Utilization of Fats, Oils and Grease in Biodiesel Production

Utilization of Fats, Oils and Grease in Biodiesel Production:

From Market Study to Technical Feasibility

A thesis submitted to

Department of Chemical and Environmental Engineering

Division of Graduate Studies

University of Cincinnati

In partial fulfillment of the requirement for the degree of

Master of Science

2017

Junsong Zhang

B.E. in Mechanical Engineering, Shandong University, 2014

Committee:

Mingming Lu, PhD (Chair)

Drew McAvoy, PhD

Maobing Tu, PhD

Fumin Ren, PhD

Abstract

The biodiesel industry has continued to boom in the past few decades and new technologies have been developed to handle low-cost feedstocks. Fats, oils and grease (FOG), a nuisance to the environment, has attracted increasing attention in biodiesel production in recent years. This thesis started with a market feasibility study on FOG management in wastewater treatment plants

(WWTPs) to determine the potential market from FOG to biodiesel. Furthermore, a greener alternative titration method for measuring acid numbers in FOG was developed. Lastly, the challenges of reducing sulfur content from FOG to biodiesel are also investigated using three different types of FOG. The purpose of this thesis is to evaluate the utilization of FOG in biodiesel production, from the perspective of market to technical feasibility.

We conducted a survey of 29 WWTPs, which indicated that the preferred method of FOG handling depends on geographical location. Landfill is still the major FOG disposal method

(76.23% of capacity) followed by anaerobic digestion (12.29%) and incineration (11.48%).

Furthermore, FOG processing technology also needs to be custom developed based on each location and each WWTP. A potential market of WWTP-FOG for biodiesel production could be achieved by reducing the current disposal cost significantly and producing environmental- friendly biodiesel.

The current titration methods to determine the free fatty acid content in FOG could generate a huge amount of toxic solvent waste such as toluene. In this study, a greener method is proposed for measuring acid numbers (ANs) in comparison with the official AOCS cd 3d-63 method. This greener method can eliminate the use of toluene, and decrease the amount of isopropyl alcohol

(from 125ml to 50ml), which in turn reduces toxicity and cost. 44 samples of yellow and brown grease with a wide ANs ranging from 0.133 to 170.369 mg KOH/g were titrated to compare to

AOCS cd 3d-63. Various statistical tests including repeatability, linear model, f-test, t-test and calibration methods were conducted to evaluate the performance of green methods. Besides, the minimum detection for visual titration is determined as 0.0657 mgKOH/g for the first time. This green method can be recommended for routine titration for biodiesel plants.

Sulfur issues may become an obstacle for FOG re-utilization due to high sulfur content (up to

1000 ppm). So it is essential to study sulfur distribution from FOG to biodiesel and ensure that biodiesel produced can meet the ASTM D6751 sulfur standard (15 ppm). In this research, three sources of FOG (SD1, MSD, JTM) were investigated to evaluate the sulfur transfer from FOG to biodiesel. Sulfur content of raw FOG may vary from 300 to 800 ppm. Implementing oil- extraction processes (blending grease with WCO) and biodiesel reactions, sulfur concentration can be reduced significantly, by 66.7% and 96.97%, respectively. By using an appropriate WCO-

FOG weight ratio for oil separation (at 3.6 for MSD, 4.5 for SD1), all of the biodiesel products in this study can meet the ASTM D6751 sulfur standards (15ppm).

Keywords: FOG, Biodiesel, Market, Acid Number, Titration, Sulfur. Wastewater treatment plants.

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Acknowledgement

I would like to thank Dr. Mingming Lu, my committee chair, for offering the major direction and valuable suggestions for this thesis. Also, I deeply appreciate other committee members: Dr.

Drew C. McAvoy, and Dr. Maobing Tu and Dr. Fumin Ren, for their effort on my master study.

The support from National Science Foundation I-Corps Program (IIP 16 IIP-1660675) is fully appreciated. The committee members in NSF offered not only financial support but also business market strategy for this research. Many thanks to Mr. Art Helmstetter for his advices on the customer discovery project.

I would like to thank my other colleges: Qingshi Tu, Yang Liu, Nathan Holliday, Son Dong, Minghao Kong for their help on my master study.

Last, I would like to thank my family: Quansheng Zhang, Hongfei Zhao and my fiancé Xiaolin Wang for their forever support on my life study and career.

Table of Contents

Chapter 1. INTRODUCTION ...... 1 1.1 Background ...... 1 1.2 Biodiesel Feedback ...... 2 1.3 Fats, Oil and Grease (FOG) ...... 5 1.3.1 FFA Profile in FOG ...... 9 1.3.2 FFA Quantification Methods ...... 10 1.3.3 Sulfur content in FOG ...... 12 1.4 Goal Statement ...... 12 1.4.1 FOG Market Feasibility ...... 12 1.4.2 Green Titration Method ...... 12 1.4.3 Sulfur Analysis of FOG ...... 13 2. MANAGEMENT OF FATS, OILS AND GREASE (FOG) IN WASTEWATER TREATMENT PLANTS (WWTPs) ACROSS THE US ...... 14 2.1 Objective ...... 14 2.2 Current FOG Management – FOG Programs ...... 16 2.3 Current Disposal/Utilization of FOG ...... 17 2.3.1 Landfill ...... 17 2.3.2 Anaerobic Digestion ...... 19 2.3.3 Incineration ...... 22 2.4 Result Summary ...... 22 3. A GREEN ALTERNATIVE TITRATION METHOD FOR DETERMINATION OF THE ACID NUMBERS (ANs) OF FATS, OILS, GREASE (FOG) ...... 23 3.1 Objective ...... 23 3.2 Acid Number and FFA % ...... 23 3.2.1 AOCS Methods ...... 24 3.2.2 ASTM Methods ...... 26 3.3 Literature Review...... 27 3.4 Methodology ...... 33

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3.4.1 Proposed Green Method ...... 33 3.4.2 Detection limit of Visual Titration ...... 34 3.4.3 Statistic Analysis ...... 36 3.4.4 Method Calibration ...... 38 3.5 Results and Discussion ...... 39 3.5.1 Repeatability ...... 39 3.5.2 Linearity between Green and AOCS Cd 3d-63 Method ...... 43 3.5.3 Results of ANOVA f-test and t-test...... 45 3.5.4 Results of Method Calibration ...... 46 3.5.5 Density versus Acid Number ...... 47 3.6 Solvent Recovery in Titration Waste ...... 49 3.6.1 Introduction ...... 49 3.6.2 Results Summary ...... 50 3.7 Conclusion ...... 53 4. SULFUR ANALYSIS OF FATS, OILs AND GREASE (FOG) ...... 55 4.1 Introduction ...... 55 4.2 Literature Review and Objectives ...... 56 4.3 Sulfur Analysis Standards ...... 57 4.4 Materials ...... 59 4.5 Methodology ...... 59 4.5.1 Sample Source ...... 59 4.5.2 Sample Preparation ...... 61 4.6 Results and Discussions ...... 64 4.6.1 FOG properties Overview ...... 64 4.6.2 Sulfur flow from FOG to Biodiesel ...... 65 4.6.3 Ultimate Analysis of FOG ...... 69 5. CONCLUSIONS AND FUTURE DIRECTIONS ...... 72 REFERENCE ...... 74 APPLENDIX ...... 82

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List of Figures

Figure Page

1 US Biodiesel Consumption 2001-2016, million gallons ...... 2 2 Chemical Structure of Stearic Acid and Linoleic Acid ...... 6 3 Critical Cut-off points of Acid Number in Biodiesel Production ...... 13 4 Surveyed Wastewater Treatment Plants ...... 16 5 (Left): Trap Grease from MSD GC, OH; Right: Trap Grease from SD1, KY ...... 18 6 A Complete Titration Process in AOCS Cd 3d-63 ...... 25 7 Potentiometric Titration curves Obtained by the Application of (a) point-by-point titration and (b) Metrohm Titrando 808 automatic titrator...... 35 8 Green Method AN vs AOCS Cd 3d-63 AN for Overall Samples ...... 43 9 Green Method AN vs AOCS Cd 3d-63 AN for Yellow Grease ...... 44 10 Green Method AN vs AOCS Cd 3d-63 AN for Brown Grease ...... 44 11 Experimental AN (AOCS Cd 3d-63 and proposed green method) versus Calculated AN for Biodiesel Feedstock ...... 47 12 Density vs Acid Number for 44 samples ...... 49 13 Experimental Setup for Solvent ...... 51 14 (a) Titration Waste; (b) Residue Liquid Layer after Solvent Recovery ...... 52 15 Recovery Rate versus Vacuum at 80°C on Titration Waste ...... 52 16 Flow Chart of the Experimental Process (Specific for methanol removal) ...... 63 17 Sulfur Flow from JTM Grease to Biodiesel ...... 66 18 Sulfur Flow from MSD Grease to Biodiesel...... 67 19 FFA vs Time of SD1 Grease ...... 68 20 Sulfur Flow from SD1 Grease to Biodiesel ...... 69 21 H/C and O/C Ratios on the Van Krevelen Diagram ...... 71

List of Tables

Table Page 1 Feedstock Input Usage % in Biodiesel Production from 2010 – 2014 ...... 3 2 FFA Range and Market Price of Three Types of Biodiesel Feedstock in this Research ...... 4 3 Common FFA types in Oils ...... 6 4 Free Fatty Acid Profile of the Major Commodity Oils (wt%) ...... 7 5 Free Fatty Acid Profile Reported by Literature (wt%) ...... 11 6 Information Collected during Interviews ...... 15 7 Summary of Facilities with FOG for Landfill ...... 19 8 Standard Method to Determine Acid Number of Biodiesel and Feedstock ...... 24 9 Oil Sample Size Selection in AOCS Cd 3d-63 Method ...... 25 10 Literature Summary of the Acid Number Determination Methods in Biodiesel Production ...... 32 11 Summary of Sample Size, AN /Density Range and Yypes ...... 34 12 Statistical Summary of Samples by Types...... 34 13 Detection Limit of Visual Titration for Common FFAs ...... 36 14 Mean Repeatability and RSD of Two Methods on Overall Samples ...... 40 15 Mean Repeatability and RSD of Two Methods on Biodiesel (AN<0.5) ...... 40 16 Mean Repeatability and RSD of Two Methods on Yellow Grease (AN<30) ...... 41 17 Mean Repeatability and RSD of Two Methods on Brown Grease (AN>30) ...... 42 18 ANOVA f-test Results for Yellow Grease, Brown Grease and Overall Samples ...46 19 Results of AOCS 3d-63 and Green Method Calibration ...... 47 20 Composition of Titration Waste with AOCS Cd 3d-63 and Green Methods ...... 50 21 Sulfur Analysis Standards ...... 58 22 Pathways for Three Types of FOG ...... 61 23 Overview of FOG Properties ...... 64 24 Sulfur Content in Three Types of FOG, unit: ppm ...... 65 25 Ultimate Analysis of FOG and Solids ...... 70

Abbreviations

FOG: Fats, Oil and Grease

WWTP: Wastewater treatment plant

WCO: Waste Cooking Oil

AN: Acid Number

FFA: Free Fatty Acid

FAME: Free Fatty Acid Methyl Ester

MGD: Million Gallons per day

PBE: Proton Balance Equation

JTM: JTM Food Group, Harrison, OH

MSD: The Metropolitan Sewer District of Greater Cincinnati, Cincinnati, OH

SD1: Sanitation District No. 1 of Northern Kentucky, Fort Wright, KY

H&F: Heating and Filtration

WGE: Waste Grease Extraction

RSD: Relative Standard Deviation

ANOVA: Analysis of Variance

IPA: Isopropyl Alcohol

AOCS: American Oil Chemists' Society

ASTM: American Section of the International Association for Testing Materials

LCFS: California Low Carbon Fuel Standard

NAAQs: National Ambient Air Quality Standards

HHV: Higher Heating Value

Chapter.1 Introduction

1.1 Background

Concerns on climate changes and fossil fuel depletion have motivated the governments and

individuals to approach environment friendly fuels. Biodiesel, a promising alternative biofuel,

has attracted more and more attention in recent years. As reported by the United States

Environmental Protection Agency (USEPA), the emission of particulate matter (PM) from

burning B20 (20 % biodiesel, 80% diesel) would be reduced by 10 % compared to burning

regular diesel. In addition, biodiesel emits less carbon monoxide and unburned hydrocarbons,

and it contains less sulfur (Zhang et al. 2013). Approximately 2.1 billion gallons of biodiesel

have been consumed in the United States in 2016, which shows a rapid increasing since 2011

(Figure 1, EIA, 2017). Biodiesel is technically defined as: “a fuel comprised of mono-alkyl esters

of long chain fatty acids derived vegetable oils or animal fats, designated B100, and meeting

ASTM D6751 standards (ASTM D6751)”. Pure biodiesel can be applied in the traditional diesel

engine without required modifications (Demirbas, 2008).

Figure 1. US Biodiesel Consumption 2001-2016, million gallons

1.2 Biodiesel Feedstocks

The major feedstock for biodiesel production is refined soybean and canola (rapeseed) oil in the

US (EIA 2016). These feedstocks are typically in a high quality form and maintain a stable

market. And also, they are the easiest to process for producing biodiesel compared to other

feedstock (Demirbas, 2008). Moreover, 51.67 % of total biodiesel feedstock consumed in

October 2016 came from soybean oil. (EIA Report 2016). However, the price of soybean oil is

increasing in recent years, both in food and fuel.

Beyond vegetable oils, waste cooking oil (WCO) is another feedstock for biodiesel production.

Due to variations in cooking, storage as well as seasonal impacts, the free fatty acid content

(FFA%) in WCO can be up to 20%, however, 1% to 9% is a more normal range (AGMRC

Report. 2016). Like soybean oil, there is also a stable market for waste cooking oil in the US.

The third class of biodiesel feedstock includes waste yellow or brown grease derived from waste fats. In most cases, these greases can be obtained from restaurants, grease haulers, meat processing facilities and wastewater treatment plants (WWTPs). Currently, there is no significant market position for brown grease especially trap grease. The free fatty acid content of brown grease can be up to 80% and it has a larger fluctuation than soybean oil and WCO. Research and development improvements such as oil extraction need to be developed to investigate the technical feasibility from grease to biodiesel. (Ragauskas et al. 2013; Chai et al. 2014; Tu et al.

2016)

Table 1. Feedstock Input Usage % in Biodiesel Production from 2010 – 2014 Unit: Million pounds (Source: U.S. Energy Information Administration Table 3)

As shown in Table 1, waste materials, such as yellow and brown grease as biodiesel feedstock, increased rapidly from 2010 to 2014. Due to an increasing demand for biodiesel, 1,074 million gallons of yellow grease, were introduced to the US biodiesel production in 2014, which is much

3 higher than 2010. Under the California Low Carbon Fuel Standard (LCFS), biodiesel made from

FOG is among the lowest in carbon intensity (86% carbon reduction), and will receive more attention as municipalities are moving toward lower carbon intensity (Farrell and Sperling 2007).

It can be predicted that biodiesel derived from waste materials will be booming continuously in the future.

One alternative way, biodiesel feedstock can be classified by free fatty acid content as follows

(AGMRC Report. 2016):

1. Refined soybean, canola oil and others (FFA < 1.5%)

2. Waste cooking oils and animal fats with low FFA (FFA < 4%), yellow grease

(FFA<15%)

3. FOG with high FFA, brown grease (FFA ≥ 15%)

Table 2 summarized four types of biodiesel feedstock used in this study. In order to make the biodiesel more price-competitive with diesel, studies to assess the feasibility of using inexpensive waste materials as feedstock for biodiesel production need to be undertaken.

Table 2. FFA Range and Market Price of Three Types of Biodiesel Feedstock in this Research

Biodiesel Feedstock FFA % range Current Market Price Waste Cooking Oil (WCO) 0 – 6, up to 9 $0.26/lb a Yellow Grease 0 - 15 $0.21/lb a Brown Grease >15 N.A. a By USDA Price. Dec, 2016

1.3 Fats, Oils and Grease (FOG)

Fats, oils and grease (FOG) refers to a mixture of lipids, water and solids generated from grease

traps of restaurants, food processors and individuals. FOG from commercial sources is usually

collected by certified grease haulers, and they are disposed either at a municipal wastewater

treatment plant (WWTP), reused somehow, or sent to landfills. It was estimated that 4.24 billion

lbs of FOG is produced annually in the US (Wiltsee. 1998). Currently, the major disposal method

for FOG in the US is still landfilling. In recent years, there has been an increase in interest of

using FOG as biodiesel feedstock. (Hums et al. 2016; Tu et al. 2016; Ragauskas et al. 2013;

Wallace et al. 2016). Technical challenges of utilizing FOG as a biodiesel feedstock mainly lie in

the low and unstable oil quality, especially in terms of high free fatty acid (FFA) content. (Chai

et al. 2014, Tu et al. 2016)

FOG mainly consists of free fatty acids and glycerides (triglyceride, diglyceride and

monoglyceride). The FFA content and total amount of glycerides can determine the quality of

biodiesel feedstock. In food and oleochemical industries, the FFA content can reflect the

nutritional quality of the oil product and FFA types indicate how feedstock is treated in both

industrial processing and storage. (Wang. 2012, Aricetti and Tubino. 2012.)

1.3.1 FFA Profile in FOG

Fatty acids are belonging to straight chain aliphatic carboxylic acids. There are over 1,000 types

of natural free fatty acids from C4 to C22, where C16 to C18 are the most common. Table 3

summarizes the names, formulas and sources of common FFAs. The common structure of FFA

consists of two carbon units and cis double bonds inserted at specific positions. The multiple

5 selections of positions will result in a large quantity of fatty acids varying in chain length and unsaturation (Bailey. 1964). Saturated fatty acids have a straight hydrocarbon chain, however, unsaturated acids have a bend chain (Figure 2). In comparison with unsaturated fatty acids, saturated fatty acids without cis double bonds are less likely to be oxidized and have a higher cetane number, but crystallization may occur at very high temperatures. (Cancakci and Gerpen.

2001)

Table 3. Common FFA types in Oils (Source: Shahidi, 2005) Free Fatty Common Formula Significant Source Acid Name C16:0 Palmitic CH3(CH2)14CO2H cottonseed, palm Acid C18:0 Stearic Acid CH3(CH2)16CO2H cocoa butter, tallow C18:1 Oleic Acid CH3(CH2)7CH=CH(CH2)7CO2H cottonseed, olive, palm, rape C18:2 Linoleic CH3(CH2)4(CH=CHCH2)2(CH2)6CO2H corn, sesame, soybean, Acid sunflower C18:3 Α-linolenic CH3CH2(CH=CHCH2)3(CH2)6CO2H linseed Acid

Figure 2. Chemical Structure of Stearic Acid and Linoleic Acid Source: http://wps.prenhall.com/

Typical fatty acid compositions of commodity oils are shown in Table 4. Linoleic acid, C18:2, is dominant (53%) in soybean oil, which is the major feedstock of biodiesel. However, the other commercial vegetable oil such as palm, olive and rape mainly consist of oleic acid (C18:1).

Stearic acid (C18:0) only occurs in animal fats like tallow and lard with 31% and 11%

6 respectively, and it can be ignored in vegetable oils (Shahidi, 2005). Unlike commodity oils, waste yellow or brown grease has a larger variation in FFA composition and it is more difficult to control quality (Ragauskas et al. 2013; Chai et al. 2014; Tu et al. 2015

TABLE 4. Free Fatty Acid Profile of the Major Commodity Oils (wt%). (Source: Shahidi, 2005 ) C16:0 C18:0 C18:1 C18:2 C18:3 Other Soybean 11 22 53 8 6 Palm 44 40 10 6 Olive 10 78 7 5 Rape 4 56 26 10 4 Tallow 26 31 31 2 10 Lard 27 11 44 11 7

Some research studies were attempting to quantify the FFA profiles in FOG and to re-utilize them in biodiesel production. A few representative studies are summarized as follows:

Ngo et al (2011) analyzed trap grease from San Francisco, CA (SF) and Atlanta, GA (ATL) in the US. A distillation process is applied to eliminate moisture and other contaminations. Three types of grease (Distilled ATL, ATL, SF) are analyzed by converting the FFA into free fatty acid methyl ester (FAME). The FAME is analyzed by GC-MS. It is assumed that the profile of

FAME can reflect that amount of FFAs.

Wang et al. (2008) reported the FFA profile of both brown grease form Guangzhou EPA, China and refined soybean oil from Korea. The results of soybean oil are quite close to the profiles in

Table 4 with that C18:2, largest part: 53.27% and C18:1: 23.12%. Chai et al. (2014) analyzed

FAME profile of waste cooking oil (5% FFA) from the Cincinnati Zoo, Ohio. They also plot a curve for optimizing the best methanol-to-FFA molar ratio for different FFA levels in

7 esterification. Kamali et al. (2011) also analyzed the fatty acid profile of trap grease samples from Kalamazoo, MI by a spray desorption collection system and followed by GC-MS analysis.

The FFA profile from literature are summarized in Table 5 with their source, AN and average molecular weight. No big difference between the distribution of FFAs is observed in different grease samples (Table 5). Among the FFAs, C18 is the most common acid in grease which takes

57.53% of total acids in minimum. The saturated fatty acids level varies from 31.42% to 48.85%, while the heavier fatty acids (≥C14) of the waste grease samples are all extremely high, varying from 88.28% to 97.51%.

There is significant similarity in FFA composition between refined commodity oil and waste grease (Tables 4 and 5). The C18:1 acid is the dominant component for most samples accounting from 40-79% by weight. However, C18:2 acid is dominant in refined soybean oil at 53%.

Moreover, C16:0 represents 11-27% by weight in refined oil and 17.9%-31.2% in waste grease.

Since both wastewater treatment plant and grease haulers collect from food processing facilities where vegetable oil is widely used. It can be assumed that waste grease is mainly derived from vegetable oil but with a complicated heating, degrading, oxidizing processes (Aricetti and

Tubino. 2012). The C16: 0, C18:1 fatty acids increase, while the C18:2, C18:3 fatty acids decrease significantly when AN increase from soybean oil to yellow/brown grease (Table 5).

However, no relevant reference was found that explains the mechanism of how C18:2, C18:3 are converted to C16:0, C18:1 during the process from vegetable oil to waste grease. Also, for waste grease FFA profile analysis, an in-direct measurement is commonly employed, that convert FFA

8 to FAME and then measures the FAME composition. (Chai et al. 2014, Kamali et al. 2011,

Montefrio et al. 2010, Nisola et al. 2009). It is unknown whether the FFA composition would vary significantly, after esterification/transesterification conversion.

1.3.2 FFA Quantification Methods

The FFA content in oil is quantified by acid number (AN), a parameter which is defined as the amount (mg) of KOH necessary to neutralize 1.0 g of oil samples (AOCS cd 3d-63). AN is a critical indicator for the quality of both FOG and biodiesel. The corresponding threshold values for FOG and biodiesel are 0.5 mg KOH/g for biodiesel (ASTM D6751) and 30 mg KOH/g for the cut off points of yellow and brown grease. Acid number is also crucial for determining chemical dosage and conditions of esterification step for biodiesel production. (Ragauskas et al.

2013; Chai et al. 2014; and Tu et al. 2016, Agnew et al. 2009).

The official methods to determine AN includes visual and potential titration. Compared to potential titration, visual titration method is more cost-efficient and easier, which is widely accepted in biodiesel industry. Solvent mixture of IPA and toluene is used to dissolve oil samples in the official visual method (AOCS Cd 3d-63). However, the toluene usage can result in toxicity concerns. (Ukai et al. 1994; Aricetti and Tubino 2012; Baig et al. 2011). Besides, due to multiple titrations required for FOG, the high amount of solvent usage (125 ml) in Cd 3d-63 may also generate a large quantity of waste solvent. It is an increasing burden for biodiesel labs and industry (Aricetti and Tubino 2012; Baig et al. 2011; Kovacs et al. 2011; Wang et al. 2008;

Figueiedo et al. 2015).

1.3.3 Sulfur Content in FOG

In addition to the challenge of waste solvent generation, the other technical concern on biodiesel production using FOG is the high sulfur content. Studies have reported that FOG may contain

62.3 ppm to 1,750 ppm sulfur, which is much higher than the limitation of 15 ppm of biodiesel

(ASTM D6751; Keener et al. 2008). It is also reported that biodiesel made from FOG alone can not meet the 15ppm sulfur standard (SFWPS. 2011; Cairncross et al. 2016; Chakrabarti et al.

2008). So it is essential to study the sulfur concentration flow from FOG to biodiesel, as well as to investigate the method of producing sulfur qualified biodiesel from FOG (He et al. 2009; Kim et al. 2011; Ward et al. 2012; Ragauskas et al. 2013).

TABLE 5. Free Fatty Acid Profile Reported by Literature (wt%) Chai et Ngo et al. Nisola et Neczaj et Montefrio Wang et al. 2008 Canakci. 2007 al. 2014 2011 al. 2009 al. 2012 et al. 2010 FFA Trap Brown Yellow Soybean Brown Brown Not Not Soybean Oil Grease Grease Grease Oil Grease Grease Mentioned Mentioned C10:0 ------0.41 - - - C12:0 - - 0.02 - - - 0.74 0.03 - - C14:0 1.16 0.07 0.03 1.66 2.43 2.82 0.02 0.01 1.16 C14:1 0.39 C16:0 30.38 10.95 3.34 22.83 23.24 10.58 23.1 25.8 28.83 30.38 C16:1 1.42 0.13 3.13 3.79 1.84 0.03 1.2 C18:0 6.02 4.31 2.09 12.54 12.96 4.76 10.2 0.06 16.31 7.2 C18:1 38.39 23.12 79.75 42.36 44.32 22.52 43.8 39.6 53.37 38.39 C18:2 18.83 53.27 12.39 12.09 6.97 52.34 11.3 19.9 15.1 C18:3 1.31 6.77 2.04 0.82 0.67 8.19 0.93 C20:0 0.27 0.22 0.4 0.4 Other 2.49 1.24 0 4.57 5.62 1.61 4.07 14.16 1.48 6.57 Saturated 37.56 15.6 5.7 37.03 38.63 15.34 37.67 26.31 45.15 38.74 FFA % 49.6 5 82.9 40 Acid 165.8 80 Number 99.2 10 EPA, Commercial, WCO, Restaurants, Meat WWTP, Source N.A. Restaurants China Korea resultants Haulers processing Singapore MW ave, 272.98 281.91 281.33 274.35 270.62 286.99 272.55 274.26 275.17 272.79 g/mole

1.4 Goal Statement

As a newly emerging idea, this study is focused on market study and technical feasibility of FOG used for biodiesel production. Accordingly, this thesis covers three topics: (1) Survey to assess

FOG management practices in the US wastewater treatment plants (WWTPs) and investigate the potential market from converting FOG to biodiesel; (2) Develop a green titration method for determining the acid number in FOG; and (3) Conduct sulfur analysis of FOG.

1.4.1 FOG Market Feasibility

The FOG in wastewater treatment plants (WWTPs) has been a serious burden in most areas in the US. The average FOG generated is 13.3 lb/ person/year in the US (Wiltsee 1998), which translates to 4.24 billion pounds per year. Furthermore, the biggest cause (47%) of sewer pipe blockage is by fat, oil and grease (FOG), and the cost to a municipality can easily be in millions of dollars (US EPA, 2003; 2007). In some states, commercial food services are required to install and their grease inceptors, and the FOG is taken by waste disposers to wastewater treatment plants (WWTPs) or to landfills. If the FOG can be made into biodiesel, it can reduce waste generation, save landfill cost and produce alternative energy with a low-cost feedstock. In this study, the market feasibility of FOG in WWTPs will be investigated by 29 surveyed WWTPs in the US. The purpose is to study the good FOG management practices and discover the potential market from FOG to biodiesel.

1.4.2 Green Titration Method

Acid number is a critical factor in quality control for both biodiesel and biodiesel feedstock.

Biodiesel with high acid number can be detrimental to motors, injectors and also oxidize storage tank (Aricetti and Tubino. 2012). Current titration methods to measure AN (AOCS and ASTM

12 methods) can generate a lot of waste toxic solvents. The significance of testing AN in biodiesel feedstock is to develop a simple, reliable and green method which is suitable over a wide AN range of values (Figure 3). Among those points, the cut-off AN for brown grease and yellow grease is 30 mg KOH/g. This value is used to distinguish samples in this study. According to

Ragauskas et al. 2013; Chai et al. 2014; and Tu et al. 2015, AN of waste grease (AN > 20 mg

KOH/g) needs to be measured accurately to determine the chemical dosage during esterification.

Therefore, there is a need to develop a new green method and validate the method by using a wide range of AN values (Figure 2).

Figure 3. Critical Cut-off Points of Acid Number in Biodiesel Production

1.4.3 Sulfur Analysis of FOG

The sulfur content from FOG to derived biodiesel is turning to be a critical parameter due to the strict standard on sulfur of biodiesel. In this study, the sulfur content of FOG from three sources were analyzed. The purpose is to report sulfur concentration, to investigate the transfer of sulfur from FOG to biodiesel, and to check if biodiesel product could meet ASTM D6751 standards. In addition, FOG and solids analysis were conducted.

Chapter 2. Management of Fats, Oils and Grease (FOG) in Wastewater Treatment Plants (WWTPs) across the US

2.1 Objective

FOG, or fats, oils and grease, in public wastewater systems has caused the most blockages in

sewer pipes in the US and other developed parts of the world. Fats, oils and grease (FOG) refer

to a mixture of lipids, water and solids generated from the grease traps of restaurants, food

processing facilities and individuals. In most cases, FOG is disposed by certified grease haulers

either at a municipal wastewater treatment plants (WWTPs), rendering plants, etc. It was

estimated that 13.3 lbs of trap grease is produced per person annually in the US, which translates

into 4.24 billion lbs per year (Wiltsee. 1998).

The occurrence of FOG is the No.1 cause (47%) of the sewer pipe blockage, which are harmful

to the environment, and extremely expensive to remedy (US EPA, 2003; 2007). To date, it is still

mainly landfilled, although alternative uses, such as biodiesel and bio-digesters are also

explored. In this study, the purpose is to investigate the current FOG disposal or utilization

methods, and the market feasibility of WWTP-FOG re-utilization and good practice in FOG

management. The top 100 WWTPs in the US, as well as a few local ones, were contacted for

information collection. 29 WWTP facilities responded and were interviewed, including site visits

at 21 facilities, while the rest were conducted by phone or email (Lu et al. 2017). In addition to

these on-site investigation, some online research also was conducted to expand the survey range.

Among the 29 WWTPs we interviewed in 11 states (Figure 4), 16 WWTPs are located in East

North Central, 3 are located in the New England Region, 3 in the East South Central, 4 in the

Mountain, 2 in the Pacific, and 1 in the Middle Atlantic. With a total capacity at 3766 MGD, 16 facilities are more than 50 MGD. 9 out of these WWTPs are among the top 50 in the US by capacity, including all of the top 4 largest: the Stickney Water Reclamation Plant (Chicago, IL), the Detroit Water and Sewerage Department (Detroit, MI), the Blue Plains WWTP (Washington

DC) and the LA Sanitation (Los Angeles, CA).

The following data was collected in the technical survey (Table 6): facility capacity, FOG program, FOG disposal methods, and acceptance of FOG from haulers (Y/N). FOG disposal cost and other FOG handling details are also included when available.

Table 6. Information Collected during Interviews. Category Information / Questions Current Capacity (MGD) Decision Maker (Project Driver) Facility Overview Influencer User Accept grease from haulers (Y/N) FOG mix with waste water (Y/N) Grease from haulers are separated from scum in plant (Y/N) FOG program (Y/N, details) FOG Source and Handling Disposal Methods Annual Amount of FOG (gal/ton) Annual Landfill Cost Tipping fee (Anaerobic Digestion) Other

Figure 4. Surveyed Wastewater Treatment Plants

2.2 Current FOG Management – FOG Programs

In this survey, all municipalities (29 facilities) have set up FOG programs to manage grease to avoid FOG issues and severe regulatory impacts. As part of the FOG program, all the plants require food producers to install grease traps or grease control equipment for reducing their FOG volume load facilities, 62.6% by capacity (MGD) do not accept FOG from haulers into their facilities. Two representative cases are presented in detail (Lu et al. 2017).

The Detroit wastewater treatment plant (MI) receives wastewater from 78 communities with a current capacity of 660.5 million gallons per day (MGD), which is the second largest WWTP in

US by capacity. The facilities with an obstruction are regulated to install grease traps. The specific discharge limitation on FOG is 1,500 mg/l and the treatment cost surcharge for excess

FOG is $0.459/lbs. Under the inspection and control from the Great Lakes Water Authority,

Detroit WWTP does not permit any release of grease materials into their system resulting in limited quantity of FOG scum and other problems in recent years (Kuplick. 2016).

In Connecticut (CT), on average, there were six sewer pipe blockages every month before 2005

(McCarthy, 2005). The situation has improved by a Food Preparation Establishments (FOG program) that became effective in 2005. CT permit seeks to reduce FOG from entering the public wastewater system through outdoor grease interceptors (1,000 gallons minimum) and automatic grease recovery units (AGRU). The discharge limitation on FOG is 100 mg/l and surcharge is

$4.12/cf. Although the FOG program is efficient in FOG reduction from restaurants, the AGRU has a low efficiency at 10% of FOG capture in New Heaven which also results in issues in the collection system (Findley. 2016).

2.3 Current Disposal/Utilization of FOG

2.3.1 Landfill

Landfill is a conventional way to dispose grease trap waste and it is considered as the most available method to dispose of waste grease (Colorado Department of Public Health and

Environment, 2002). The landfill cost varies from $15/ton to $30/ton in areas where FOG is allowed to landfill. The annual estimate landfill cost for 5 WWTPs are shown in Table 7. from

$42,560 to $517,800. In this survey, 20 of 29 WWTPs landfill the FOG directly which account for 76.2% by capacity (MGD) (Lu et al. 2017). All of them are from Mid-West (East North

Central and East South Central). The representative WWTPs are summarized as follows:

Metropolitan Sewer District of Greater Cincinnati (MSDGC) handles 151 million gallons of waste water per day. By collection both sewer grease and grease from 60 certificated grease haulers, approximately 1.6 million lbs of FOG are collected into the primary settling tank per year. In the primary settling tank, FOG from grease haulers are mixed with the scum grease in the sewer pipe, also with the other wastewater sources such as septic materials. In collection system, the FOG floating on the top of the water is skimmed by a mechanical skimmer and then transported into a separator tank for roughly removing the water (Figure 5). After that, all FOG was delivered to landfill with an annual cost around $208,000 (Scanlan, 2016).

Figure 5. (Left): Trap Grease from MSD GC, OH; Right: Trap Grease from SD1, KY

Sanitation District No. 1 of Northern Kentucky (SD1) operates three facilities. Among them, dry creek is the largest in capacity at 46.5 MGD. Similarly, to MSDGC, SD1 also accepts the grease from 30 certified grease haulers. A report of FOG composition, volume must be submitted to plant before each dumping. SD1 separated the grease from grease haulers and scum into two collection systems. The majority of FOG is in the receiving station for grease from haulers without mixing with waste water and other septic materials. A separation tank is used to remove water content. The FOG is finally transported to the landfill. With a detailed record of tracking

18 grease under their FOG program, 1,310,220 gallons of FOG is collected by SD1 and disposed for landfill (106 times) in 2015 (Crawford, 2016). As shown in Table 7, the annual landfill cost is around $102,540. By separating the FOG by two categories: hauler/render’s grease and scum in waste water, SD1 can offer high quality FOG for potential biodiesel production compared to other WWTPs in local areas.

Table 7. Summary of Facilities with FOG for Landfill Facilities Name City, State Current Accept grease Annual Capacity from Grease Landfill cost (MGD) Haulers (Y/N) $ Stickney Chicago, IL 812 N 517,800 Treatment Plant Detroit Water and Detroit, MI 660.5 N NA Sewerage Department MSD, Cincinnati Cincinnati, 151 Y 208,000 OH SD1, Dry Creek Covington, 46.5 Y 102,540 KY Montgomery Dayton, OH 18 N 42,560 County (OH)

2.3.2 Anaerobic Digestion

FOG is also considered as a feedstock for anaerobic co-digestion with sewage sludge to produce biogas. It is reported that FOG contain high organic content and high methane potential (Long et al. 2012). Although anaerobic digesters have been employed in several WWTPs in US, some operational concerns still exist in co-digestion of FOG with sludge due to the inhibition of methane generation caused by FOG or its derivatives (Long et al. 2012; Luostarinen et al. 2009).

In this survey, 4 of 29 WWTPs (12.29% by capacity) employed FOG anaerobic digestion. Most of them are located in the pacific region where land for landfill is limited (Lu et al. 2017).

The LA Sanitation is the largest WWTP in CA with a current capacity of 325 MGD controlling grease from 10,000 food services in LA. However, the LA Sanitation Plant does not accept the grease from grease haulers and the scum grease is mixed with activated sludge, to digester, which generates combined heat and electricity for the plant (Xu, 2016). The Albuquerque Water

Utility Authority (NM) is piloting a FOG receiving station for digestion by a survey of 11

WWTPs that has digestion system at currently operating (Gupta et al. 2016). Moreover, Gupta reported that the inconsistent FOG availability, variable gas production and high maintenance requirements are the main obstruction in digestion of FOG for WWTPs.

In addition to the surveyed WWTPs mentioned before. Other WWTPs are also investigated by online research projects. Most of them are digesting FOG with small capacity. Some representative ones are summarized as follows:

City of Gresham (OR) operates a 13 MGD WWTP which has a daily FOG generation at 10,000

Gallons. It is reported that Gresham also accepts grease from haulers. The FOG and food waste are anaerobic digested to generate biogas for operating a hot water boiler on site which accounts for 7% of the plants energy production (American Biogas Council). City of West Lafayette (IN) has built a FOG receiving station since 2009. It is mainly focused on re-use the FOG collected from Purdue University for digestion which also increases WWTP’s value as a community asset.

By digesting FOG, the remaining landfill cost has been significantly reduced by 31.5% from

2007 to 2011 (Kenny/Jenks Consultants). The other WWTPs with FOG receiving stations for

20 digestion are all located in California (CA), which include: Central Marin Sanitation Agency

(San Rafael, 7MGD); South Bayside System Authority (Redwood City); City of Millbrae

WWTP. They are all small scale of WWTPs by capacity (less than 10MGD) and they all set

FOG receiving stations followed by LA Sanitation which is a central WWTP in CA

(Kenny/Jenks Consultants).

2.3.3 Incineration

FOG can be mixed with other WWTP residues for incineration. Compared to anaerobic digestion, incineration has the advantages at lower cost and maintenance. However, a moisture removal process is employed before the FOG added into incinerators where high energy input is required. In this study, 5 plants are incinerating FOG taking 11.48% of total flow, 4 of them are in located Northeast (New England and Middle Atlantic).

Greater New Haven Water Pollution Authority (CT) collected the grease from grease haulers separately from the scum grease. With 4000 loads of FOG per year to their bio-solid incinerators

(Findley, 2016), both grease from haulers and scum is incinerated with a heating value at

115,000 BTU/gallon which is close to fuel oil. Similarly, Manchester Water and Sewer

Department (CT) incinerated most of FOG they collected. A system with gravity thickener and centrifuges is applied to remove water content beforehand (Raymond, 2016). However, their

FOG-related pain points are some residue grease which is stuck in lift stations and those grease are delivered to landfill, finally with a cost at $260/ton. Passaic Valley Sewer Commission is the largest WWTP in New Jersey (NJ) with 330 million gallons of (MGD) waste water operated per

21 day. They incinerated the majority of FOG with activated sludge by a wet air oxidation process at 400°C and 800 psi (Urbanski, 2016).

2.4 Result Summary

29 WWTPs were surveyed by site visits or phone/email, out of which 9 are in the top 50 in the

US. The study indicated that the preferred method of FOG handling depends on geographical location. For example, some WWTPs in the Northeast incinerate the FOG as a fuel source or for volume reduction, land-filling is more common in the Midwest, and anaerobic digestion is more practiced on the West coast. 17 WWTPs do not accept grease hauler FOG, which represent

62.6% by capacity. The FOG from these facilities only come from the sewer system, which may have a lower lipid content and a lower quality. Landfill is still the major FOG disposal method, while anaerobic digestion practice is increasing. This also indicates that the FOG processing technology needs to be custom developed based on each WWTP. Besides, a potential market from FOG of WWTP to biodiesel is discovered by reducing current disposal cost significantly and producing environmental-friendly biodiesel (Lu et al. 2017).

Chapter 3. A Green Alternative Titration Method for Determination of the Acid Numbers (ANs) of Fats, Oil, Grease (FOG)

3.1 Objective

In this study, the official acid number (ANs) determination methods are listed and compared.

Then a greener alternative method is proposed which can reduce solvent usage and avoid toxic

chemicals. The method is as reliable as official standards in accuracy and repeatability by testing

a few statistical tests. Furthermore, the applicability of this green method with respect to a wide

ANs (yellow and brown grease) are evaluated.

3.2 Acid Number and FFA %

The acid number is a critical parameter that quantifies the free fatty acid content in an oil sample

or biodiesel. It is defined as the quantity in milligrams of potassium hydroxide, KOH, necessary

to neutralize 1.0 g of FFA containing samples (AOCS Cd 3d-63, Aricetti and Tubino 2012). FFA

content (wt%) is another parameter applied in biodiesel production, which is the percentage of

FFA mass and sample mass. Both AN and FFA % can be analyzed by an acid-base titration

process. There are five official standards to determine the acid number in biodiesel and biodiesel

feedstock (Aricetti and Tubino. 2012). These methods are based on acid-base titration with a

visual or potentiometric indicator. Among these methods, the background solvents such as

isopropyl alcohol, toluene, ethanol are used and with KOH or NaOH solution as reagent (Table

8).

Table 8. Standard Method to Determine Acid Number of Biodiesel and Feedstock. Method Indication Indicator Solvent (ml) Reagent

AOCS Cd pH Phenolphthalein IPA, Toluene 0.1N KOH in IPA 3d-63 (visual) (125ml, 1:1 v/v) AOCS Ca 5a- pH Phenolphthalein Ethanol 0.1N NaOH in Water 40 (visual) EN 14014 pH Phenolphthalein Ethanol KOH in Ethanol (visual) ASTM D664 Potential Potentiometric IPA, Toluene, KOH in IPA Water ASTM D974 pH p-naphtholbenzein IPA, Toluene 0.1N KOH in IPA (visual)

3.2.1 AOCS methods AOCS Cd 3d-63 is widely applied in biodiesel from lab to industry which is a simple visual titration with phenolphthalein as indicator (Kovacs et al. 2012; Aricetti and Tubino. 2012,

Gonzaga and Sobral 2012). While measuring FFA by ACOS Cd 3d-63, a sample with specific mass, varying with its acid number (shown in Table 9), is dissolved in 125 mL of a solvent that is a 1:1 v/v mixture of toluene with isopropyl alcohol (IPA). This background solvent is initially neutralized with B ml KOH solution until the change of a slight pink color (Figure 6). The oil sample is added then followed by A ml KOH solution, drop by drop under vigorous shaking. The indication of end point titration is a stable pink color (persists for 30s) shown in Figure 5. Acid number of oils and fats is calculated through equation 1 as followed and the estimate cost of each titration is around $0.87 in lab scale.

(퐴−퐵)×퐶×56.1 퐴푐푖푑 푛푢푚푏푒푟 = 푚푔퐾푂퐻푔−1 (Eq. 1) 푀 Where:

Where, A: KOH solution needed, ml

B: KOH solution required for blank solvent titration, ml

C: Molarity of KOH solution

M: Oil used, g

Based on the FFA composition in Table 4 and 5. The average fatty acid in FOG can be represented by oleic acid (M = 282.5). Furthermore, the conversion between acid number

FFA % is shown in equation 2.

퐹퐹퐴%∗561.1 퐹퐹퐴%∗561.1 Acid Number = = = 2.003 * FFA% (Eq. 2) 푀푊 표푓 퐹퐹퐴 282.5 Where, 561.1 is MW of KOH (56.11) and a correction (10) to match fraction considering unit and percentage conversion.

Table 9. Oil Sample Size Selection in AOCS Cd 3d-63 Method

Figure 6. A Complete Titration Process in AOCS Cd 3d-63

Compared to Cd 3d-63, an older method, Ca 5a-40 uses ethanol and NaOH instead of

IPA/Toluene and KOH. Without a background solvent neutralization, 5a-40 is not applied as widely as 3d-63 and it can be considered as an alternative method for rough measurements.

(Rukunudin et al. 1998)

3.2.2 ASTM Methods In addition to AOCS, ASTM D 664 is another standard method for measuring the AN based on potentiometric titration in a non-aqueous system. It is considered as a more suitable for colored samples since potentiometric indication can ignore observational error by some literatures.

ASTM D 664 has also been applied even to heavy oils bitumens and bio-oils for acid number measurement. (Wang et al. 2008; Kovacs et al. 2012; Baig et al. 2013). However, the detection limit of ASTM D 664 remains debatable and several operational problems still exists. (Baig et al.

2013; Kovacs et al. 2012).

Kovacs et al. (2012) listed several problems encountered in D664 as follows:

a. Long analysis time especially for FFA samples (yellow/brown grease) due to instability

of signals

b. High fluctuation and bounce (300-500 mv) after drops of reagent. Half an hour is

suggested before reaching stationary

c. Irregular signal pattern occurs after the fluctuation in respect to intensity and direction

d. Complex electrode cleaning

In summary, the potentiometric method (ASTM D664) can ignore observational error but needs a long time (two hours) in electrode cleaning/maintenance and waiting for stable signals. If not, it would reduce the repeatability and accuracy of measurements significantly. Modifications on

D664 are required to be developed in reducing waiting and cleaning time of electrode. (Baig et al.

2013; Kovacs et al. 2012; Figueiredo et al. 2015)

In addition to AOCS and ASTM D664 methods, other standards (ASTM D974 and EN 14014) are used which applied the same principle (visual titration) as AOCS methods only with slight changes on solvent selection and types of indicator.

3.3 Literature Review

According to literature review, only a few literatures have been found in comparison of official standards and modified methods (Table 10). These “new” methods, themselves, are similar to official standards by solving problems of accuracy, electrode cleaning time on potentiometric titration. On the other side changes on solvents and reagents on visual titration have been applied for meeting the green chemistry principles.

Knothe (2006), critically reviewed ASTM and EN methods and pointed out the mediocre reproducibility problem in ASTM D664 due to variability of electrodes. Wang et al. (2008) measured 28 biodiesel samples (B100, B20 and mixture) with ASTM D 664 method then improved the repeatability and accuracy within 5% range. However, their samples with AN range from 0.042-0.463 resulting in being unknown if D664 can be suitable for fats, oils and grease with higher acid number. In addition to AN range, only repeatability test cannot represent the applicability of D644 completely since there is no standard calibration tested.

Gonzaga and Sobral (2012) developed a new method based on potentiometric titration system composed of an electrochemical cell (platinum, silver), and pH electrodes with LiCl. The new method produced similar results as D664 with 8 biodiesel samples (AN: 0.18 to 0.95 mg KOH/g) by ANOVA f-test with p-value at 0.17. Although p value (higher than 0.05) which passed 95%

27 confidence level, a small sample size of 8 is not a desired result for ANOVA test since error impact could make results unreliable.

Baig et al. (2011) reported a modified method on ASTM D974 by reducing reagent concentration, from 0.1N to 0.02N KOH, and solvent dosage, 100 ml to 20 ml IPA/ Toluene. The error can be reduced from 42.88% to 5.82% by applying the new method. And the accuracy is within 3.51% over the biodiesel samples with AN range from 0.313 to 0.525 mg KOH/g. Baig also calibrated the modified method and D974 by standard biodiesel samples with calculated acid number from 0.05 to 0.30. The linear regression shows that both D974 and modified methods have excellent linearity values (R2) with 0.9474, 0.9968 respectively.

Aricetti and Tubino (2012) reported a green method which is similar as AOCS Cd 5a-40. Instead of 0.1N NaOH solution in water, they chose lower concentration at 0.02N NaOH and replace ethanol to an ethanol-water system (1:1 v/v). Paired Student’s t-test and Snedecor’s F test are applied to compare results with Cd 3d-63, a more accurate method in AOCS standards. The new method is extremely green and so simple that can fit samples with AN range within 0.129-0.487 which are high-quality feedstock. However, they don’t mention the problem that solubility of yellow/brown grease (High AN) in ethanol is much lower than IPA/Toluene. After that, Tubino and Aricetti (2013) applied their ethanol-water, NaOH system into potentiometric titration. 12 samples of refined oil (canola, sunflower, corn and soybean) are measured and compared to

AOCS Cd 3d-63 results. No evidence of systematic differences are observed between two methods through a paired student’s t-test at 95% confidence level.

Mahajan et al. (2006) reported the accuracy of ASTM D974 on nine soybean oil standards in the

AN range of 0.198 to 1.17. All accuracies were within 3.3% and repeatability was around 6% at

AN of 0.5. Kovacs et al. (2012) compared three official methods; ASTM D664, EN 14014 and

AOCS Ca 5a-40, by measuring 10 different samples at AN range from 0.3 to 31.8. Standard

Error for all three methods can be controlled within 5.95% (Table 8.) By an ANOVA f-test among three methods, Kovacs proved that there are no significant difference using visual or potentiometric measurement. Observational error of color indication exists, however, were slightly lower than calculated errors using fluctuating data in potentiometric titrations (Kovacs et al. 2012).

Figueiredo et al. (2015), compared potentiometric and voltammetric titration for biodiesel samples. The voltammetric titration is based on reduction of 2-methyl-1,4-naphthoquinone in the presence of acids that can be converted to acid number. Although mean repeatability of ASTM

D664 (18%) is better than voltammetric method (12%). Voltammetric method still presents similar results to ASTM D664 with correlation coefficient at 94% and slope at 0.9928.

Moreover, similarly to Aricetti and Tubino (2012), the ethanol usage in voltammetric titration can decrease the solubility of the yellow/ brown grease in mixture, which results in uncertainty for high AN samples titration. Table 10 summarizes the comparison of new methods in literature with indicator, solvent, reagents, AN range and statistical analysis.

Beyond the literature review in Table 10, some analytical device developers also attempt to develop new reliable methods to address problems in titration. De Voort et al. (2003); Da Silva et al. (2009), and Batista et al. (2016) applied spectroscopic methods to measure ANs of dark and

29 ready-to-emulsify samples. It is reported that potential titration can be problematic in practice: time-consuming, labor-intensive, toxic solvents etc.

In a summary, the problems in current titration methods include the following aspects:

1. Observational error (manual detection) in visual titration methods: AOCS Cd 3d-63, Ca

5a-40, EN 14014 and ASTM D974

2. Toxic organics and high volume of solvents. Method: AOCS Cd 3d-63, Ca 5a-40, ASTM

D664 and 974

3. Complicated electrode cleaning and long reaction time: ASTM D664

4. Accuracy and reproducibility reduction when system maintenance: ASTM D664

Toluene usage in visual method (Cd 3d-63) results in toxicity concerns in titration. It is reported that toluene presents higher toxicity than IPA on human health and metabolism (Ukai et al.

1994). Use of a solvent mixture also posts challenges in solvent recovery, as well as a higher cost. According to Shah and Venkatesan. (1989), single solvent has been applied in FFA extraction from high FFA oils (up to 75%). Besides, some biodiesel producers have used IPA only to reduce toluene exposure (Chai. 2015). However, the ANs measurement range of the oil feedstock is usually limited to yellow grease.

In spite of various modifications on visual and potentiometric titration methods, few studies are focused on high AN samples (brown grease). A former study analyzed the contribution of acids in pyrolysis oil and calibrated the standard bio-oil with titration results from ASTM D664 (Park et al. 2017). Although the maximum AN detection is at 58.75 mgKOH/g, the acid composition of

30 bio-oil (acetic, propionic and vanillic acids) is completely different from acids in FOG (oleic, stearic and palmitic acids). Low cost and accurate AN titration method for brown grease (lower qualitied grease) is in more critical need.

Table 10. Literature Summary of the Acid Number Determination Methods in Biodiesel Production

Solvent Reference Sample AN Range Statistical Reference Indicator (ml) Reagent Method Size mg KOH/g Sample Source Analysis

biodiesel, IPA, AOCS Cd 3d-63, refined Oil, Kovacs et al. Phenolphthalein Toluene, KOH in ASTM D664, oleic acid, 2011 Potentiometric Ethanol IPA, EN 14104 11 0.25-31.80 yellow grease ANOVA test Water, Aricetti and Ethanol 0.02N Tubino. (75ml, 1:1 NaOH in 0.129-0.487 Paired Student’s t- 2012;2013 Phenolphthalein v/v) Water AOCS Cd 3d-63 10, 12 0.091-5.611 biodiesel test, f-test Gonzaga and IPA, Water, 0.01N in ASTM D664 , Sobral 2012 Potentiometric LiCl IPA AOCS Cd 3d-63 8 0.182-0.943 refined oil t-test IPA, Error, Linear Baig et al. Toluene, KOH in 0.013-0.254 Model 2011;2013 Potentiometric Water IPA ASTM D664 6, 42 0.313-0.525 biodiesel, Repeatability Mahajan and IPA, 0.1N KOH palmitic, Repeatability, Konar. 2006 p-naphtholbenzein Toluene in IPA ASTM D974 9 0.198-1.17 soybean oil Accuracy IPA, Wang et al. Toluene, 0.1N in Repeatability, 2008 Potentiometric Water IPA ASTM D664 28 0.042-0.463 biodiesel Error t-test, Linear Figueiredo et 0.1 N pure FFA, Model al. 2015 Voltammetric Ethanol KOH ASTM D664 7 0.1-0.49 Biodiesel Repeatability Batista et al. KOJ in Coefficient of 2016 Spectrophotometric Ethanol Water ASTM D664 9 0.1-0.73 Biodiesel Variation IPA, bio-oil, acetic Park et al. Toluene, 0.1N KOH acid, propionic 2017 Potentiometric Water in IPA ASTM D664 6 15.47-58.75 acid Method Calibration

3.4 Methodology

3.4.1 Proposed green method

The proposed green method is based on AOCS Cd 3d-63, however, replacing IPA/Toluene

solvent to IPA and reducing solvent amount from 125 ml to 50 ml. The reagent (0.1N KOH in

IPA) is been substituted to 0.1N KOH in water to reduce solvent usage. Compared to using

organic solvent as background, the aqueous reaction also increases ionizability of FFA especially

for samples with high acid number (Brown et al. 1998). 0.8 ml of 1% phenolphthalein in IPA is

selected as an indicator that consider the reduction ratio (50/125) of solvent.

The other procedure remains the same as AOCS Cd 3d-63 with a solvent neutralization and

adding KOH solution under vigorous shaking until the time that pink color persists for more than

30 s (Figure 5). The acid number of oils and fats is calculated through equation 1 and each

sample is repeated at least three times to ensure accuracy.

There are 44 different samples of biodiesel and biodiesel feedstock to be prepared and titrated.

The categories include refined oil, waste cooking oil, trap grease, coffee oil after soxhlet

extraction and biodiesel product from our lab. All the solvents and reagents are of analytical

grade with the following chemicals supplied by: Isopropyl Alcohol, 99.5% by Pharmco-Aper

Inc; Toluene, 99.9%; Oleic Acid, 99%; Phenolphthalein 98%; KOH 85.3% by Fisher Scientific

Inc. USA. Table 11 and 12 provide the detailed information for 44 different samples.

Table 11. Summary of Sample Size, AN /Density Range and Types Sample Size 44 Acid Number Range, mg KOH/g 0.133 – 170.369 Density Range, g/ml 0.8507 – 0.9230 Types WCO, FOG, coffee oil, biodiesel, refined soybean oil, mixture etc.

Table 12. Statistical Summary of Samples by Types Types Source Quantity WCO Restaurants form University 13 of Cincinnati. OH FOG Sample 1 * JTM Food Inc, Cincinnati. 13 OH FOG Sample 2 ** Metropolitan Sewer District 11 of Greater Cincinnati (MSDGC, OH) Coffee oil *** After 4h/12h soxhlet 3 extraction of spend coffee grounds Lab scale Biodiesel derived After 2h tranesterifation of 3 from WCO WCO Soybean Oil Kroger Inc. Cincinnati, OH 1

* Heating at 105°C is employed for oil extraction. **Solvent free oil extraction method (Tu et al. 2016) *** Soxhlet extraction (Liu. 2015)

3.4.2 Detection limit of visual titration

As illustrated in Chap 3.2, observational error of color indication is an obvious challenge of all

visual titration methods. However, no literature indicated how to quantify the color indication

error in real titration process. In this study, minimum detection limit of visual titration can be

analyzed by the following hypothesis and calculation:

Due to no significant difference on ionization properties of multiple FFAs, acid number titration

could be simplified as a weak monoprotic acid (FFA, represent by HA) with strong base

(KOH/NaOH). Tubino and Aricetti. (2013) reported a pH curves during potentiometric titration

34 in Figure 7 which is a further evidence that AN titration could be considered as HA + OH- = A-

+ H2O where HA is a weak monoprotic.

Figure 7. Potentiometric Titration Curves Obtained by the Application of (a) point-by-point titration and (b) Metrohm Titrando 808 automatic titrator. (Source: Aricetti and Tubino. 2013)

Rinbom (1963) derived a titration error % equation from a series formula manipulation in proton balance equations of weak acid with strong base. (Equation. 3)

10∆푝퐻−10−∆푝퐻 퐸푡 = × 100% (Eq. 3) √퐶∙퐾푡

Where:

Et: error in titration %,

Δ pH: observational error at end point,

C: normality of HA solution at end point

Kt: Ka/Kw,

Ka: HA dissociation constant;

Kw: water dissociation constant

According to Rinbom (1963), observational error at end point can be up to 0.3 which means

ΔpH ≤ 0.3 it will result Et ≤ 0.2%. So, Equation 3 can be converted as followed as Equation 4:

-8 CKa ≥ 10 (Eq. 4)

Equation 4 is the theoretical criteria that whether HA could be titrated accurately by considering

observational error at end point. For a specific free fatty acid, the detection limit of the HA

concentration can be calculated shown in Table 13. As illustrated in Table 3 and 4, where the

FFA profile, C18:1 is dominant in refined soybean oil and C18: 2 is dominant in WCO and FOG.

The minimum AN detection limit can be estimated as 0.06572 mg KOH/g for soybean oil or

biodiesel and 0.03671 mg KOH/g for WCO or other yellow grease (Table 13). For the samples

with AN less than 0.065 mg KOH/g, titration based potentiometric (ASTM D664) is suggested

because potentiometric titration has lower detection limit than visual titration by eliminating

observational error at titration end.

Table 13. Detection Limit of Visual Titration for Common FFAs. Free Fatty MW PKa* Minimum Concentration Minimum Acid Number Acid (g/gmole) (mole/L) FFA % mg KOH/g C18:0 284.48 4.75 0.00056 0.01778 0.03556 C18:1 282.46 5.02 0.00105 0.03286 0.06572 C16:0 256.42 4.75 0.00056 0.01602 0.03204 C18:2 280.45 4.77 0.00059 0.01835 0.03671 *Pka source: Pubchem database

3.4.3 Statistic Analysis

In this study, the objective is to compare the proposed green method and AOCS Cd 3d-63 of 44

different samples by the following critical statistic parameters or tests:

a. Repeatability

2.77×푆퐷 푅푒푝푒푎푡푎푏푖푙푖푡푦 % = (Eq. 5) 푀푒푎푛×푛

Where:

SD is standard deviation n is the number of operators involved in the analysis =1

b. Weighted linear regression model at 95% confidence level

The purpose of linear regression is to predict the results of green method using AOCS cd 3d-63 data.

c. t-test at 95% confidence level (CI)

In this study, t-test is applied to compare results of both methods in each AN level. The p value can illustrate whether there is no significant difference between two methods at specific AN level.

d. ANOVA f-test at 95% confidence level (CI)

In each measurement, results are impacted by the following factors: method, AN level factor and intercept, which is shown in Equation 6. The 44 samples are randomly selected by representing whole AN level from 0 to 200 mg KOH/g. ANOVA f-test is applied to results in 44 AN levels including yellow grease and brown grease. The p value can indicate whether there is systematic differences between AOCS and green methods at 95% CI.

Measurement = Method + AN level + Intercept + Error (Eq. 6)

Where, method is a fixed effect, AN level is a random effect.

3.4.4 Method Calibration Since there are no standard samples in specific acid number, Wang et al (2008), Baig et al.

(2013) both conducted method calibration with calculated AN of biodiesel and biodiesel blend.

They blend different standard biodiesel samples with specific ANs. Then, the calculated AN of

product is derived by the each ANs and their volume fractions.

However, this method cannot be applied to standard FOG samples. In this study, standard FOG

samples are prepared with the following procedures; pure oleic acid was added into waste

cooking oil (WCO, AN=0.34), with a vigorous stirring. The calculated AN will be defined as

follows:

푀 표푙푒푖푐 푎푐푖푑+퐴 %×푀 푤푐표 퐶푎푙푐푢푙푎푡푒푑 퐴푁 = ( ) × 100 × 2.0003 (Eq. 7) 푀 푤푐표

Where, A% is the FFA % of WCO = 0.17%

With the standard FOG samples, the errors were calculated with following formula:

퐸푥푝푒푟푖푚푒푛푡푎푙 퐴푁 푚푒푎푛−퐶푎푙푐푢푙푎푡푒푑 퐴푁 퐸푟푟표푟 % = ( ) × 100 % (Eq. 8) 퐶푎푙푐푢푙푎푡푒푑 퐴푁

In this study, standard FOG samples with calculated AN that equals to 10, 20, 40, and 60 mg

KOH/g are prepared then titrated by AOCS Cd 3d-63 and the proposed green method,

respectively.

3.5 Results and Discussion

3.5.1 Repeatability

Based on the equations mentioned before, the preliminary results are shown in Table 13. Each

sample is titrated at least three times. This means, SD, RSD% and repeatability were calculated

for both AOCS Cd 3d-63 and green method. In this study, data points are divided in to three

groups: biodiesel, yellow grease (AN<30), and brown grease (AN>30) in Table 14-17.

According to Hamblin et al. (2004), the mean repeatability which is less than 12% is suggested

for testing titration methods.

As it is shown in Table 14, the mean repeatability for overall samples of the proposed green

method is 12.87% which is close to 12%. However, the mean repeatability of AOCS Cd 3d-63 is

14.4% which is even higher. Moreover, results of different sample categories vary significantly

in biodiesel, yellow grease and brown grease. As it is shown in table 15, for all 3 biodiesel

samples (AN<0.5), the mean repeatability for both methods are close to 40% which is much

higher than 12%. In Baig el al. (2011), Mahajan et al. (2006) and Wang et al. (2008)’s

publications, similar situations occur with a high repeatability value and variation when titrating

biodiesel samples (AN<0.5). Among them, Wang et al. (2008) titrated biodiesel samples using

ASTM D664, the potentiometric which can ignore observational error theoretically. However,

Wang’s results shows that the repeatability of B100 and ULSD varies from 5.45% to 27.64%.

Therefore, it can be assumed that both visual and potentiometric titration can result in poor

repeatability in biodiesel or biodiesel blend (AN<0.5).

Table 14. Mean Repeatability and RSD of Two Methods on Overall Samples. AOCS Cd 3d-63 Proposed green method Mean RSD % Mean Repeatability % Mean RSD % Mean Repeatability % 5.213 14.44 4.646 12.87

Table 15. Mean Repeatability and RSD of Two Methods on Biodiesel (AN<0.5). Unit: mg KOH/g AOCS 3d RSD Repeatability Green Method AN RSD (%) Repeatability 3d-63 , AN (%) (%) (%) 1 0.133 ± 10.825 29.986 0.129 ± 0.006 4.562 12.637 0.014 2 0.259 ± 19.868 55.035 0.225 ± 0.074 32.87 91.049 0.051 3 0.268 ± 13.323 36.906 0.268 ± 0.017 6.281 17.398 0.036 Mean % 14.672 40.642 Mean % 14.571 40.361

Compared to biodiesel samples, the yellow grease and brown grease have a stable and reasonable repeatability in both methods. (Table 16, 17). As it is shown in Table 16, for yellow grease samples, the repeatability of AOCS cd 3d-63 and the green method are 12.798 and 12.042 %, that is very close to 12% as suggested by Hamblin et al. (2004). For brown grease, which is shown in Table 17, the mean repeatability of the green method is reduced to 8.575%, whereas,

AOCS’s values maintaining at 11.994%. It can be concluded that both AOCS Cd 3d-63 and the proposed green method can control repeatability within range of 12% for yellow and brown grease in this study.

Table 16. Mean Repeatability and RSD of Two Methods on Yellow Grease (AN<30). AOCS 3d 3d-63 AN, mgKOH/g, RSD (%) Repeatability Green Method AN, RSD Repeatability t value of p value of (Mean ± SD) (%) mgKOH/g Mean ± SD (%) (%) t-test t-test

1 0.566±0.063 11.111 30.778 0.559±0.036 6.374 17.655 0.158 0.883 2 0.781 ± 0.052 6.681 18.507 0.719 ± 0.049 6.799 18.834 1.357 0.248 3 0.925 ± 0.021 2.222 6.156 0.904 ± 0.063 7.005 19.404 0.444 0.696 4 1.378 ± 0.036 2.605 7.215 1.44 ± 0.029 2.035 5.637 -2.121 0.101 5 1.485 ± 0.062 4.167 11.542 1.382 ± 0.127 9.201 25.486 1.066 0.375 6 1.809 ± 0.062 3.448 9.552 1.788 ± 0.059 3.289 9.11 0.378 0.725 7 2.279 ± 0.172 7.529 20.854 2.97 ± 0.243 8.173 22.64 -3.487 0.036 8 3.064 ± 0.131 4.277 11.848 3.075 ± 0.128 4.165 11.537 -0.096 0.929 9 3.631 ± 0.330 9.083 25.161 3.771 ± 0.179 4.736 13.118 -0.61 0.579 10 3.715 ± 0.255 6.863 19.009 3.694 ± 0.161 4.35 12.05 0.11 0.918 11 4.773 ± 0.206 4.309 11.936 5.18 ± 0.253 4.883 13.525 -2.167 0.099 12 5.417 ± 0.083 1.533 4.246 5.321 ± 0.029 0.546 1.512 1.838 0.173 13 6.174 ± 0.024 0.386 1.069 5.913 ± 0.054 0.916 2.536 6.423 0.013 14 7.789 ± 0.371 4.768 13.207 7.377 ± 0.500 6.777 18.772 1.145 0.321 15 9.444 ± 0.327 3.466 9.602 9.066 ± 0.567 6.254 17.324 1 0.387 16 10.576 ± 0.157 1.483 4.109 10.888 ± 0.106 0.973 2.695 -2.652 0.059 17 11.455 ± 0.261 2.279 6.313 9.194 ± 0.141 1.533 4.248 12.511 0.001 18 11.7 ± 0.664 5.674 15.718 10.767 ± 0.567 5.266 14.587 1.85 0.14 19 12.524 ± 0.638 5.094 14.111 11.898 ± 0.365 3.065 8.49 1.393 0.244 20 15.396 ± 0.312 2.024 5.607 15.604 ± 0.206 1.318 3.651 -0.898 0.422 21 15.777 ± 0.404 2.561 7.095 15.592 ± 0.252 1.614 4.47 0.63 0.565 22 17.945 ± 1.180 6.571 18.203 16.245 ± 1.308 8.054 22.308 1.672 0.171 23 21.471 ± 0.646 3.01 8.337 20.814 ± 0.772 3.708 10.272 0.995 0.383 24 25.306 ± 0.776 3.067 8.497 25.615 ± 0.398 1.555 4.307 -0.584 0.596 25 25.808 ± 0.688 2.667 7.387 25.163 ± 0.650 2.584 7.157 1.062 0.349 26 26.512 ± 2.126 8.017 22.208 24.209 ± 0.303 1.252 3.467 1.849 0.198 27 27.671 ± 2.724 9.843 27.266 30.229 ± 3.312 10.956 30.347 -1.033 0.362 Mean % 4.620 12.798 Mean % 4.347 12.042

Table 17. Mean Repeatability and RSD of Two Methods on Brown Grease (AN>30). AOCS 3d 3d-63 AN, mgKOH/g, RSD (%) Repeatability Green Method AN, RSD Repeatability t value of p value of (Mean ± SD) (%) mgKOH/g Mean ± SD (%) (%) t-test t-test

1 36.336 ± 1.019 2.804 7.766 35.623 ± 0.595 1.67 4.626 1.046 0.368 2 42.102 ± 2.290 5.438 15.063 39.831 ± 4.199 10.542 29.202 0.822 0.47 3 51.675 ± 5.476 10.597 29.355 46.829 ± 3.004 6.414 17.768 1.272 0.281 4 61.616 ± 0.418 0.679 1.881 60.301 ± 0.274 0.454 1.256 4.25 0.015 5 76.804 ± 3.016 3.927 10.878 68.91 ± 1.203 1.746 4.836 4.211 0.032 6 76.877 ± 3.913 5.09 14.099 73.751 ± 0.903 1.224 3.39 1.348 0.299 7 79.309 ± 1.619 2.041 5.653 77.366 ± 1.745 2.26 6.259 1.253 0.283 8 105.18 ± 9.058 8.612 23.856 103.361 ± 1.890 1.829 5.065 0.337 0.765 9 112.2 ± 1.805 1.608 4.455 107.345 ± 0.613 0.571 1.582 4.413 0.032 10 112.82 ± 0.977 0.866 2.4 111.114 ± 0.603 0.543 1.504 2.412 0.078 11 134.019 ± 4.668 3.483 9.649 140.617 ± 11.963 8.508 23.566 -0.89 0.448 12 140.434 ± 3.192 2.273 6.295 151.073 ± 6.018 3.984 11.035 -2.294 0.114 13 159.985 ± 6.487 4.055 11.232 152.191 ± 0.902 0.593 1.642 2.061 0.171 14 170.369 ± 15.583 9.147 25.336 166.631 ± 5.003 3.003 8.317 0.387 0.729 Mean % 4.33 11.994 Mean % 3.096 8.575

3.5.2 Linearity between green and AOCS Cd 3d-63 method

In this study, the purpose of linear regression model is to predict green method’s results using

AOCS results and comparing them. As it’s shown in Figure 8, the linearity curve for overall

samples (44) demonstrates an excellent correlation coefficient (R2 = 0.9963). The linear

regression equation is y = 0.9917x – 0.3923, where the intercept can be ignored. It can be

concluded that results of the green method are 1% approximately lower than that of AOCS Cd

3d-63. Similarly, when the linear regression model is applied to yellow grease and brown grease,

the correlation coefficient is 0.9906 and 0.9878 as shown in Figure 9 and 10.

Overall Samples 180

160 y = 0.9917x - 0.3923 R² = 0.9963 140

120

100

Acid Number, Proposed Green method Green Proposed Number, Acid 0 20 40 60 80 100 120 140 160 180 Acid Number, AOCS Cd 3d-63

Figure 8. Green method AN vs AOCS Cd 3d-63 AN for Overall Samples. (Unit mg KOH/g)

Yellow Grease (AN<30) 40

y = 0.9852x - 0.0653 30 R² = 0.9906

Acid Number, Proposed Green method Green Proposed Number, Acid 0 0 10 20 30 Acid Number, AOCS Cd 3d-63

Figure 9. Green method AN vs AOCS Cd 3d-63 AN for Yellow Grease. (Unit mg KOH/g)

Brown Grease (AN>30) 180

160 y = 1.0214x - 3.8475 R² = 0.9878 140

120

100

20 Acid Number, Proposed Green method Green Proposed Number, Acid 0 0 20 40 60 80 100 120 140 160 180 Acid Number, AOCS Cd 3d-63 Method

Figure 10. Green method AN vs AOCS Cd 3d-63 AN for Brown Grease. (Unit mg KOH/g)

Based on Figure 8, 9 and 10, the green method can be applied to predict the results of AOCS Cd

3d-63 with excellent correlation coefficient (R2) demonstrating the reliability of the green

method within a wide AN range from 0.1 to 170 mg KOH/g. Compared to yellow grease, the

intercept of brown grease is 3.8475 which cannot be neglected. The reason of the high intercept

can be related to higher repeatability which is caused by dark color of oil samples (Table 17).

However, by a reduction of solvent and sample mass of the green method, color change is

improved significantly compared to AOCS. Color change improvements resulted in lower

repeatability of the green method than that of AOCS Cd 3d-63 (Table 17). For AN measurements

of brown grease, an equation of y = 1.0214x - 3.8475 can be suggested for results conversion in

both methods, where y is AN of green method and x is AN of AOCS Cd 3d-63.

3.5.3 Results of ANOVA f-test and t-test.

As indicated in chap. 3.4, ANOVA f-test is applied to overall results in 44 AN levels. The results

of ANOVA f-test are listed in Table 18. The p-value of method for overall samples is 0.069,

higher than 0.05, which revealed no evidence for systematic differences between two methods at

the 95% confidential level. Furthermore, the p value of major sample categories: yellow grease

and brown grease, also indicated no significant difference with p value at 0.1 and 0.3031 was

observed in both methods.

In this study, t-test is focused on testing results of both methods in each AN level. The t-test

results (Table 15) indicates that all of three biodiesel samples with p value higher than 0.05 pass

the t-test where there are no systematic differences at 95% confidential level. 24 of 27 (88.9%)

yellow grease samples and 11 of 14 (78.6%) brown grease samples have a p value over 0.05.

Due to the uncertainty of t-test in a small sample size, the ratios are acceptable. Although t-test

on overall data ignores the factor behind data such as AN level, p-value is 0.906 that confirm

Tubino and Aricetti (2013)’s t-test results.

Table 18. ANOVA f-test Results for Yellow Grease, Brown Grease and Overall Samples t value p value

Yellow Grease Intercept 5.743 2.96E-06

Method -1.657 0.100

Brown Grease Intercept 2.935 0.0154

Method -1.033 0.3031

Overall Intercept 5.202 5.04E-06

Method -1.831 0.069

3.5.4 Results of method calibration

The linearity curves relating to AN experimental results (green and Cd 3d-63) to the calculated

AN of FOG samples were obtained as shown in Fig. 11. The correlation coefficient R2 for AOCS

Cd 3d-63 and the green method were 0.9988 and 0.9998, that demonstrated excellent linearity in

calibration of both methods. However, the linear equations indicated that both methods could

have overestimated measurements than calculated results. For AOCS Cd 3d-63, a ratio of 2.55%

overestimate is observed and the green method’s is 1.61%. It is caused by observational error at

titration end which is the systematic error of all visual titration methods. As it is shown Table 19,

error of AOCS 3d-63 can be controlled within 9.84%, mean error is 4.93%. Furthermore, error of

the proposed green method is less than 5.59% with a mean error at 2.93%. In this study, the

green method even shows better performance in error control than AOCS Cd 3d-63. According

to the results of other literature, (Baig et al. 2013, Kovacs et al. 2012, Wang et el, 2008), an error

within 10% is widely accepted on the calibration of titration.

AOCS Cd 3d-63 Proposed Green Method

80 80 y = 1.0255x 70 70 y = 1.0161x R² = 0.9988 R² = 0.9998 60 60 50 50 40 40 30 30 20 20

10 10

Acid Number, Measurement Measurement Number, Acid Acid Number, Measurement, Measurement, Number, Acid 0 0 0 10 20 30 40 50 60 70 80 90 0 10 20 30 40 50 60 70 80 90 Acid Number, Calculated Acid Number, Calculated

Figure 11. Experimental AN (AOCS Cd 3d-63 and proposed green method) versus Calculated AN for Biodiesel Feedstock (unit: mg KOH/g)

Table 19. Results of AOCS 3d -63 and green method calibration FFA %, 5 10 20 30 40 Calculated AN, Calculated 10.02 20.03 40.06 60.09 80.12 Oleic Acid Added 2.54 5.46 12.40 21.31 33.20 (g) AOCS Cd 3d-63 10.56 ± 0.27 21.97 ± 0.46 42.20 ± 0.68 60.90 ± 0.86 81.76 ±1.36 AN, (Mean ± SD) Green Method 10.56 ± 0.23 20.62 ± 0.46 41.24 ± 1.28 60.83 ± 1.11 81.21 ± 1.76 AN, (Mean ± SD) AOCS Cd 3d-63 5.59 9.84 5.51 1.49 2.20 Method Error % Green Method 5.59 3.08 3.11 1.38 1.51 Error %

3.5.5 Density versus Acid Number

According to Eq.2, mass of sample should be measured accurately in each titration process.

However, since most of samples are oil based, with high viscosity especially in heavy oils, mass

loss can not be ignored during sample transferring to titration system. Based on Tubino and

Aricetti (2013), pipette is widely accepted in real lab practice to decrease loss of sample transfer.

In that case, the density of oil samples should be measured to determine sample mass. In this study, density of 44 different samples (Table 11) are measured accurately with a Sartorius

BALANCE (Model: CP225D) and 20 ml volumetric flask. The purpose is try to simply the density term in Equation 2 and find relations between density and AN. Each measurement is repeated at least three times for ensure precision.

According to the average molecular weight shown in Table 5. Due to significant variations on

FFA profiles from WCO to trap grease. MW of trap grease is much lower than WCO or soybean oil. The mean values of WCO and trap grease are 272.9g/gmole, and 283g/gmole, respectively. It can be assumed that high AN samples could have lower density than low AN ones.

The density results are shown in Fig. 12. Among the 44 samples, density values vary from

0.8507 to 0.9230 g/ml. The minimum density (0.8507 g/ml) is the sample with highest AN (170 mg KOH/g). A general trend that density decrease when AN increases is observed. Density of majority of samples with AN >21 is lower than 0.89 g/ml. By categories, mean density of yellow grease and brown grease are 0.89g/ml and 0.88g/ml. These values can be applied into Eq.2 to simply sample density term.

Figure 12. Density vs Acid number for 44 samples

3.6 Solvent Recovery in titration waste

3.6.1 Introduction

Solvent recovery is a routine practice in the chemical and pharmaceutical industries due to the

economic and environmental benefits (Renge et al. 2013). In biodiesel production, each AN

titration can generate 127 ml of waste solvent, approximately, using AOCS Cd 3d-63. Although

the proposed green method reduced it to 52 ml, large amount of waste solvents were generated in

both lab and industrial settings due to many titrations needed. In order to reduce disposal cost

($8/lbs in University of Cincinnati), recycling waste titration solvent of AOCS and green

methods were studied and some preliminary results were obtained to optimize recovery

conditions.

The waste solvent mixture contains organic solvents (IPA/Toluene), remaining oil,

phenolphthalein and water etc. The composition of titration waste of 4L can be estimated as in

Table 20.

Table 20. Composition of Titration Waste with AOCS Cd 3d-63 and Green Methods. AOCS Cd 3d-63, wt% Proposed Green Method, wt% IPA 49.18 71.43 Toluene 46.24 - Water - 19.14 Oil* 4.23 9.08 phenolphthalein 0.05 0.15 Soap 0.08 0.18 *Assuming average oil sample is 5g at 10 mg KOH/g

Lin and Wang et al. (2004) recycled the IPA up to 93% from semiconductor plant waste with

IPA concentration at 65% in an integrated method. It includes the process of air stripping and

condensation with activated carbon filter. Ooms et al. (2014) studied the recovery IPA from an

IPA/Water mixture, by a heterozeotropic distillation system with 99.96% of IPA that could be

obtained. Renge et al. (2009) applied a complex azeotropic distillation process to separate

methanol and toluene from pharmaceutical waste. It is reported that recycling methanol (95%) is

more efficient than recycling toluene (84%). Murthy and Shah (2012) separated the IPA-

toluene mixture by using a poly membrane. A recovery rate of 78% of toluene can be obtained at

12 psi, 40°C. Although, those techniques show high recovery percentage especially in

heterozeotropic/ azeotropic distillation, as those methods are in high cost which makes them not

widely used in biodiesel production.

3.6.2 Results Summary

In this study, a simple roto-evaporation process with a vacuum is applied to waste solvents of

AOCS Cd 3d-63 and the green method. The purpose is to compare the recovery percentage for

50 both titration methods and optimize best condition (T, P) for solvent recycling. The product solvents are used to re-titrate specific samples to analyze the feasibility of solvent reuse.

The boiling point of IPA and Toluene 82.5°C and 110.6°C, respectively, at 1 atm. However, they would be decreased significantly at vacuum condition. Therefore, the vacuum pump was applied in this study to increase the solvent recycle efficiency at low heating temperature. The experiment setup is shown in Figure 13. with a BUCHI Rotavapor II, KINNEY vacuum Pump

(Model: KVC) and a commercial vacuum gauge (0-30 Hg). In each recovery, solvent temperature was set as 80°C which is close to boiling point, with three vacuum conditions: 12,

20 and 28 in Hg. The recovery rate (%) and loss rate (%) can be obtained by equation 9 and 10.

푆표푙푣푒푛푡 푅푒푐푦푐푙푒푑,푔 푅푒푐표푣푒푟푦 푅푎푡푒 % = × 100% Eq. 9 푊푎푠푡푒 푆표푙푣푒푛푡,푔

푊푎푠푡푒 푆표푙푣푒푛푡,푔−푆표푙푣푒푛푡 푅푒푐푦푐푙푒푑,푔−푂푖푙,퐾푂퐻,푔 퐿표푠푠 푅푎푡푒 % = × 100% Eq. 10 푊푎푠푡푒 푆표푙푣푒푛푡,푔

Figure 13. Experimental Setup for Solvent Recovery

After an hour of the evaporation and condensation process, the recovered solvent was clear, however, residue liquid showed two layers: oil and KOH/Indicator in Figure 14. It can be assumed that only solvent (IPA or IPA/Toluene) and a small amount water are condensed after evaporation that can be separated from the remaining oil, KOH and phenolphthalein residues.

Results of recovery rate under 80°C, vacuum at 15, 20 and 28 in Hg are shown in Figure 15. It can be observed that the recovery rate of IPA is 5% approximately, higher than the IPA/Toluene system because the boiling point of toluene (110.6°C) is much higher than IPA which causes in- complete condensation of IPA- toluene system. The highest recovery rate is 94.36% at 28 in Hg vacuum.

Figure 14. (a) Titration Waste; (b) Residue Liquid Layer after Solvent Recovery

Figure 15. Recovery Rate versus Vacuum at 80°C on Titration Waste

3.7 Conclusion

The new proposed green method has been developed on determination of acid number for biodiesel and FOG, especially for low-quality feedstock. It eliminated the usage of toluene which is a common solvent in AOCS and ASTM standards. Meanwhile, reduction in solvents (from

125ml to 50ml), sample size, and reagent (from KOH in IPA to KOH-water) are being proposed for green chemistry. The new method reduces the titration cost from $0.87 to $0.28 per time in lab. Also, the solvent recycle efficiency of IPA is 5% higher than IPA/Toluene. The minimum detection limit of all visual titration methods (Proposed green, AOCS Cd 3d-63, ASTM D974,

EN 14014) is 0.0658 mg KOH/g. For samples with AN lower than 0.065, potentiometric titration method (ASTM D664 or modified) is suggested for accurate measurement.

Compared to AOCS Cd 3d-63 method, the green method furnished equivalent results by linearity

(R2=0.9963, slope=0.9917) with a wide AN range from 0.133 to 170.369 mg KOH/g. No systematic difference between the two methods are observed in each category (biodiesel, yellow grease, brown grease) through ANOVA f-test and t-test. The green method maintains good repeatability at 8.575% which is even better than AOCS Cd 3d-63 at 11.994%. By calibration of two methods, correlation coefficient R2 for AOCS Cd 3d-63 and green method were 0.9988 and

0.9998, that demonstrated excellent linearity. Error of AOCS Cd 3d-63 can be controlled within

9.84%, whereas, the green method errors are more stable within range of 5.59%. That is because that KOH- water system can provide an aqueous reaction which could increase ionizability of

FFA in high AN samples. The major limitation for the application of AOCS Cd 3d-63 is slight color change at titration end point for dark-colored samples. However, it could be solved by

53 sample mass reduction for dark oils. By hundreds of titrations, the slight color change which result in significant error in titration end is not observed in this study.

The mean density of yellow grease and brown grease are reported as 0.89g/ml and 0.88g/ml.

Furthermore, the green method with single IPA can increase the solvent recycle efficiency by 5% compared to AOCS cd 3d-63 method (IPA/Toluene)

Considering the above observations, the green method can provide accurate and reliable results to determine ANs of FOG. By reducing solvent dosage and eliminating the use of the more toxic toluene, it can be recommended as a routine titration method in R&D as well as in biodiesel plants for both yellow and brown grease.

Chapter 4. Sulfur Analysis of Fats, Oils and Grease (FOG)

4.1 Introduction

High sulfur content in fuel can result in negative impacts on human health and air quality by

generating more sulfur dioxide, which is one of the air pollution criteria in National Ambient Air

Quality Standards (NAAQs, 40 CFR part 50). Besides the air pollution impact, fuels with high

sulfur levels may increase engine wear and reduce the efficiency and the life-span of oxidation

catalytic converters (Johnson. 2011; Kim et al. 2011).

Compared to petroleum diesel, biodiesel has a highlighted advantage of being sulfur-free. Based

on the biodiesel standard ASTM D 6751 (S15), it specifies a maximum limit of 15ppm of sulfur

content in biodiesel. Since FOG is booming as a biodiesel feedstock, which has been shown in

Chap 1 and 2, sulfur content in FOG is attracting more attention in recent years (Alcantara et al.

2000; Fernando et al. 2007; Demirbas. 2009; Kim et al. 2011). Besides, sulfur also can be

introduced into biodiesel by acid-esterification pretreatment which is a necessary process for

handling high FFA feedstock (Chai et al. 2014; Kim et al. 2011). The catalytic reaction will also

result in uncertainty of sulfur content in biodiesel and increase the difficulty of biodiesel quality

control. For those WWTPs with FOG digestion/incineration processes, sulfur content is also a

major concern for determining their FOG utilization (Findley, 2016; Urbanski, 2016).

4.2 Literature Review and Objective

Based on the literature, the complex sulfur forms in FOG include alkyl sulfates, sulfonates, reduced sulfur compounds, for example, hydrogen sulfide, mercaptans etc. (Correa and Arbilla

2008; Preprocess Inc, He et al. 2009; Kim et al. 2011; Ward et al. 2012). They can be complex mercaptans molecules with aromatic ring backbones or with straight chain backbones. These mercaptans are not soluble in water, but is soluble in methanol. The boiling points vary from

5.95°C to 170°C. The complexity of sulfur forms in FOG is determined by many factors, such as microbial degradation and sulfur compounds added to the FOG during cleaning or cooking processes. It is necessary to quantify the sulfur content of FOG in biodiesel production targeting the product below 15ppm sulfur.

Keener et al. (2008) reported the sulfur and metal content in 27 FOG samples from 23 different locations of in the US. The FOG sources are mainly derived from food-processing facilities, food service establishments and residential sources where significant cooking occurred. Based on those representative FOG samples, the sulfur content varies from 62.3 ppm to 1,750 ppm with a mean at 447 ppm. These results show a high variation of sulfur content (SD=363 ppm) in different FOG samples. However, the sulfur analysis method is not reported.

Kim et al. (2011) pointed out the high sulfur concentration of 640 ppm in brown grease and developed Zn/ZnO2 catalysts for esterification pretreatment of FOG. The sulfur level is decreased significantly to 303 ppm which meets the ASTM D 6751-S500 sulfur specification

(<500ppm), however, not meeting S15 standard. The sulfur analysis is conducted by the ASTM

D4951 method.

Demirbas. (2009) analyzed the sulfur content in WCO and biodiesel from WCO. The results are

900 ppm and 600 ppm, respectively, which exceed the ASTM D6751 S500 standard. However, details of sulfur analysis method are still not reported in this paper. However, it is reported that

FOG from WWTPs contain 200-400 ppm of sulfur (Ragauskas et al. 2013; Megan et al. 2016;

He et al. 2011) He et al. (2009) indicated that most sulfur content from WCO has been transferred to the glycerine phase during transesterfication, which also contributes to sulfur reduction in the biodiesel, which is desirable result. And also, it is reported that biodiesel made from FOG alone can not meet the 15ppm sulfur standard (SFWPS. 2011; Cairncross et al. 2016;

Chakrabarti et al. 2008).

In this study, the purpose is to conduct a sulfur analysis process for three sources of FOG from food processing facilities and wastewater treatment plants (WWTPs). These results can be applied to investigate the sulfur quality of FOG feedstock in biodiesel production or incineration.

The series of processes include: solvent-free oil extraction process, acid-esterification and transesterification (Chai et al. 2014; Tu et al. 2016; Agnew et al. 2009), FOG will be converted to biodiesel for sulfur analysis to meet the ASTM D6751 (S15) standard. Furthermore, the sulfur flow from FOG to biodiesel will be reported for various sources of FOG.

4.3 Sulfur Analysis Standards

a. ASTM D 5453 Method:

D5453 determines sulfur content by UV fluorescence. The sample is combusted in a quartz combustion tube, resulting in the production of SO2 which is then converted to excited SO2. The fluorescence is detected by a photomultiplier tube to measure sulfur concentration. Sample size

57 at 20ml or above is suggested by D5453 for accuracy (Gerpen.et al. 2004; ASTM D5453). In this study, D5453 is used to quantify sulfur in biodiesel samples.

b. ASTM D4951 Method:

D4951 is based on inductively coupled plasma (ICP) optical emission spectrometry with a wavelength of 180 nm. A sample is diluted (1% to 5% by mass of oil in solvent) with mixed xylenes or kerosene. The solutions are run through the ICP. By comparing emission intensities of the sample to those of the standards, concentrations of the elements present are determined. In addition to sulfur, metal composition such as calcium, copper, magnesium, and zinc can be measured by D4951 (Young et al. 2011; Amais et al. 2014; ASTM D4951).

c. ASTM D4239 Method:

D4239 is based on a high temperature combustion method with acid-base titration detection procedures for coal and coke. A weighted sample in the combustion boat is burned at 1350°C with oxygen. The product will be absorbed in an iodine solution. With a polarized platinum electrode, sulfur dioxide is titrated by iodine titrant. The volume of titrant expended is used to calculate sulfur content in samples (Oasmaa et al. 2003). In this study, D4239 is applied to determine sulfur in raw FOG, oil extracted and remaining solids.

Table 21. Sulfur Analysis Standards Method Instrument/Principle Detection Sample Sample Type Size

ASTM D5453 UV fluorescence Excited 20 ml Light Hydrocarbons,

SO2 Motor Fuels, Biodiesel

ASTM D4951 Inductively coupled Emission 1%, 5% Lubricating Oils plasma (ICP) Intensities dilution in xylenes

ASTM D4239 Elemental, Titration - Coal, Coke,

Combustion of SO2 FOG solids

In addition to those standards mentioned, the alternative method ISO 20884 in EN 14214 uses

wavelength-disperse X-Ray Florescence spectrometry (XRF) to determine sulfur content

(Knothe. 2006; Barker et al. 2008).

4.4 Materials

The waste cooking oil was collected from the restaurants in the University of Cincinnati (FFA<

0.5%). FOG sample 1 was from the FOG receiving station of MSDGC. FOG sample 2 was from

SD1 in Northern Kentucky. FOG sample 3 was obtained from JTM Food Group. The raw trap

grease was taken from the supernatant layer of the grease trap of JTM’s food processing facility.

Sulfuric acid (HPLC grade, 99.8%, Pharmco-Aaper), methanol (HPLC grade, 99.9%, Pharmco-

Aaper), toluene (HPLC grade, 99.8%, Tedia) and isopropyl alcohol (HPLC grade, 99.8%,

Pharmco-Aaper), KOH pellet, glycerin (ACS grade), and phenolphthalein were purchased from

Fisher Scientific USA (www.fisherci.com).

4.5 Methodology

4.5.1 Sample Source

In this study, three types of FOG samples are prepared by two pathways which is shown in Table

22.

The first pathway was producing biodiesel from sewer grease obtained from MSD. Waste grease

extraction (WGE) technology was used as the oil extraction process which is in accordance to

59 the former study (Tu et al. 2016). This process includes a solvent-free lipid extraction by using waste cooking oil as an oil acceptor and blend it with waste grease at specific temperature.

(Chapter 4.2.2) After extraction, a filtration process is employed to separate the resulting oil

(WGE oil) and remaining solids (WGE solids). WGE oil underwent an acidic esterification process for reducing the FFA and then converted into biodiesel through alkaline transesterification (Chai et al. 2014). The crude biodiesel will be separated from the glycerin layer by settling and purified by water washing. Sulfur content in WGE oil and WGE solids will be analyzed with ASTM D4239.

The second pathway was producing biodiesel from FOG obtained from the JTM Food Group

(OH). The JTM oil and JTM solids are separated through a heating & filtration method (H&F).

The technical processes after the separation (acid-esterification, alkaline transesterification) was similar to those of the first pathway. The sulfur content in JTM oil and JTM solids are analyzed by ASTM D4239.

As illustrated in Chapter 2, SD1 grease can be considered as a grease totally from haulers. The oil quality and lipid fraction is in between MSD and JTM. In this study, both of the two pathways can be used to SD1 grease targeting oil separation. The oil product will be converted to biodiesel by acid-esterification and alkaline- transesterification processes which are shown in

Table 22. The sulfur content in biodiesel product will be analyzed in Iowa Central Fuel Testing

Laboratory by ASTM D5453 standard.

Table 22. Pathways for three types of FOG Source Process Product FOG Sulfur Analysis Biodiesel sulfur Standard standard JTM Raw Heating& JTM Oil Grease Filtration JTM Solids SD1 Raw Heating& SD1 Oil Grease Filtration ASTM 4239* ASTM 5453** WGE SD1 solids MSD Raw WGE MSD Oil Grease MSD Solids * conducted by OKI lab (Cincinnati, OH) ** conducted by Iowa Central Lab (Fort Dodge, IA)

4.5.2 Sample Preparation

Waste Grease Extraction by WCO (WGE)

200 g MSD raw sewer grease was used for each experiment. According to the former study (Tu et al. 2016), the WCO-to-FOG ratio was selected as 3.2, 3.6 (wt/wt) which is the optimum condition for MSD grease. The temperature and time for the extraction were 70 ºC and 4 hrs, respectively. The WCO was pre-heated to 70 ºC before the sewer grease was added. In addition, sewer grease and WCO mixture were stirred for 15 seconds and the same stirring procedure was performed once every hour afterwards. A 1,000 ml flask was used as the reactor and a thermocouple was inserted into the flask to monitor the temperature. The flask was placed on a hotplate. At the end of the extraction, a liquid sample (3ml) was withdrawn and a titration was performed to determine the FFA by using green titration method illustrated in chapter 3. For

SD1 grease, WCO-to-sewer grease ratio was selected as 3.2, 3.6, 4, 4.5:1 to ensure the product oil is a yellow grease. The other procedure remains the same as MSD FOG.

Heating & filtration (H&F)

4,000 g raw grease from the JTM Food Group were used for each experiment. The raw trap grease was heated at 105°C to remove water. After a 12-hour heating, the oil was separated from the remaining solids by filtration.

Acidic esterification

A 1,000 ml flask with a hotplate were selected as the reactor for the experiment. Besides, the reactor also contained a stirring system with agitation speed at 600 rpm. The purpose of stirring is to ensure enough mass and heat transfer rate for the reaction. A thermometer was inserted into the flask to monitor the temperature. The dose of methanol and catalyst (H2SO4) was determined by the regression equation in the former study (Chai et al. 2014). The designated amount of methanol and H2SO4 were blended in a flask and heated to 65 ºC. The heated oil was being mixed with the methanol/H2SO4 solution in the reactor for 2 hrs. At the end of the extraction, 3 ml liquid sample were withdrawn and titrated to determine the FFA by the green titration method. Afterwards, the oil layer was separated from methanol layer by a separation funnel. And the oil extracted was then roto-evaporated at 70 ºC and 28 in Hg for methanol removing. The oil layer also needs to be filtered if needed.

Alkaline transesterification

The alkaline transesterification was performed by following the procedure in Agnew et al. 2009.

The molar ratio between methanol and oil was 5:1 and the dose of NaOH was 3.5 g/L oil. The designated amount of methanol and NaOH were mixed and heated to 65 ºC before addition to the heated oil. The mixture was being stirred in a 1,000 ml flask for 2 hrs. After reaction, the mixture

62 was placed in a separation funnel and settled until a visible layer of glycerin appeared at the bottom. The glycerin was drained from the bottom of the funnel and the crude biodiesel was purified by several batches of water washing. After washing processes, biodiesel was being roto- evaporated at 70 ºC and 28 inch Hg for methanol/water removing.

Methanol removal process

As the major sulfur content in FOG, mercaptans and others are soluble in methanol, which is a reactant in both esterification and transesterification. In this study, all procedures with methanol involved must have a methanol removal process afterwards. It includes the oil layer separation after esterification and biodiesel product after transesterification (Figure 16). Therefore, careful methanol removal is essential to ensure that the sulfur compounds dissolved in methanol are separated from biodiesel.

Figure 16. Flow Chart of the Experimental Process (Specific for methanol removal)

4.6 Results and Discussion

4.6.1 FOG properties Overview

As shown in Table 23, among three types of FOG, oil content in JTM is the highest (32.68%).

MSD FOG contain 30.58% solids, which is higher than the other two. For the oil quality

(FFA%), JTM can produce a yellow grease product oil (13.36%FFA), however, FFA% of SD1 is much higher at 55.24% FFA by heating & filtration. By WGE technology, FFA% in MSD and

SD1 can be reduced lower than 15% as a yellow grease, with lower FFA in MSD oils.

It is reported that there are seasonal impacts on FOG quality with high variation in oil content and oil properties by different seasons (Kobayashi et al. 2017). In this study, it is obvious that high variation exists in FOG, however, it is unknow if this is caused by seasonal impacts.

Table 23. Overview of FOG Properties JTM SD1 (Winter) MSD (Winter) (Summer) Moisture, wt% 53.81 39.62 50.88 Oil, wt% 23.35 29.8 32.68 Solid, wt% 22.34 30.58 16.44 55.24 (H&F) 9.6. (WGE, Ratio = 3.2) 13.36 FFA% of Oil 12.12 (WGE, ratio= 4.5) 9.1 (WGE, ratio = 3.6) (H&F)

The sulfur content in FOG, solids and oils are reported in Table 24. Sulfur content in raw FOG vary from 500 to 800 ppm. Also, it is illustrated that sulfur concentration in oils are lower than raw FOG. It is caused by the evaporation of low boiling point mercaptans since heating process exists in both H&F and WGE. Among the three types of FOG, SD1 grease has the highest sulfur content at 800 ppm. However, JTM and MSD oils are much lower (300ppm sulfur).

Table 24. Sulfur Content in Three Types of FOG, unit: ppm Raw Grease Solid Oil JTM (Summer) 700 1000 300 MSD (Winter) 500 600 300 (WGE) 600 500 SD1 (Winter) 800 800 (H&F) (WGE)

4.6.2 Sulfur flow from FOG to Biodiesel

JTM Grease

JTM grease is originated from the JTM Food Group, a food processor. It contains the highest oil content at 32.68% among three sources. By heating and filtration (H&F), oil can be separated from solids and the product oil is at 13.36% FFA, which is in yellow grease category. The sulfur flow form JTM oil to JTM Biodiesel (B100) is shown in Figure 17. Heating and filtration (H&F) can reduce the sulfur concentration by 57.1% from 700 ppm in the grease to 300 ppm in the oils.

In biodiesel reactions (esterification, transesterification), sulfur concentration is reduced by

95.2% to 14.4 ppm which accounts for 4.61% of total sulfur. Although sulfuric acid is added to the mixture during esterification, the sulfur content is still reduced by washing and solvent evaporation. 95.4% of total sulfur existed in by-product phases such as remaining menthol, glycerol and washing water. In this study, JTM biodiesel (B100) is qualified with ASTM D6751 meeting the 15 ppm sulfur standard (Figure 17).

*(#): Mass Distribution of Total Sulfur (wt%)

Figure 17. Sulfur Flow from JTM Grease to Biodiesel

MSD Grease

MSD grease was collected from MSD Cincinnati WWTP. As shown in chapter 3.3.1, MSD grease can be considered as a mixture of WWTP scums and grease from haulers. Among these three sources of FOG, MSD has the lowest oil content at 23.35%, and a process of heating & filtration cannot separate oil and solids. According to Tu et al. 2016, the waste grease extraction process (WGE) is applied in this study with the optimum condition (70C, 4 hours, WCO-FOG ratio = 3.2, 3.6). The yellow grease product is at 9.1 - 9.6% FFA and finally can be converted to biodiesel.

As it is shown in Figure 18, sulfur concentration is reduced by 40% after the WGE process.

WCO-FOG ratio has a minor impact of sulfur reduction in this process. However, for biodiesel reactions, WCO-FOG ratio of 3.6 can reduce sulfur concentration by 96.9% which is higher than the ratio of 3.2 by 94.03%. Furthermore, biodiesel (B100) from ratio of 3.6 contains 9.1 ppm sulfur which meets the ASTM D6751 standards. In this study, a higher ratio (≥3.6) is suggested for biodiesel production from MSD grease.

*(#): Mass Distribution of Total Sulfur (wt%) Figure 18. Sulfur Flow from MSD Grease to Biodiesel

SD1 Grease

As explained in chapter 3.3, SD1 grease is mainly from certificated grease haulers. It can be considered as a “mediocre” grease at the oil content compared to MSD and JTM. By heating & filtration process, oil content can be separated from FOG. However, the product oil is at 55.24%

FFA which is brown grease with high viscosity. According to Tu et al. 2016, WGE process is applied to SD1 grease to optimize conditions for extraction of the perspective of both FFA and sulfur content. Furthermore, the impact of WCO-SD ratio was also studied. Figure 18 shows the oil FFA progress under 70℃ extractions with WCO-FOG ratio from 3.2 to 4.5. The FFA of oil starts to increase by time and remains stable from 4 to 6 hours. It indicates that the extraction

67 process ends after 4 hour which is in is consistent with Tu et al. 2016’s conclusion. After extraction, only WCO-FOG at 4.5 can produce a yellow grease product (FFA<15%).

25 WCO-FOG ratio: 3.2 WCO-FOG ratio: 3.6 WCO-FOG ratio: 4 20 WCO-FOG ratio: 4.5

Yellow grease 15

FFA, % 10

0 0 30 60 90 120 150 180 210 240 270 300 330 360 390 Time, min Figure 19. FFA vs Time of SD1 Grease

The sulfur content with H&F and WGE (ratio=3.2, 4.5) is shown in Figure 20. Similarly to MSD and JTM, highest sulfur reduction consists in esterification and transesterification. It is illustrated that WGE can have higher sulfur reduction compared to heating & filtration. Furthermore, ratio of 4.5 was selected as the optimum condition for SD1 grease to produce an intermediated yellow grease, at the same time, final biodiesel product can meet ASTM D6751 Sulfur standard

(13.9ppm).

*(#): Mass Distribution of Total Sulfur (wt%)

Figure 20. Sulfur Flow from SD1 Grease to Biodiesel

4.6.3 Ultimate Analysis of FOG

The purpose of this sub-chapter is to investigate the combustion properties of FOG and solids after oil extraction. It is a quantitative analysis of various elements present in the sample, such as carbon, hydrogen, sulfur, oxygen, nitrogen, fly ash and heating value. In this study, raw FOG and solids samples are conducted by ASTM D5453 standards to be analyzed (OKI lab, OH). In addition, the H/C and O/C ratio are also reported to compare with other fuel sources. (H. Prajitno et al. 2016).

As shown in Table 25, no significant difference of high heating value (HHV) was detected among three sources of FOG. Also, the FOG has a higher HHV than derived solids since the high HHV oil has been extracted from FOG. The HHV range is 29.24 to 35.64 MJ/Kg.

According to the Van Krevelen Diagram (Fig. 21), both FOG and solids could be in one category with similar combustion properties. And H/C molar ratio range is 1.72 to 1.98 which is close to oil and higher than coal, lignite and anthracite. The O/C ratio is from 0.17 to 0.23 which is in between coal and lignite. The carbon to nitrogen (C:N, wt/wt) ratio is within the desirable range for anaerobic digestion ranges from approximately 20:1 to 30:1. This indicates the remaining solids are suitable for anaerobic digestion (Yuan and Zhu 2016).

Table 25. Ultimate Analysis of FOG and Solids (wt%) HHV wt% Ash C H O N S H/C O/C C:N MJ/Kg MSD FOG 0.05 69.32 10.95 19.35 3.28 0.05 32.21 1.90 0.21 24.66 (summer) Solids 0.01 69.25 10.02 18.64 3.02 0.06 31.35 1.74 0.20 26.75 MSD FOG 0.10 67.83 9.74 19.63 2.65 0.05 33.31 1.72 0.22 29.86 (winter) Solids 0.08 68.19 10.38 20.88 2.41 0.06 30.16 1.83 0.23 33.01 FOG 0.15 69.17 10.12 17.23 3.15 0.08 33.32 1.76 0.19 25.62 Solids after 0.01 70.36 10.53 16.32 2.79 0.08 33.20 1.80 0.17 29.42 SD1 WGE (winter) Solids After 0.02 69.78 9.85 16.75 2.92 0.70 30.65 1.69 0.18 27.88 H&F JTM FOG 0.40 65.86 10.55 19.56 3.58 0.07 35.64 1.92 0.22 21.46 (summer) Solids 0.05 69.21 11.42 16.30 2.92 0.10 34.88 1.98 0.18 27.65 JTM FOG 0.01 76.71 11.9 11.1 <0.01 0.2 39.17 1.86 0.11 NA (winter)* Solids 1.32 54.46 10.21 29.7 3.81 0.5 26.24 2.25 0.41 16.68 *Conducted by the former study (Tu et al. 2016)

Figure 21. H/C and O/C Ratios on the Van Krevelen Diagram

Chapter 5. Conclusions and Future Directions

In this study, commercial and technical feasibility from FOG to biodiesel are investigated in the

following perspectives: current FOG management, green titration method, and sulfur analysis.

According to the survey in 29 WWTPs, it indicated that the preferred method of FOG handling

depends on geographical location. WWTPs in the Northeast incinerate the FOG as a fuel source

or for volume reduction, land-filling is more common in the Midwest, and anaerobic digestion is

more practiced on the West coast. 17 WWTPs do not accept grease hauler FOG, which represent

62.6% by capacity. The FOG from these facilities only come from the sewer system, which may

have a lower lipid content and a lower quality. Landfill is still the major FOG disposal method,

while anaerobic digestion practice is increasing. This also indicates that the FOG processing

technology needs to be custom developed based on each WWTP. Besides, a potential market

from FOG of WWTP to biodiesel is discovered by reducing current disposal cost significantly

and producing environmental-friendly biodiesel.

Based on official titration method ACOS cd 3d-63, a green alternative method has also been

developed for determination of acid numbers for FOG. It eliminated the usage of toluene, a

common and yet toxic solvent in AOCS and ASTM standards. Within a wide range of AN, the

green method can generate statistically equivalent results as the ACOS cd 3d-63 method, with

high linearity, lower repeatability, passing ANOVA f-test, t-test and lower errors. By reducing

solvent dosage and elimination the use of the more toxic toluene, it can be recommended as a

72 routine titration method in R&D as well as in biodiesel plants for both yellow and brown grease.

As indicated in chapter 4, sulfur flow from three FOG (JTM, MSD, SD1) to biodiesel is investigated. Sulfur content of raw grease vary from 300 to 800 ppm. And by an oil-separation process (H&F or WGE), the derived oil contains 300 to 500 ppm sulfur. Then biodiesel reaction can reduce the sulfur concentration by 84.9% to 95.2%. By propitiate WCO-FOG ratio (at 3.6 for

MSD, 4.5 for SD1), all of the biodiesel products can meet the ASTM D6751 sulfur standards

(15ppm). The heating value of FOG and solids are reported as 29.24 to 35.64 MJ/Kg. The remaining solids can be incinerated from the prospective of fuel.

The future studies would be focused on the seasonal impact on the FOG properties and sulfur forms in FOG/biodiesel. In addition, the sulfur removal strategy from FOG to biodiesel can be investigated.

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Appendix I (Chapter 2) Surveyed WWTPs

Facility Name Present City State FOG Disposal Accept FOG from Capacity, Haulers, Y/N MGD The Metropolitan Sewer 151 Cincinnati OH Landfill Y District of Greater Cincinnati City of Dayton Water 72 Dayton OH Landfill N Reclamation Facility Toledo Wastewater 85 Toledo OH Landfill N Treatment Jackson Pike Wastewater 114 Columbus OH Landfill N Treatment Montgomery County 18 Montgomery OH Landfill N Environmental Services Southerly Wastewater 170 Cleveland OH Landfill Y Treatment Plant Fairfield Wastewater 5 Fairfield OH Landfill N Treatment Facility City of Mason's Water 6 Mason OH Landfill N Reclamation Plant Perrysburg Waste Water 5.4 Perrysburg OH Landfill N Treatment Plant Dry Creek Wastewater 46.5 Villa Hills KY Landfill Y Treatment Plant (SD1) Town Branch Wastewater 20.99 Lexington KY Landfill Y Treatment Plant Louisville Metropolitan 120 Louisville KY Landfill N Sewer District (MSD) Detroit Wastewater 660.5 Detroit MI Landfill N Treatment Plant Ypsilanti Community Utilities 22.5 Ypsilanti MI Landfill N Authority City of Howell Wastewater 2.45 Howell MI Landfill N Treatment Plant Ann Arbor Wastewater 15.1 Ann Arbor MI Landfill N Treatment Plant Metropolitan District 53.5 Hartford CT Incineration Y Connecticut East Shore Water Pollution 30.7 New Haven CT Incineration Y Abatement Facility Manchester Water and 8.2 Manchester CT Incineration N Sewer Department LA Sanitation 325 Los Angeles CA Digestor N

East Bay Municipal Utility 70 Oakland CA Digestor Y District Metropolitan St. Louis Sewer 110 St. Louis MO Incineration Y

District Passaic Valley Sewerage 230 Newark NJ Incineration Y Commission Albuquerque Wastewater 55 Albuquerque NM Digestor N Utility Stickney Water Reclamation 812 Chicago IL Landfill N Plant The City of Boulder WWTP 13 Boulder CO Digestor N The Metro Wastewater 152 Denvor CO Landfill Y Reclamation District (Denver CO) City of Tempe Wastewater 23 Tempe AZ Landfill Y Treatment Facility DC Water 370 DC DC Landfill Y Total 3766.84

Summary of the Surveyed WWTPs

# in top 50 9 Landfill # 20 Digestor # 4 Incineration # 5 Landfill % 76.23 Digestor % 12.29 Incineration % 11.48 % not accept grease haulers 62.62 # not accept grease haulers 17.00

Appendix II (Sulfur Analysis for FOG)

Analysis Report of sulfur in FOG and Ultimate Analysis

Appendix III (Analysis of Biodiesel)

Analysis Report of Biodiesel