. (2004). Biodiesel Production Technology, August 2002-

January 2004, NREL Subcontractor Report: NREL/SR-510-36244.

11. Dantas, M.B., Almeida, A.A.F., Conceicão, M.M., Fernandes Jr., V.J., Santos, I.M.G., Silva, F.C., Soledade, L.E.B., Souza, A.G. (2007). Thermal and kinetic study of corn biodiesel obtained by the methanol and ethanol routes, Journal of Thermal Analysis and Calorimetry, 87, 835-839.

12. Lapuerta, M., Armas, O., Rodriguez, F. (2008). Effect of biodiesel fuels on diesel engine emissions, Progress in Energy and Combustion Science, 34, 198-223.

13. Zhou, W., Boocock, D.G.B. (2006). Phase Behavior of the Base-Catalyzed Transesterification of

Soybean Oil, Journal of the American Oil Chemists’ Society, 83, 1041-1045.

14. Tiwari, K. A., Akhilesh, K., Hifjur, R. (2006). Biodiesel production from jatropha oil with high free fatty

acids: An optimized process, Biomass and Bioenergy, 31(8), 569-575.

15. Berrios, M., Siles, J. (2007). A kinetic study of the esterification reaction of free fatty acids (FFA) in

sunflower oil, Fuel, 86, 2383-2388.

101

16. Freedman, B., Pyrde, E., Mounts, T. (1984). Variables affecting the yields of fatty esters from

transesterified vegetable oils, Journal of the American Oil Chemists’ Society, 61 (10), 1638-1643.

17. Przybylski, R. Canola Oil: Physical and Chemical Properties. Canola Council. Retrieved September 12,

2010, from www.canola-council.org/canola_resources/product43.aspx.

18. Knothe, G. (2001). Analytical methods used in the production and fuel quality assessment of

biodiesel, Transactions of the American Society of Agricultural Engineers, 44, 193-200.

19. Pinto, A.C., Guarieiro, L.L.N., Rezende, M.J.C., Ribeiro, N.M., Torres, E.A., Lopes, W.A., Pereira, P.A.P.,

Andrade, J.B. (2005). Biodiesel: an overview, Journal of the Brazilian Chemical Society, 16, 1313-1330.

20. ASTM InternationalWebsite. Retrieved September 3, 2010, from www.astm.org

21. OEM Information / Standards and Warranties. Biodiesel. Retrieved September 3, 2010 from http://www.biodiesel.org/resources/oems/.

22. The National Biodiesel Board (NBB). Biodiesel. Retrieved September 3, 2010 from http://www.biodiesel.org/

23. McCormick, R.L., Alleman, T.L., Ratcliff, M., Moens, L., Lawrence, R. (2004). Survey of the Quality and Stability of Biodiesel and Biodiesel Blends in the United States in 2004. NREL Technical Report: NREL/TP-540-38836.

24. McCormick, R.L., Alleman, T.L., Deutch, S. (2007). 2006 B100 Quality Survey Results, Milestone Report. NREL Technical Report: NREL/TP-540-41549

25. National Biodiesel Board Web site, Fuel Quality Enforcement Guide, accessed on March 27, 2007,

www.biodiesel.org/resources/fuelqualityguide/.

26. McCormick, R.L., Alleman, T.L., Fouts, L. (2010). Analysis of Biodiesel Blend Samples Collected in the

United States in 2008. NREL Technical Report: NREL/TP-540-46592.

27. BQ-9000 Quality Management Program, BQ-9000, accessed December 5, 2010, http://www.bq-

9000.org/

28. Mittelbach, M. (1996). Diesel fuel derived from vegetable oils, VI: Specifications and quality control

of biodiesel. Bioresource Technology, 56 (1), 7-11.

29. Standard Test Method for Determination of Free and Total Glycerin in B-100 Biodiesel Methyl Esters

By Gas Chromatography (2007). American Society for Testing and Materials, Philadelphia, PA, ASTM D

6584 – 07.

30. Janaun, J., Ellis, N. (2010). Perspectives on biodiesel as a sustainable fuel, Renewable & Sustainable

Energy Reviews, 14 (4), 1312-1320.

31. Kobmehl, S. O., Heinrich, H. (1997). The Automotive Industry's View on the Standards for Plant Oil-

Based Fuels, Fuels, 18-28.

102

32. Bailer, J., Mittelbach, M., Plank, C., Schindlbauer, H. (1994). Handbook of Analytical Methods for

Fatty Acid Methyl Esters Used as Diesel Fuel Substitutes. Research Institute for Chemistry and

Technology of Petroleum Products, Vienna.

33. Worgetter, J., Mittelbach, M., Junek, H. (1983). Diesel fuel derived from vegetable oils: Preparation

and use of rape oil methyl ester, Energy in Agriculture, 2, 369-384.

34. Freedman B.H., Kwolek W.F., Pryde E.H. (1986). Quantitation in the analysis of the transesterified soybean oil by capillary gas-chromatography, Journal of the American Oil Chemists’ Society, 63 (10), 1370-1375.

35. Plank, C., Lorbeer, E. (1995). Simultaneous determination of glycerol, and monoglycerides, diglycerides and triglycerides in vegetable oil methyl-esters by capillary gas-chromatography, Journal of Chromatography A, 697 (1-2), 461-468.

36. Barry, L. B. (2006). Analyze Biodiesel Oil for Glycerin, Using Restek’s Robust Rtx-5 Capillary GC Column, Restek.

37. Standard Test Methods for Flash Point by Pensky-Martens Closed Cup Tester (2007). American Society for Testing and Materials, Philadelphia, PA, ASTM D 93 – 07.

38. Fat and oil derivatives – Fatty Acid Methyl Ester (FAME) – Determination of Methanol Content (2003). European Committee for Standardization, Brussels, EN 14110.

39. Standard Test Method for Water and Sediment in Fuel Oils by the Centrifuge Method (2004). American Society for Testing and Materials, Philadelphia, PA, ASTM D 1796 – 04.

40. Petroleum products – Determination of water – Coulometric Karl Fischer titration method (2000). European Committee for Standardization, Brussels, EN ISO 12937.

41. Mahajan, S., Konar, S., Boocock, B. (2006). Determining the Acid Number in Biodiesel, Journal of the American Oil Chemists’ Society, 83, 567-570.

42. Standard Test Method for Acid Number of Petroleum Products by Potentiometric Titration (2009). American Society for Testing and Materials, Philadelphia, PA, ASTM D 664-09a.

43. Acid Value Determination of Oils (1997). Official Methods and Recommended Practices of the American Oil Chemists’ Society, AOCS Press, Champaign, Method Cd 3a-63.

44. Standard Test Method for Acid and Base Number by Color-Indicator Titration (2008). American Society for Testing and Materials, Philadelphia, PA, ASTM D 974 – 08.

44. Standard Test Method for Cloud Point of Petroleum Products (2005). American Society for Testing and Materials, Philadelphia, PA, ASTM D 2500 – 05.

45. Dijkstra, A., Meeussen W. (1995). Interpreting the oxygen stability index, Proceeding of the World

Congress of the International Society for Fat Research, 3, 629-637.

46. Mittelbach, M., Gangl, S. (2001). Long Storage Stability of Biodiesel Made from Rapeseed and Used

Frying Oil, Journal of the American Oil Chemists’ Society, 78, 573-577.

103

47. Mittelbach, M., Schober, S. (2003). The Influence of Antioxidants on the Oxidation Stability of

Biodiesel, Journal of the American Oil Chemists' Society, 80, 817-823.

48. Fat and oil derivatives – Fatty acid methyl esters (FAME) – Determination of oxidation stability

(accelerated oxidation test) (2003). European Committee for Standardization, Brussels, BS EN 14112.

49. Lacoste, F., Lagardere, L. (2003). Quality parameters evolution during biodiesel oxidation using Rancimat test, European Journal of Lipid Science and Technology, 105, 149-155.

50. Daun, J. K., Hougen, F.W. (1976). Identification of Sulfur Compounds in Rapeseed Oil, Journal of the American Oil Chemists’ Society, 54, 351-354.

51. Gerpen, J., Shanks, B., Pruszko, R.(2004). Biodiesel Analytical Methods August 2002- January 2004,

NREL Subcontracor Report: NREL/SR-510-36240, July 2004.

52. Prankl, H., Korbitz, W., Mittelbach, M., Worgetter, M. (2004). Review of Biodiesel Standardization

World-Wide, IEA Bioenergy.

53. pHLip Test:Biodiesel Field Test Kit, In pHLip Test. Retrieved July 20, 2010, from

http://www.phliptest.com/

54. Wedel, R. (2008). Monitoring Biodiesel Fuel Quality, National Biodiesel Conference. Retrieved

December 5, 2010, from

http://phliptest.com/NBB%20Biodiesel%20Expo%202008%20Fuel%20Quality%202-4-08.ppt

55. Quality Trait Analysis (QTA), Retrieved August 5, 2010, from

http://www2.qta.com/templates/System/default.asp?id=43916

56. Knothe, G. (1999). Rapid Monitoring of Transesterification and Assessing Biodiesel Fuel Quality by NIR Spectroscopy Using a Fiber- Optic Probe, Journal of the American Oil Chemists’ Society, 76, 795–800.

57. Infrared Spectroscopy- The Functional Group Region (2005-2006). Lab Guide for Chemistry 35 and

36: Introductory Organic Chemistry Laboratory. Dept. of Chemistry. Sec. 11.5.3, pp. 316.

58. Dubé, M.A., Zheng, S., McLean, D.D., Kates, M. (2004). A Comparison of Attenuated Total Reflectance-FTIR Spectroscopy and GPC for Monitoring Biodiesel Production, Journal of the American Oil Chemists’ Society, 81, 599–603.

59. Siatis, N.G., Kimbaris, A.C., Pappas, C.S., Tarantilis, P.A., Polissiou, M.G. (2006). Improvement of Biodiesel Production Based on the Application of Ultrasound: Monitoring of the Procedure by FTIR Spectroscopy, Journal of the American Oil Chemists’ Society, 83, 53–57.

60. i-Spec Q 100, Paradigm Sensors, Retrieved August 5, 2010, from http://www.paradigmsensors.com/

61. The 3-27 Conversion Test, Make Biodiesel, Retrieved July 18, 2010, from http://www.make-

biodiesel.org/index.php?option=com_content&view=article&id=153&Itemid=186

104

62. Soap in Oil (1997). Official Methods and Recommended Practices of the American Oil Chemists’

Society, AOCS Press, Champaign, Method Cc 17-79.

63. Formo, M. W., Jungermann, E., Norris, F. A., Sonntag, N. O. V. (1979). Fat‐based surface‐active agents. In Bailey's Industrial Oil and Fat Products, 1, 595‐596

64. Robinson, J. W. (1995). Chapter 8: Spectrophotometry, calorimetry, and polarimetry. In Undergraduate Instrumental Analysis, 386‐410. New York, N.Y.: Marcel Dekker.

65. A. Zawadzki, D. Shrestha, B. (2007). Biodiesel blend level detection using ultraviolet absorption spectra, Transactions of the ASABE, 50, 1349-1353.

66. Sastry, G.S.R., Murthy, A.S.R.K., Prasad, P.R. (2006). Identification and determination of bio-diesel in diesel, Energy Sources, Part A: Recovery, Utilization and Environmental Effects, 28 (14), 1337-1342.

67. Ingle, D. J., Crouch, S. R. (1988). Spectrochemical Analysis, Prentice Hall, New Jersey.

68. TSL230R-LF Programmable Light-to-Frequency Converters (2007). Parallax INC, v1.0.

69. Madigan, M. T., J. Martinko, and J. Parker. (2002). Chapter 6: Microbial growth. In Brock Biology of Microorganisms. Englewood Cliffs, N.J.: Prentice Hall.

70. Mauritz, K. (2007). Broadband dielectric spectroscopic characterization of Nafion chemical degradation, Journal of Power Sources, 172, 72-77.

71. Hippel, A.R. (1954). Dielectric and Waves, 1st Edn., Chap. 1, pp. 3-5, John Wiley and Sons, New York.

72. Hippel, A.R. (1954). Dielectric Materials and Applications, 1st Edn., Chap. 3, pp. 157-167, Wiley, New

York.

73. Runt, J.P., Fitzgerald, J. (1997). Dielectric Spectroscopy of Polymeric Materials- Fundamentals and

Applications, 1st Edn., Chap. 2, pp. 67-72, American Chemical Society, Washington DC.

74. Sorichetti, P.A., Romano, S.D. (2005). Physico-chemical and electrical properties for the production

and characterization of Biodiesel, Physics and Chemistry of Liquids, 43 (1), 37-48.

75. Basics of measuring the dielectric properties of materials (1992). Hewlett Packard literature number

5091-3300E, Application Note 1217-1.

76. Boiteux, G. (1987). Dielectric and Related Molecular Processes in Ion Containing Polymers, Structure

and Properties of Ionomers, 227-245, D. Reidel Publishing Company.

77. Munack, A., Krahl, J. (2003). Erkennung des RME–Betriebs mittels eines Biodiesel-Kraftstoffsensors (Identifying Use of RME with a Biodiesel Fuel Sensor), Landbauforsch, (Special Issue) 257 .

78. Munack, A., Krahl, J., Speckmann, H. (2002). A Fuel Sensor for Biodiesel, Fossil Diesel Fuel, and Their Blends, ASAE, Paper No. 02-6081.

79. Tat, M.E., Van Gerpen, J.H. (2003). Biodiesel Blend Detection with a Fuel Composition Sensor, Applied Engineering in Agriculture, 19, 125–131.

105

80. Boudreau, K. B. (2010). PROGRESS TOWARDS THE DEVELOPMENT OF AN AUTOMATED ULTRASONIC

NONLINEAR MEASUREMENT SYSTEM, M.S. Thesis in Engineering Mechanics, The Pennsylvania State

University.

81. McClements J.D., Povey, M.J.W. (1988). Ultrasonic Velocity Measurements in Some Liquid Triglycerides and Vegetable Oils, Journal of the American Oil Chemists’ Society, 65, 1787–1790.

82. Boelhouwer, J.W.M. (1967). Sound Velocities in and Adiabatic Compressibilities of Liquid Alkanes at Various Temperatures and Pressures, Physica, 34, 484–492.

83. Rolling, R.E., Vogt, C.J. (1960). The Adiabatic Bulk Modulus of Normal Paraffin Hydrocarbons from Hexane to Hexadecane, Trans. ASME–J. Basic Eng., 82,635–644.

84. Standard Practice for Statistical Assessment and Improvement of Expected Agreement between Two Test Methods that Purport to Measure the Same Property of a Material (2007). American Society for Testing and Materials, Philadelphia, PA, ASTM D 6708 – 08.

85. Standard Practice for Conducting an Interlaboratory Study to Determine the Precision of a Test Method (2007). American Society for Testing and Materials, Philadelphia, PA, ASTM E 691 – 99.