Longitudinal Study of Wastewater Greases and Their Potential for the

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Longitudinal Study of Wastewater Greases and Their Potential for Production of Biofuels Megan E Hums, Hiral Amin, Ya-Chi Tsao, Mira Olson, Sabrina Spatari, and Richard Cairncross Energy Fuels, Just Accepted Manuscript • DOI: 10.1021/acs.energyfuels.7b03550 • Publication Date (Web): 11 Jan 2018 Downloaded from http://pubs.acs.org on January 11, 2018

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Energy & Fuels

Longitudinal Study of Wastewater Greases and Their Potential for Production of Biofuels Megan E. Hums1, Hiral Amin3, Ya-Chi Tsao3, Mira S. Olson2, Sabrina Spatari2, and Richard A. Cairncross1* 1. Department of Chemical and Biological Engineering, Drexel University, Philadelphia, PA 2. Department of Civil, Architectural, and Environmental Engineering, Drexel University, Philadelphia, PA 3. Planning and Research Unit, Philadelphia Water Department, Philadelphia, PA *Corresponding author: Richard Cairncross: 3141 Chestnut St. Department of Chemical and Biological Engineering, Philadelphia, PA 19104. Email: [email protected] Phone: 215-895-2230

Abstract Grease trap waste (GTW) and sewage scum grease (SSG) are underutilized, high-lipid waste streams that have the potential to be converted into biodiesel. This paper presents a longitudinal study of GTW and SSG samples that were obtained over a one year period; GTW was sampled from a storage tank at a grease collection company and SSG was sampled from scum concentration buildings at three wastewater resource recovery facilities. Samples were fractionated to quantify their lipids, secondary wastewater, and solids content. Results show that the average lipid content of SSG was seasonally dependent; lipid content was 15-40% in cooler months and 3-21% in warmer months. Alternatively, GTW showed an average overall lipid content of 4% in raw GTW; however, the floating layer from settled GTW had an average lipid content of 34%. These greases could serve as feedstocks for urban low-carbon biodiesel production while reducing the volume of biosolid waste disposal.

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1.

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INTRODUCTION Grease trap waste (GTW) and sewage scum grease (SSG) are underutilized, low-quality waste

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streams that have the potential to be converted into biodiesel [1-3]. These wastes are troublesome to the

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wastewater system because of the formation of deposits due to calcium-based saponified grease causing

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sewer blockages and overflows [4, 5]. GTW is kitchen effluent that is collected in a grease interceptor.

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Grease hauling companies collect GTW from a variety of food service establishments and deliver the

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GTW to disposal sites. Frequently, smaller GTW loads are aggregated at transfer stations before being

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transported to disposal sites. SSG is floating material collected from settling tanks at water resource

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recovery facilities (WRRFs) where SSG is skimmed from the tanks, partially-dewatered, neutralized with

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lime and transported to disposal sites. The disposal method for GTW and SSG varies depending on the

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region with common disposal at landfills, land application, anaerobic digesters, or incinerators [6, 7]. The

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lipids contained in GTW and SSG are often referred to as FOG (for fats oils and greases) or brown

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grease; brown grease can be separated from water and solids in GTW and SSG by heating and settling or

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by more advanced techniques such as solvent extraction. Brown grease lipid separation and conversion

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into biodiesel offers energy commodity benefits [8] and possibly environmental benefits [9, 10] compared

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with disposal alone. While FOG shows potential as a source of bioenergy, it is rarely utilized in practice

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[11].

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The environmental impacts of using wastewater greases to produce biodiesel have been

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previously analyzed using life cycle assessment (LCA) [9, 10, 12]. Major findings from this research

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points to the sensitivity of life cycle impact assessment (LCIA) metrics due to brown grease lipid content

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[9, 10]. For brown grease lipid contents less than 10%, the predicted greenhouse gas emissions (GHG)

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are greater than 50 gCO2-eq/MJ-fuel [9], but at higher lipid contents the GHG emissions are as low as 20

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gCO2-eq/MJ-fuel; for comparison, low-sulfur diesel and soybean biodiesel GHG emissions (GHG) are 93

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and 25 gCO2-eq/MJ-fuel, respectively [13, 14]. The disposal of the residual wastes from the GTW (wet

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solids and wastewater after brown grease extraction) and the collection of the GTW account for roughly

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75% of GHG emissions [9]. These processes are already occurring in the “business-as-usual” model


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where the brown grease lipids are disposed with the wet solids. Comparing grease biodiesel production to

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the “business-as-usual” process, there is a 20-75% GHG reduction due to reductions in waste disposal and

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displacement of low-sulfur diesel [9].

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The overall composition of GTW and SSG vary including their brown grease lipid content which

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could impair the viability of a large-scale biodiesel production facility. The brown grease lipids that are

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extracted from these low-quality greases are highly variable in both quantity and quality [7, 15]; samples

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of GTW have shown brown grease lipid content variability from 0.4-40% [15]. To the authors’

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knowledge the lipid content of SSG is not as well researched but estimates of lipid composition between

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3-11% have been measured [3]. The quality of the brown grease lipids also varies; these waste greases

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have high free fatty acid (FFA) contents which require different biodiesel conversion than conventional

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biodiesel feedstocks because of the formation of soaps [2, 16]. The FFA content of these greases ranges

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between 26-100% FFA [16, 17]. The FFA profiles reported for GTW and SSG are about 36-60% oleic

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acid (C18:1) and 24-35% palmitic acid (C16:0) [3, 7]. In addition, these greases have high concentrations

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of impurities including oxidized lipids, volatile organic compounds, nitrogen, sulfur, and metals [15].

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This paper presents results from a longitudinal study undertaken to determine the compositional

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variability of GTW and SSG and brown grease lipids. The longitudinal study also includes water quality

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testing of wastewater that is generated as part of the brown grease separation process and comparison to

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the quality of other wastewater streams within the WRRF.

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2.

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MATERIALS AND METHODS The longitudinal study spanned a twelve-month period from July 2014 through June 2015.

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During this study, 61 samples of sewage scum grease (SSG) and 35 samples of grease trap waste (GTW)

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were collected on a roughly weekly basis and analyzed using a grease lipid extraction (GLE) process.

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GTW was sampled from Russell Reid Waste Management (RRWM), a grease hauler that collects a

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substantial portion of the GTW in the Philadelphia metropolitan area, and SSG was sampled from three

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WRRFs in the Philadelphia Water Department (PWD) system.

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2.1.

Materials and Equipment


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Sulfuric acid (Fischer Scientific), deionized water, Methanol (Spectrum), Toluene (Sigma-

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Aldrich), Isopropanol (PTI Process Chemicals), Potassium Hydroxide (Sigma-Aldrich) were used in the

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separation and titration of the grease lipid extraction procedure.

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A New Brunswick Gyrotory Water Bath Shaker G76 with Heater was used to heat and agitate the

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grease samples. Depending on extraction location, a Clay Adams Dynac II Centrifuge or a ThermoFisher

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Sorvall Legend X1 Centrifuge was used to promote faster fractionation of grease layers.

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Raw grease samples were collected and placed in 3.8 L (1 gal) UN-compliant shipping pails; the

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pails had a reusable locking lid and o-ring seal that enabled transporting samples with minimal risk of

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spillage. The pails were stored in a 7 ᵒC refrigerated storeroom between sampling and testing.

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2.2.

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Wastewater Grease and Water Sampling A schematic of the GTW and SSG sampling and testing processes are shown in Figure 1. Photos

of sampling locations for sampling GTW and SSG are shown in Figures 2 and 3, respectively. Grease haulers collect GTW from the grease interceptors of multiple food service establishments.

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GTW readily separates into layers during storage. GTW samples were obtained from RRWM at their

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Deptford, NJ transfer station where an 1890 L (500 gal) polypropylene settling tank was located. This

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settling tank received GTW from a box truck that conducted full pump-outs of interior and small exterior

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grease interceptors (removal of all floating solids, wastewater, and sediments from the grease trap).

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Normally several loads of GTW were added to the settling tank before sampling, and the GTW settled in

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the tank for 1-7 days prior to sampling. Because the tank was made of polypropylene, the tank was semi-

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transparent; so, it was possible to observe the transition between different layers. After settling, there were

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three layers visible in the tank: (1) a floating layer, (2) a wastewater layer, and (3) a sediment layer

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(Figure 2). The floating layer and sediment layer were both darker than the secondary wastewater. The

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depth and volume of each layer was recorded prior to sampling from each layer for analysis.

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Samples from the top floating layer were obtained manually as grab samples. The floating layer

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was semi-solid and contained water, solids, and lipids. Occasionally, the floating grease layer appeared to

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have separated into several separate layers of floating solids, liquid grease, and foam. When several


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floating layers were apparent, the layers were sampled separately. The samples from the top floating

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layer were analyzed by a grease lipid extraction (GLE) procedure developed for this project to identify the

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quantity of brown grease lipids, floating solids, extraction water, and sediments. A detailed description of

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the GLE process is discussed in the following section. A sample of the wastewater layer (tank water) was

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obtained while the tank was drained.

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SSG floats to the top of the primary settling tank at a WRRF. The surface of the primary settling

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tank is skimmed and the scum flows by gravity to a scum concentration building (SCB) where it

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accumulates and is partially dewatered by settling. Grab samples of SSG were collected from SCBs at

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PWD’s three WRRFs. Figure 3 displays photos of an SCB and the process of obtaining SSG samples.

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SSG samples were drained prior to the GLE procedure in which they were fractionated into

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lipids, solids, and extraction wastewater. The water effluent of the primary tank and SCB (underflow

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water - UW), the drained water during the GLE (filtered water – FW), and the extraction water (EW)

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were all sampled to test the water quality. Water sampling points are indicated with an asterisk (*) in

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Figure 1.

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2.3.

Grease Lipid Extraction (GLE) Fractionation of Wastewater Grease Samples

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The wastewater grease fractionation step was performed both at PWD and Drexel using a

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standard operating procedure which is referred to as Grease Lipid Extraction (GLE); the GLE method was

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modified slightly based on equipment available at each site. GLE was performed on the floating layer of

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the GTW and of the partially dewatered SSG. The GLE method was developed from preliminary

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experiments to optimize lipid separation from SSG. We have observed that GTW lipids separate by

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heating and settling more easily than SSG lipids; however, to enhance lipid separation, the GLE method

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includes lowering the pH of the raw grease samples by using sulfuric acid prior to heating and settling. A

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process flow diagram of the GLE process is shown in Figure 4.

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Samples of wet raw grease were mixed by hand to homogenize the mixture. For samples of SSG,

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some large trash objects such as twigs, plastic wrappers, and paper were removed when taking a sample

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for GLE. Approximately 200 g of wet raw grease was strained for 15 min to remove excess free water.


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Four replicate GLE experiments were conducted for each sample of raw waste grease. For each replicate,

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about 40 g of strained raw grease was placed into a 250 mL glass Erlenmeyer flask. 10 mL of 10%

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sulfuric acid in water solution was added to each Erlenmeyer flask. Each flask was lightly capped; the

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mixture was heated at 60 ᵒC and shaken for 30 min. The mixture in each individual flask was transferred

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to an individual 50 mL centrifuge tube and centrifuged at about 1000 RPM for 15 min. After

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centrifuging, four layers within the centrifuge tube were observed from top to bottom: (1) brown grease

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lipids, (2) floating solids, (3) extraction wastewater, and (4) sediments. The weight and volume of each

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of these layers was measured. The weight of each layer was divided by the starting mass to give a percent

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weight of each layer. Because of the addition of the sulfuric acid, the sum of the weight of the layers is

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greater than 100%.

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At PWD, the GLE procedure was scaled up to 500 mL volumes of SSG to produce enough

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secondary wastewater for water quality testing. The following is a list of differences between analytical

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methods at PWD and Drexel University:

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•

PWD experiments used larger scum samples (~500 mL) compared to Drexel (~40 mL).

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•

The centrifuge used at PWD was able to produce higher centripetal accelerations.

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•

The Drexel extractions used a higher degree of agitation during the heating and shaking stage.

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•

PWD extractions were performed within 24 hours after sample collection. Drexel extractions

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were performed days, weeks, or months later, but samples were stored in a cold room (~7 °C)

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until the extraction experiments were performed.

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•

In samples containing a significant amount of trash, it was often difficult to obtain consistent

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experimental samples – which lead to higher variability both in the initial experimental sample at

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PWD and in later experiments performed at Drexel.

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The supporting information includes a comparison of results from the Drexel and PWD extractions.

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Figure S1 shows a strong correlation between the GLE lipid extraction performed at Drexel and PWD

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(significant below 0.01 level). There was no correlation (not significant below 0.05 level) between the


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total acid number (measurement of lipid quality discussed in the next section) of Drexel extracted samples

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and PWD extracted samples. Drexel samples for GLE were stored longer prior to GLE which could be a

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cause for the lack of correlation.

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2.4.

Brown Grease Lipid Quality

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The free fatty acid (FFA) content of the lipids was measured using titration. Potassium hydroxide

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(KOH) was dissolved in 20% volume water in methanol solution to make a 0.1 M titrant. Five ml solvent

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of 50% volume toluene in isopropanol with phenolphthalein indicator was used to dissolve approximately

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0.3-0.5 g of brown grease lipids.

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The total acid number (TAN) of the lipids was calculated to determine the amount of KOH

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needed to reach the slightly pink endpoint per gram of sample titrated. This calculation was determined

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using Equation 1: =

= (. 1)

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Where,

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VTitrant = volume of titrant, mL

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MTitrant = molarity of titrant, mol/L

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MWKOH = molecular weight of potassium hydroxide, g/mol

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mSample = mass of sample titrated, g

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The TAN is reported as mg KOH/g sample.

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The TAN number was then normalized to a percentage FFA value on an oleic acid basis by

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dividing by 198.6 mgKOH/g, which is the TAN of pure oleic acid. While using the %FFA on an oleic

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acid basis is not as accurate as gas chromatography techniques, it provides an approximate value for

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%FFA and is simple and quick to perform on a large number of samples.

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2.5.

152 153

Wastewater Quality For wastewater samples from GTW, the water quality was characterized by chemical oxygen

demand (COD) and total solids content. The COD was tested according to Hach Company method 8000


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for water, wastewater and seawater [18]. Total solids in the wastewater was determined according to

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Hach Company method 8271 for potable, surface and saline water and for domestic and industrial

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wastewater [19].

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For wastewater samples from SSG, the water quality was characterized by COD, total Kjeldahl

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nitrogen (TKN), total solids content, volatile solids content, ammonia, pH, conductivity, and alkalinity

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using multiple standardized methods. A table of the wastewater quality metrics and standardized methods

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is shown in Table 1.

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2.6.

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Statistical Analysis Statistical analysis was performed using IBM SPSS Statistics 23 [20].

1-tailed Pearson

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correlations were used to compare the data. The Pearson coefficient indicated the trend of the correlation

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(positive or negative) and the significance indicated the strength of the correlation. Significance values

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above 0.05 were not considered significant; values below 0.05 and above 0.01 were considered weakly

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significant; values below 0.01 considered significant.

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The parameters studied for correlations of GTW and SSG are shown in Tables 1 and 2,

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respectively. The boxes are colored to identify similar parameter groupings. External factors such as

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temperature, precipitation, and time of year are grouped as green. Extracted lipids and TAN are grouped

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in orange; extracted solids are grouped in brown; and extracted water is grouped in blue. The water

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quality parameters for the tank water (GTW) and underwater (SSG) are grouped in purple. The water

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quality parameters for the extraction water are grouped in light red.

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3.

RESULTS AND DISCUSSION

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3.1.

Longitudinal Study Grease Trap Waste Results

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3.1.1. Grease Trap Waste Composition

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GTW was sampled 35 times from the RRWM settling tank. The total volume of raw GTW in the

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collection tank ranged between 1110 -1728 L (293-457 gal) with an average of 1431 L (379 gal). GLE

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was performed only on the top floating grease layer. Figure 5 displays the volume of each of the layers in

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the tank; however, the floating grease layer is represented as two components based on GLE results: (1)


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the extractable brown grease lipids (orange) and (2) wet floating solids which are the remainder of the

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floating grease (tan). The brown grease lipid layer volume varies between 0.2-12.1% of the raw GTW

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volume with an average of 3.9%. However, the brown grease lipid content can also be expressed as a

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volume of the float layer to represent concentration of the brown grease from gravity settling alone.

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Brown grease content of the float grease layer varies between 11-100% with an average of 34% which is

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a 750% increase in brown grease content compared to raw GTW. The wet solids in the floating layer

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ranges between 0-22% of the raw GTW volume with an average of 7%. The wastewater layer (blue) was

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47-83% of the raw GTW volume with an average of 66%. The wastewater layer had the largest volume

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of material and the highest variability of the layers observed during sampling. The sediment layer

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(brown) in the tank varied between 15-35% of the raw GTW volume with an average of 23%.

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3.1.2. Grease Trap Waste Tank Water Quality

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The water quality of the GTW tank water is shown as a box plot in Figure 6. The box portion

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represents the 25-75 percentiles, the whiskers represent the 5-95 percentiles, the filled square represents

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the mean, the straight horizontal line represents the median, and the star represents the minimum and

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maximum. The average COD was 9,192 mg/L with a standard deviation of 4,665 mg/L. The COD of the

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tank water varied greatly with a minimum of 2,247 mg/L and a maximum of 14,580 mg/L. The average

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total solids content in the GTW tank water was 0.28% with a standard deviation of 0.13%. The total

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solids content in the tank water was generally between 0.20-0.32% except for one sample at each extreme

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with a minimum of 0.16% and maximum of 0.56%. The wastewater quality of the tank water is similar to

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that of industrial wastewater.

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3.1.3. Statistical Correlations between Data from Grease Trap Waste Samples

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Correlations were sought between pairs of all data sets (Table 2) collected for GTW. The

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significant correlations found for GTW are shown in Table 4. The table shows the correlated parameters,

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Pearson correlation, significance (Sig.), number of samples (N), and the relevance of the correlation. The

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table is divided between significant correlations (** correlation is significant at the 0.01 level) and weakly

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significant (* correlation is significant at the 0.05 level). The first three rows of Table 4 are the strongest


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identified correlations that are between the depth of the wastewater layer and the depths of the sediment

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layer, the float grease layer and the extractable lipids. These pairs of correlated parameters are related to

208

mass balances; for example, if the percent water that separates increases, it is necessary that either the

209

lipid content or wet solids will decrease. When the water content of GTW is higher and the wastewater

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layer is thicker, this correlates to less sediments, less float grease, and less lipids. These correlations

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between the thickness of the wastewater layer (i.e. the dilution of the GTW) and the solid layers are not

212

surprising. Plots of these correlations are shown in the supporting information. Table 4 also shows weak

213

inverse correlations between the total solids content of the wastewater and the sampling

214

month/temperature. This result shows that there is a slight seasonal variability to the wastewater total

215

solids content. There is also a weak inverse correlation between the amount of brown grease lipids and

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the TAN. In some cases, there was a free-oil layer at the top of the tank which could have been waste

217

cooking oil. The variability of the GTW could also be due to the type of grease abatement device used at

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the restaurant/cooking facility. It was observed that FOG layers typically remained constant for different

219

types of grease traps; however, food solids can accumulate in the first compartment or be transported

220

depending on the inlet tee [21]. The variability in inlet type at the various food services could explain

221

some of the variability seen with the sediment layer.

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3.2.

Longitudinal Study Sewage Scum Grease Results

223

3.2.1.

Sewage Scum Grease Composition

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SSG was sampled 61 times from SCBs at PWD during the longitudinal study and each sample

225

was fractionated using the GLE procedure. Figure 7 displays the monthly averages of the amounts of the

226

GLE fractions of SSG; the total percentage of the factions is above 100% because the percent mass is

227

calculated based on the starting wet grease mass and sulfuric acid solution is added during GLE. The

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monthly average secondary wastewater fraction has the largest variability, ranging between 8-72% of

229

initial sample volume, and the wet residual solids (including both floating solids and sediments) have the

230

next largest variability 21-74%. The largest values of monthly average lipid content are in March 2014,


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January 2015, and April 2015 with over 40% lipids while the lowest monthly average lipid content is in

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August 2014 with only 3% lipids.

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3.2.2.

234

Sewage Scum Grease Water Quality Individual water quality sampling was performed (not monthly averages) of the SCB underflow

235

water, filtered water, and extraction water and is shown as box plots in Figure 8. The box portion

236

represents the 25-75 percentiles, the whiskers represent the 5-95 percentiles, the filled square represents

237

the mean, the straight horizontal line represents the median, and the star represents the minimum and

238

maximum. For all water quality metrics but volatile solids and pH, the intensity of the metric becomes

239

higher with each grease processing stage. The underflow water (UW) has a low value, as the raw SSG is

240

filtered, the filter water (FW) has a higher value, and finally the extraction water (EW) has the highest

241

value. This trend of increasing concentration of contaminants is related to the trend that at each

242

processing step there is a smaller volume of water being separated from the solids. The volatile solids of

243

the three water samples are similar with the highest range in the underwater samples. The trend for pH is

244

UW>FW>EW because of the acid added during the GLE which lowers the pH. The conductivity of the

245

extraction water samples is consistently greater than 1999 uS/cm which is the detection limit of the test.

246

Conductivity is likely high, particularly for extraction water, because of the acid added and high total

247

solids. The extraction water was not tested for alkalinity due to the high pH and the sensitivity of the

248

equipment. The extraction water has high concentration of contaminants and a low pH; however the

249

volume of extraction water is and could be diluted by the influent flow of the primary settling tanks in a

250

process implemented at a WRRF. The theoretical dilution of the extraction water in the primary tank

251

influent is shown in the supporting information.

252

3.2.3.

253

Correlations were sought between pairs of all data sets (Table 3) collected for SSG and results are

254

grouped into highly significant correlations (Table 5) and weakly significant correlations (Table 6). One

255

of the most significant correlations (significance = 9.71x10-9) was the temperature/time of year at

256

collection and the lipid content, showing season variation of lipid content in the composition of SSG. In

Statistical Correlations between Data from Sewage Scum Grease Samples


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addition to variation in lipid content, the color and texture of SSG samples taken in the warmer, summer

258

months were visibly different than those taken in the cooler, winter months. Summer samples were often

259

black and sludge-like with a larger amount of plastic trash and twigs that produced very little lipids.

260

Some collaborators have suggested that the black material is likely sludge from the bottom of the primary

261

settling tank floats to the surface due to higher biological activity during the summer months causing

262

“floating rafts” of sludge. In the samples that contained the highest value of lipids, the SSG was gray-

263

brown and grainy. Example pictures of these scum samples are included in the supporting information.

264

All of the remaining significant correlations are listed in Table 5. The table shows the correlated

265

parameters, Pearson correlation, significance (Sig.), number of samples (N), and the relevance of the

266

correlation. The table is divided between significant correlations (** correlation is significant at the 0.01

267

level) and weakly significant (* correlation is significant at the 0.05 level). The parameters that appear

268

most frequently in the significant correlations are “% Lipids of SSG Sample” (8 times), “Extraction WW

269

– COD” (7 times), “Sampling Month” (4 times), “Daily AVG temp” (3 times), and other extraction

270

wastewater parameters (2-4 times each).

271

Several of the most significant correlations relate to seasonal variations in the composition of

272

SSG and the corresponding wastewater streams. Seasonal variation is shown by correlations between (in

273

order of highest to lowest significance) percent lipids of SSG, percent wet solids of SSG, extraction

274

wastewater COD, extraction wastewater total solids, percent water of SSG sample, underwater

275

conductivity, extraction water ammonia, extraction water total solids, and extraction water nitrogen and

276

either the daily average temperature or the sampling month. The percent solids, percent water, and

277

percent lipids of SSG are all inversely correlated. The secondary wastewater generated during GLE

278

correlated with temperature such that more wastewater is collected when processing samples are collected

279

during warmer months. The mechanism for the formation of scum that rises to the top of the settling

280

tanks leads to higher water content in warmer temperatures (perhaps due to the solubility or emulsion

281

forming mechanics).


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12 282

The extraction water COD has significant correlation with four other water quality metrics of the

283

extraction water, with the percent lipids in SSG, and with season. In addition, many of the significant

284

correlations are between wastewater parameters – either different wastewater quality metrics in the same

285

stream or between the underwater stream and the extraction water. Most of these wastewater correlations

286

(“WW correl” in Table 5) are positive correlations, for example TKN, total solids and volatile solids are

287

all positively correlated with COD in the extraction water. The high TKN, high ammonia, and high COD

288

content of the extraction water may imply a high level of biodegradable organic carbons, which would

289

imply a high biological oxygen demand (BOD). However, during the extraction procedure, acid is added

290

to SSG which lowers the pH to less than 1.0, which would eliminate biological activity; therefore, BOD

291

of the extraction water samples was not studied in this project.

292

3.3. Grease Trap Waste and Sewage Scum Grease Lipid Content Comparison

293

Histograms of GTW and SSG lipid and FFA contents are compared in Figure 9. Because there

294

are more samples of SSG than GTW, the y-axis displays a normalized frequency by dividing the number

295

of occurrences in that specified range by the total number of samples in the data category. Figure 9-A

296

shows the lipid content comparison between the raw GTW (brown grease, floating solids, wastewater,

297

and sediments) and just the floating solids layer. The majority of lipid contents for raw GTW are below

298

10% while the floating layer content had more variability. The average lipid contents for the raw GTW

299

and floating layer are 4% and 32%, respectively. Figure 9-B shows there is very little difference between

300

the FFA content of GTW and SSG brown grease lipids. There are more low-FFA samples of brown

301

grease lipids for SSG than GTW but majority of samples range from 70-90 %FFA. The average FFA

302

contents for GTW and SSG are 77% and 76%, respectively. Figure 9-C shows the seasonal variability of

303

the SSG lipid content. The months were grouped according to season/temperature: warm months were

304

grouped as July-October (average 21.4 ᵒC, minimum 12.5 ᵒC, and maximum 27.2 ᵒC), temperate months

305

were November, December, May, and June (average 18.7 ᵒC, minimum 0.0 ᵒC, and maximum 27.2 ᵒC),

306

and cool months were January to April (average 3.3 ᵒC, minimum -8.2 ᵒC, and maximum 16.3 ᵒC). There

307

is overlap between the ranges of lipid contents for the samples in the warm and temperate seasons;


Energy & Fuels 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

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13 308

however, the warm season has a larger number of samples in the 0-10% lipid content range. All samples

309

from the cool season have lipid contents above 20% and all samples from the warm season have lipid

310

contents below 30%. The average lipid content for the warm, temperate, and cool season was 7%, 22%,

311

and 40%, respectively. Figure 9-D shows that FFA content is not seasonally dependent. All SSG FFA

312

contents overlap with the majority of samples in the 60-80% range. The average FFA content for the

313

warm, temperate, and cool season is 77%, 78%, and 75%, respectively.

314

3.4. Potential Biodiesel Production

315

An estimate for the potential amount of biodiesel that could be produced from raw wastewater

316

grease was calculated using processing volumes of GTW from a WRRF, which aggregates GTW from

317

multiple local collection companies [22], and SSG from a municipal WRRF (PWD). Table 7 displays

318

five scenarios of biodiesel production from GTW, SSG, and a combination of the two feedstocks:

319

1. GTW processing only.

320

2. SSG processing throughout the year (12 months of the year).

321

3. SSG processing during temperate and cool seasons (8 months of the year).

322

4. GTW and SSG processing (combination of scenarios 1 and 3).

323

5. GTW and SSG processing at a larger scale (twice the amount of GTW scenario 1 and

324

quadruple the amount of SSG scenario 3).

325

The table displays the amount of raw grease that is processed and the amount of brown grease is

326

estimated using the lipid content percentages from the longitudinal study results. The amount of biodiesel

327

produced from the brown grease was estimated as 81% conversion. Fuel production was assumed to be

328

90% based on laboratory studies at Drexel University and a 90% distillation yield for biodiesel

329

purification. While we currently can achieve up to 90% yield in distillation, the sulfur content is still

330

about 30 ppm S; therefore, we assume that with further optimization, we can have a high yield with low

331

sulfur content [23]. The calculations are shown in the SI. There is a larger collection volume of raw

332

GTW (scenario 1) compared to SSG (scenarios 2 and 3), and even with the lower lipid content compared


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14 333

to SSG (4% for GTW compared to 20% for SSG) there is approximately 750% more biodiesel produced

334

from GTW than SSG.

335

Using life cycle analysis results from Hums et al. 2016, a fitted equation (included in SI) was

336

developed to estimate the GHG emissions of biodiesel production from the lipid content of GTW [9]; this

337

correlation was used to estimate the GHG emissions in Table 7. The GHG emissions for low-sulfur

338

diesel is 93 gCO2eq/MJ fuel [13]. It was assumed that for every energy unit of biodiesel produced, an

339

equal energy unit of low-sulfur diesel was replaced. The last row of Table 7 shows the GHG reduction

340

from replacing low-sulfur diesel with biodiesel produced from wastewater greases. Depending on the

341

scenario, there is a potential for 63-77% GHG reduction from producing and consuming biodiesel instead

342

of low-sulfur diesel. Because the GHG emissions are inversely correlated to lipid content, the SSG

343

biodiesel production has a higher GHG emission reduction because of the high lipid content material

344

compared to that of GTW.

345

The potential for biodiesel production scales with brown grease content; the brown grease

346

seasonal variability of the SSG was not an expected result and causes some challenges for implementing

347

viable biodiesel using only SSG. The seasonal variability of brown grease volumes may present similar

348

processing challenges that exist for processes that utilize biomass feedstocks. There are some options for

349

designing a SSG-to-biodiesel production plant. The SSG could be aggregated at the WRRF throughout

350

the year with biodiesel production occurring only when a sufficient quantity of brown grease is available

351

for biodiesel production. Alternatively, SSG and GTW could be transported to a centralized processing

352

facility that aggregates the waste and separates brown grease from multiple sources prior to biodiesel

353

production. Both the grease hauler and water resource recovery facilities could implement a brown

354

grease production plant and sell the brown grease to biodiesel producers.

355

4.

356

CONCLUSIONS This paper reports the first longitudinal study of GTW and SSG production that included samples

357

collected repeatedly from the same collection points over the course of a year. Despite the variability of

358

the lipid content of each low-quality waste stream, there is a substantial amount of brown grease in GTW


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Page 16 of 34

15 359

and SSG that can be extracted for biodiesel conversion. Because of their similarities in brown grease

360

composition and FFA content, the biodiesel processing steps would be similar for using either GTW or

361

SSG as a feedstock.

362

5.

363

ACKNOWLEDGMENTS The research presented in this study was performed in partial fulfillment of a Water Environment

364

and Reuse Foundation (WERF) grant [23]. The funding for this research comes from the WERF

365

Research Grant, Extraction of Lipids from Wastewater to Produce Biofuels (U3R13, 2014) and Phase I

366

US EPA Small Business Innovation Research (SBIR), Biofuel Production from Grease Trap Waste

367

(Grant EP-D-14-019, 2014).

368 369 370 371

The authors would like to acknowledge the following people for their contributions to this research: Drexel University: Laura Hrabar, James Moran, Paul Gonzalez, Tapiwa Ndlovu, Jinhao Wang, Adam Moody, Zachary Gibbins, and Edwin Guillermo

372

Philadelphia Water Department: Truong Ngoan, Fletcher Hand, John Consolvo, Aaron Bitler,

373

Alexandra Rosario-Arocho, Jose Mathai, Thomas Pulimkalayil, Keith Houck, John Muldowney, Mary

374

Ellen Senss, Mohammad Ibraham, Matthew Jackson, Maulin Gandhi, Douglas Cowley, Amy Szor,

375

Robert Lendzinsky, Anthony DiGironimo, Nafissa Bizo, Gregy Hanson, Paul Kohl, and Amanda Byrne

376

Russell Reid Waste Management: Gary Weiner, Stephen Bisbee, Esau O’Neil, and Lisa

377 378 379

D’Alessandro Environmental Fuel Research, LLC: Don Wilson and Marylin Huff Conflicts of Interest: None.


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16 380

6. REFERENCES

381 382 383 384 385 386 387 388 389 390 391 392 393 394 395 396 397 398 399 400 401 402 403 404 405 406 407 408 409 410 411 412 413 414 415 416 417 418 419 420 421 422 423

1. 2.

3. 4. 5. 6. 7. 8. 9. 10. 11. 12. 13. 14. 15. 16. 17. 18. 19. 20. 21. 22. 23.

Canakci, M., The potential of restaurant waste lipids as biodiesel feedstocks. Bioresour. Technol., 2007. 98(1): p. 183-90. Stacy, C.J., C.A. Melick, and R.A. Cairncross, Esterification of free fatty acids to fatty acid alkyl esters in a bubble column reactor for use as biodiesel. Fuel Process. Technol., 2014. 124: p. 7077. di Bitonto, L., et al., Efficient solvent-less separation of lipids from municipal wet sewage scum and their sustainable conversion into biodiesel. Renewable Energy, 2016. 90: p. 55-61. Iasmin, M., L. Dean, and J. Ducoste, Quantifying fat, oil, and grease deposit formation kinetics. Water Research, 2016. 88: p. 786-795. He, X., et al., Mechanisms of Fat, Oil and Grease (FOG) deposit formation in sewer lines. Water Research, 2013. 47(http://dx.doi.org/10.1016/j.watres.2013.05.002). Wiltsee, G., Waste Grease Resources in 30 US Metropolitan Areas. 1998, Appel Consultants, Inc. Long, J.H., et al., Anaerobic co-digestion of fat, oil, and grease (FOG): A review of gas production and process limitations. Process Safety and Environmental Protection, 2012. 90(3): p. 231-245. Lopez, R.J., et al., High rate anaerobic digestion of wastewater separated from grease trap waste. Renewable Energy, 2014. 62: p. 234-242. Hums, M.E., R.A. Cairncross, and S. Spatari, Life-Cycle Assessment of Biodiesel Produced from Grease Trap Waste. Environ. Sci. Technol., 2016. Tu, Q. and B.E. McDonnell, Monte Carlo analysis of life cycle energy consumption and greenhouse gas (GHG) emission for biodiesel production from trap grease. J. Clean. Prod., 2015. Wallace, T., et al., International evaluation of fat, oil and grease (FOG) waste management-A review. Journal of Environmental Management, 2017. 187(1): p. 424-435. Dufour, J. and D. Iribarren, Life cycle assessment of biodiesel production from free fatty acid-rich wastes. Renew. Energ., 2012. 38(1): p. 155-162. Cai, H., et al., Analysis of Petroleum Refining Energy Efficiency of U.S. Refineries. 2013, Argonne National Laboratory. Huo, H., et al., Life-Cycle Assessment of Energy and Greenhouse Gas Effects of Soybean-Derived Biodiesel and Renewable Fuels. 2008. Ward, P.M., Brown and black grease suitability for incorporation into feeds and suitability for biofuels. J. Food Prot., 2012. 75(4): p. 731-7. Ragauskas, A.M.E., Y. Pu, and A.J. Ragauskas, Biodiesel from grease interceptor to gas tank. Energy Science & Engineering, 2013. 1(1): p. 42-52. Ngo, H.L., et al., Catalytic Synthesis of Fatty Acid Methyl Esters from Extremely Low Quality Greases. Journal of the American Oil Chemists' Society, 2011. 88(9): p. 1417-1424. Hach, Oxygen Demand, Chemical USEPA Reactor Digestion Method, in Method 8000. 2010. Hach, Solids, Total USEPA Gravimetric Method, in Method 8271. 2012. IBM, IBM SPSS Statistics for Windows. 2015, IBM Corp.: Armonk, NY. Aziz, T., et al., Field Characterization of External Grease Abatement Devices. Water Environment Research, 2012. 84(3): p. 237-246. DELCORA, Establishing Service Charges for the Year 2014 for Hauled Waste Users of the Delaware County Regional Water Quality Control Authority's Facilities and System. 2013. Cairncross, R.A., M.S. Olson, and S. Spatari, Extraction of Lipids from Wastewater to Produce Biofuels. 2016, Drexel University. Water Environment & Reuse Foundation Final Report.


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Page 18 of 34

17 424 425 426 427 428 429 430 431 432 433 434 435 436 437 438 439 440 441 442 443

24.

25.

26.

27.

28. 29. 30. 31.

Standard Methods, D. Block Digestion and Flow Injection Analysis, in 4500-Norg Nitrogen (Organic). 1997, American Public Health Association, American Water Works Association, Water Environment Federation. Standard Methods, G. Total, Fixed, and Volatile Solids in Solid and Semisolid Samples in 2540 Solids. . 1997, American Public Health Association, American Water Works Association, Water Environment Federation. Standard Methods, B. Preliminary Distillation Step, in 4500-NH3 Nitrogen (Ammonia). 1997, American Public Health Association, American Water Works Association, Water Environment Federation. Standard Methods, D. Ammonia-Selective Electrode Method, in 4500-NH3 Nitrogen (Ammonia). 1997, American Public Health Association, American Water Works Association, Water Environment Federation. Standard Methods, B. Electrometric Method, in 4500-H+ pH Value. 1997, American Public Health Association, American Water Works Association, Water Environment Federation. Standard Methods, B. Laboratory Method, in 2510 Conductivity. 1997, American Public Health Association, American Water Works Association, Water Environment Federation. Standard Methods, B. Titration Method, in 2320 Alkalinity. 1997, American Public Health Association, American Water Works Association, Water Environment Federation. US EPA, Method 410.4, Revision 2.0: The Determination of Chemical Oxygen Demand by SemiAutomated Colorimetry. 1993, U.S. Environmental Protection Agency: Cincinnati, OH.

444 445


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18 446

TABLES

447

Table 1. SSG Wastewater Quality Testing Methods

Parameters Total Kjeldahl Nitrogen Total Solids Volatile solids Ammonia pH Conductivity Alkalinity Chemical Oxygen Demand

Method 4500-Norg Nitrogen (Organic): D [24] 2540 Solids: G [25] 2540 Solids: G [25] 4500-NH3 Nitrogen (Ammonia): B & D [26, 27] 4500-H+ pH Value: B [28] 2510 Conductivity: B [29] 2320 Alkalinity: B [30] EPA 410.4 [31]

448 449


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Page 20 of 34

19 450

Table 2. GTW parameters studied for correlation analysis.

GREASE TRAP WASTE Sampling Month Into the Tank Month Days in Tank Daily AVG Temp (ᵒC) Volume of Extractable Lipids in GTW Float Grease (%) Volume of Extractable Lipids in Tank (%) Separated GTW Float Grease in Tank (%) Separated Wastewater Layer in Tank (%) Separated Sediment Layer in Tank (%) TAN/FFA of Lipids (mg KOH/g) Tank WW - Total Solids (%) Tank WW - COD (mg/L) 451 452


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Energy & Fuels

20 453

Table 3. SSG parameters studied for correlation analysis.

SEWAGE SCUM GREASE Source of SSG: PWD WRFF (NE, SE, SW) Source of SSG: PWD WRFF Location (PTE, SCB) Source of SSG: PWD WRFF Plant & Location Sampling Month (1-16) Sampling 12 Month (1-12) Average Temperature (ᵒC) 5-day Average Precipitation (mm) % Lipids of SSG Sample % Water of SSG Sample % Wet Solids of SSG Sample TAN/%FFA of Lipids (mg KOH/g) Underflow Water COD (mg/L) Underflow Water Total Kjeldahl Nitrogen (mg/L) Underflow Water Total Solids (%) Underflow Water Volatile Solids (%) Underflow Water Ammonia (mg/L) Underflow Water pH Underflow Water Conductivity (uS/cm) Underflow Water Alkalinity (mg/L) Extraction Water COD (mg/L) Extraction Water Total Kjeldahl Nitrogen (mg/L) Extraction Water Total Solids (%) Extraction Water Volatile Solids (%) Extraction Water Ammonia (mg/L) Extraction Water pH Extraction Water Conductivity (uS/cm) Extraction Water Alkalinity (mg/L) 454 455


Energy & Fuels

21 Table 4: Summary table of the most significant parameters for the GTW process. ** Correlation is significant at 0.01 level. * Correlation is significant at 0.05 level. Parameter

Parameter

Pearson Correl.

Sig. (1-tailed)

N

Relevance

Separated Wastewater Layer in Tank (%)

Separated Sediment Layer in Tank (%)

-0.679**

3.53E-04

21

Depth of Water Layer inversely related to depth of sediments


Separated GTW Float Grease Layer in Tank (%)

-0.650**

7.11E-04

21

Depth of Water Layer inversely related to depth of float grease


Volume of Extractable Lipids in Tank (%)

-0.609**

1.70E-03

21

Depth of water layer inversely related to extractable lipids

Separated GTW Float Grease Layer in Tank (%)

COD of Wastewater (mg/L)

0.910**

2.21E-03

7

Higher COD correlates with more float grease

Total Solids of Wastewater (mg/L)

Sampling Month

-0.897**

3.14E-03

7

Total solids decreases during warmer months

Total Solids of Wastewater (mg/L)

Average Temperature (ᵒC )

-0.819*

1.21E-02

7

Total solids decreases during warmer months

Volume of Extractable Lipids in GTW Float Grease (%)

Total Acid Number (%FFA) of Lipids (mg KOH/g)

15

Acid number weakly inversely related to the amount of extractable lipids

-0.446*

4.76E-02

458 459


SIGNIFICANT

456 457

WEAKLY SIGNFICANT

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Page 22 of 34

Page 23 of 34

22 460 461

Table 5: Listing of the pairs of SSG data sets that were shown to be highly significant in the statistical analysis. ** Correlation is significant at 0.01 level. Parameter

Parameter

Extraction Water Nitrogen (TKN)

Extraction Water Ammonia (mg/L)

% Water of SSG Sample % Lipids of SSG Sample

% Lipids of SSG Sample Daily AVG Temp (ᵒC)

Pearson Correl.

Sig. (1-tailed)

N

Relevance

.747**

1.96E-11

56

**

5.25E-09

60

9.71E-09

60

7.98E-08

60

-.659

**

-.650

**

% Lipids of SSG Sample

Sampling 12 Month (1-12)

Extraction Water - Total Solids (%)


.540**

5.09E-06

59

Daily AVG Temp (ᵒC)

.502**

2.22E-05

60

**

.485

8.65E-05

55

.480**

1.04E-04

55

-.449**

1.78E-04

59

% Wet Solids of SSG Sample Extraction Water - COD (mg/L) Extraction Water Nitrogen (TKN)

Extraction Water - Total Solids (%) Extraction Water - COD (mg/L)

-.616


% Water of SSG Sample



.442**

2.05E-04

60

Extraction Water - COD (mg/L)


.440**

3.83E-04

55

Extraction Water - COD (mg/L)


-.418**

7.44E-04

55


-.373**

1.68E-03

60

**

2.19E-03

55

2.50E-03

19

2.70E-03

18

3.29E-03

14

3.92E-03

19


**

-.345

4.58E-03

56

Daily AVG 5-day AvgPrecip (mm)

-.573**

6.51E-03

18

**

7.36E-03

59

**

8.08E-03

58

.319**

8.81E-03

55

**

9.01E-03

60

9.72E-03

18

% Wet Solids of SSG Sample Extraction Water - COD (mg/L) Underflow Water - Volatile Solids (%) Underflow Water Nitrogen (TKN) Underflow Water Nitrogen (TKN) Underflow Water - Volatile Solids (%) Extraction Water Ammonia (mg/L) Underflow Water Nitrogen (TKN) Extraction Water - Total Solids (%) Extraction Water Nitrogen (TKN) Extraction Water - COD (mg/L) % Wet Solids of SSG Sample Underflow Water-Alkalinity (mg/L)

Sampling 12 Month (1-12) Extraction Water Ammonia (mg/L) Extraction Water - COD (mg/L) Underflow Water - Volatile Solids (%) Extraction Water Nitrogen (TKN)

Sampling 12 Month (1-12) % Lipids of SSG Sample

Extraction Water - Volatile Solids (%) Sampling 12 Month (1-12) TAN of Lipids (mg KOH/g)

-.379

**

.616

**

-.626

**

-.688

**

.590

-.316 -.315

.304

**

-.545


WW correl mass balance seasonal variation seasonal variation higher lipids leads to more contaminants in WW seasonal variation WW correl WW correl higher lipids leads to more contaminants in WW temp varies with season - not part of study higher lipids leads to more contaminants in WW higher lipids leads to more contaminants in WW mass balance seasonal variation WW correl WW correl WW correl WW correl higher lipids leads to less nitrogen in WW seasonal variation seasonal variation higher lipids leads to less nitrogen in WW WW correl seasonal variation lower pH --> higher TAN

SIGNIFICANT CORRELATION

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Energy & Fuels

Energy & Fuels

23 462 463

Table 6: Listing of the pairs of SSG data sets that were shown to be weakly significant in the statistical analysis. * Correlation is significant at 0.05 level Parameter

Parameter

Underflow Water - COD (mg/L)



Underflow Water-Alkalinity (mg/L)

N

Relevance

1.54E-02

21


.257*

2.37E-02

60


*

2.44E-02

24

*

2.66E-02

60

*

2.72E-02

15

2.73E-02

55

3.05E-02

21

3.39E-02

59

3.44E-02

17

3.66E-02

17

3.70E-02

24

3.71E-02

56

3.80E-02

56

3.81E-02

58

3.81E-02

21

3.98E-02

59

4.12E-02

18

4.17E-02

19

4.52E-02

19

4.55E-02

58


Underflow Water Ammonia (mg/L) Extraction Water - COD (mg/L) Underflow Water Conductivity (uS/cm) Extraction Water - Volatile Solids (%)

Underflow Water - Volatile Solids (%)

Underflow Water - pH Underflow Water Ammonia (mg/L) Underflow Water - Volatile Solids (%) Extraction Water Nitrogen (TKN)

Sig. (1tailed)

-.472*


Underflow Water - pH Underflow Water Conductivity (uS/cm) Underflow Water-Alkalinity (mg/L) Extraction Water Ammonia (mg/L) Extraction Water Ammonia (mg/L) Extraction Water Nitrogen (TKN) Underflow Water - COD (mg/L) Extraction Water - Total Solids (%)

Pearson Correlation

Daily AVG Temp (ᵒC) Daily AVG Temp (ᵒC)

Extraction Water - Total Solids (%) Underflow Water Nitrogen (TKN) Underflow Water - Volatile Solids (%) Daily AVG 5-day AvgPrecip (mm) Daily AVG 5-day AvgPrecip (mm) Sampling 12 Month (1-12)

Extraction Water - Volatile Solids (%) Extraction Water Nitrogen (TKN) Daily AVG Temp (ᵒC)

Underflow Water - Volatile Solids (%) Extraction Water Ammonia (mg/L) Daily AVG 5-day AvgPrecip (mm) Daily AVG Temp (ᵒC)

.406 .251 -.506

*

-.261

*

-.416

*

.239

*

-.452

*

-.445

*

-.371

*

.241

*

.239

*

.235

*

.395

*

-.230

*

.420

*

.407

*

.399

*

.224

464 465 466


WW correl seasonal variation seasonal variation seasonal variation WW correl seasonal variation seasonal variation WW correl WW correl WW correl seasonal variation seasonal variation seasonal variation WW correl WW correl seasonal variation WW correl WW correl seasonal variation seasonal variation

WEAKLY SIGNIFICANT CORRELATION

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Page 24 of 34

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Energy & Fuels

24 467 468 469

Table 7: Biodiesel production from GTW and SSG with five production scenarios. (1) GTW only; (2) SSG all year; (3) SSG in temperate and cool season; (4) GTW and SSG in temperate and cool season; and (5) larger scale production (twice GTW scenario 1 and quadruple SSG scenario 3). Scenario

1

2

3

4

5

Feedstock

GTW

SSG

GTW + SSG

GTW + SSG

Unit

Avg Lipid

Avg Lipid

SSG Avg Cool & Temp (8 mo)

S1+S3

2*(S1)+4*(S3)

Operation

d/y

312

365

243

312/243

312/243

GTW Avg Lipid Content

% gal/y

GTW Annual Feed Rate

ton/y kg/y

SSG Avg Lipid Content

ton/y kg/y

Raw Grease Lipid Content

ton/y kg/y gal/y

Brown Grease Production

ton/y kg/y gal/y

Purified Biodiesel Production

ton/y kg/y MJ/y gCO2/MJ

GHG Emissions from Biodiesel

mtCO2/y tonCO2/y gCO2/MJ

Displaced GHG Emissions from Low-Sulfur Diesel

-

4.0%

4.0%

14,976,000

-

-

14,976,000

29,952,000

62,450

-

-

62,450

124,900

56,772,655

-

-

56,772,655

113,545,455

-

21%

31%

31%

31%

-

337,349

224,592

224,592

1,349,398

-

1,400

932

932

5,600

-

1,272,727

847,323

847,323

5,090,909

4.0%

21.%

31.%

4.40%

5.16%

14,976,000

337,349

224,592

15,200,592

31,301,398

62,450

1,400

932

63,382

130,500

56,772,655

1,272,727

847,323

57,619,977

118,636,364

599,040

70,843

69,623

668,663

1,614,720

2,498

294

289

2,787

6,732

2,270,906

267,273

262,670

2,533,576

6,120,000

485,222

57,383

56,395

541,617

1,307,924

2,023

238

234

2,257

5,453

1,839,434

216,491

212,763

2,052,197

4,957,200

69,530,605

8,183,356

8,042,430

77,573,035

187,382,160

34

22

21

33

31

2,620,875

198,297

186,664

2,807,539

6,363,279

2,888,204

218,523

205,704

3,093,908

7,012,333

93

93

93

93

93

7,161,154

842,827

828,313

7,989,467

19,299,021

7,891,592

928,795

912,801

8,804,393

21,267,521

63.4%

76.5%

77.5%

64.9%

67.0%

% gal/y

Annual Raw Grease Feed Rate

-

% gal/y

SSG Annual Feed Rate

4.0%

mtCO2/y tonCO2/y %

Percent Reduction


Energy & Fuels 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Page 26 of 34

25 470

FIGURES

471 472

Figure 1. Schematic of Waste Grease Sampling and Grab Sample Processing

473


Page 27 of 34 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Energy & Fuels

26

474 475 476

Figure 2. Grease Trap Waste Sampling. A) Polypropylene tank showing defined layers; B) View of top surface; C) Cross-section of floating solids after scooping from tank


Energy & Fuels 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Page 28 of 34

27

477 478 479 480

Figure 3. Sewage Scum Grease Waste Sampling. A) Primary tank next to sewage scum concentration building; B) Sampling of Sewage Scum Grease in Scum Concentration Building; C) Scum concentration tank

481


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Energy & Fuels

28

482 483

Figure 4. Grease Lipid Extraction Procedure.


Energy & Fuels

29

Separated Lipid Layer in Tank (%) Wastewater Layer in Tank (%)

Separated Floating Layer in Tank (%) Sediment Layer in Tank (%)

100 90 80 Post-GLE Fractionation in Special Tank ( %)

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Page 30 of 34

70 60 50 40 30 20 10 0

484 485 486 487

Sampling Date

Figure 5. Separation of GTW into layers by settling at ambient temperature. The floating grease layer is shown as two layers: 1) the amount of extractable lipids (orange) and 2) the rest of the floating wet solids (tan).

488


Page 31 of 34

30

B)

15000

0.5

10000

0.4

7500 5000 2500

COD (25%~75%) 5%~95% Median Line

490 491

0.3 0.2 0.1

Total Solids (25%~75%) 5%~95% Median Line

0.0

0

489

0.6

12500 Total Solids (%)

A)

COD (mg/L)

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Energy & Fuels

GTW Tank Water

GTW Tank Water

Figure 6. Quality of Wastewater Sampled from GTW Tank for A) Chemical oxygen demand (COD) and B) Total solids.


Energy & Fuels

31 140 Avg Lipid

Avg WW

Avg Wet Residual Solids

120

%Component in Sewage Scum Grease

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Page 32 of 34

100

80

60

40

20

0

492 493 494

Month of Study

Figure 7. Separation of SSG averaged by month of the study. Composition is greater than 100% due to sulfuric acid added to samples after starting mass was taken.


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Energy & Fuels

32

495 496 497 498

Figure 8. Scum Concentration Building Water Quality Testing of Underwater (UW), Filtered water (FW), and Extraction water (EW) for A) Chemical oxygen demand (COD), B) Total Kjeldahl nitrogen (TKN), C) Total solids, D) Volatile solids, E) Ammonia, F) pH, G) Conductivity, and H) Alkalinity.


Energy & Fuels

33

1.0 0.9

Float

0.8

Raw GTW

B) 0.50 0.45

0.7 0.6 0.5 0.4 0.3 0.2 0.1

Frequency/Number of Samples


A)

SSG GTW

0.40 0.35 0.30 0.25 0.20 0.15 0.10 0.05

0.0

0.00

GTW %Lipids

C)

%FFA

0.7

D)

0.6

Warm 0.6

Temp Cool

0.5 0.4 0.3 0.2 0.1 0

499 500 501 502

Warm Frequency/Number of Samples


1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Page 34 of 34

0.5

Temp Cool

0.4 0.3 0.2 0.1 0

SSG %Lipids

SSG %FFA

Figure 9. Histogram of (A) Lipid content of settled GTW float layer and raw GTW, (B) Free fatty acid (FFA) content of GTW and SSG, (C) Lipid content of SSG by season, and (D) FFA content of SSG by season.

503


Longitudinal Study of Wastewater Greases and Their Potential for the

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