Life-Cycle Assessment of Biodiesel Produced from Grease Trap

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Life Cycle Assessment of Biodiesel Produced from Grease Trap Waste Megan E. Hums, Richard Cairncross, and Sabrina Spatari Environ. Sci. Technol., Just Accepted Manuscript • DOI: 10.1021/acs.est.5b02667 • Publication Date (Web): 26 Jan 2016 Downloaded from http://pubs.acs.org on February 10, 2016

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Environmental Science & Technology

ACS Paragon Plus Environment


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Life Cycle Assessment of Biodiesel Produced from

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Grease Trap Waste

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Megan E. Hums1, Richard A. Cairncross1*, & Sabrina Spatari2 1

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Chemical and Biological Chemical Engineering, Drexel University

Civil, Architectural, and Environmental Engineering, Drexel University

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KEYWORDS: Grease Trap Waste, Fats, Oils, and Greases (FOG), Biodiesel, attributional and

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consequential LCA, Renewable Energy, GHG emissions

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ABSTRACT: Grease trap waste (GTW) is a low-quality waste material with variable lipid

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content that is an untapped resource for producing biodiesel. Compared to conventional biodiesel

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feedstocks, GTW requires different and additional processing steps for biodiesel production due

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to its heterogeneous composition, high acidity, and high sulfur content. Life cycle assessment

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(LCA) is used to quantify greenhouse gas emissions, fossil energy demand, and criteria air

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pollutant emissions for the GTW-biodiesel process where the sensitivity to lipid concentration in

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GTW is analyzed using Monte Carlo simulation. The life cycle environmental performance of

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GTW-biodiesel is compared to that of current GTW disposal, the soybean-biodiesel process, and

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low sulfur diesel (LSD). The disposal of the water and solid wastes produced from separating

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lipids from GTW has a high contribution to the environmental impacts; however, the impacts of

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these processed wastes are part of the current disposal practice for GTW and could be excluded

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with consequential LCA system boundaries. At lipid concentrations greater than 10%, most of

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the environmental metrics studied are lower than those of LSD and comparable to soybean-

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biodiesel.

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INTRODUCTION


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Biodiesel is a renewable fuel that can be produced from a variety of feedstocks including

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vegetable oils, animal fats, waste greases, and algal oil. Commercial biodiesel is produced

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mostly from refined plant oils1. However, vegetable oils are an expensive feedstock and account

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for 70-88% of biodiesel production cost2. The use of waste greases, such as brown grease

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extracted from grease trap waste (GTW), as biodiesel feedstock offers a low-cost alternative to

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refined vegetable oils3. Additionally, recovery of brown grease into a value-added product

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represents an opportunity to “recapture” and recycle waste streams from the food industry.

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GTW is an effluent from commercial kitchen wastewater that is collected in grease

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interceptors to avoid sewer blockages4. GTW is a combination of fats, oils, and greases (FOG),

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water, and solids with highly variable composition3, 5. FOG, or brown grease, is the lipid portion

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of GTW that can be extracted and converted into biodiesel. The quantity of lipids in GTW varies

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depending on the source and GTW management practice3, 6, and GTW generation ranges from

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1,406-11,000 kg/yr/restaurant with a range of 0.1-40% lipid content3, 5, 7. In the United States, an

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estimated 1.8 billion kg/yr of lipids could be recovered from GTW4 which could produce about

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1.3 billion kg of biodiesel/yr assuming conversion data from the process model used in this

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paper. The variability of the generated GTW amount and lipid content make GTW a complex

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feedstock for biodiesel production as compared to conventional biodiesel feedstocks.

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GTW disposal methods vary based on location and municipal regulations3. Common practices

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are to dispose of GTW at a landfill, incinerator, or anaerobic digester8. The degradation of

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organic material in a landfill emits methane gas (biogas) which is a more potent greenhouse gas

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than carbon dioxide9, 10 and is also accounted with fossil GHG emissions unlike biogenic CO2;

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the collection and use of this biogas can benefit waste disposal facilities. Landfill gas collection

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and anaerobic digestion offer ways to reduce the emission of the methane gas by flaring or co-


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generating heat and electricity; converting the methane to biogenic carbon dioxide thus considers

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the carbon to be short-lived unlike fossil CO2 a emission11. Recent laboratory and LCA research

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has focused on anaerobic co-digestion of GTW with residual biosolids such as sewage sludge8, 12-

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wastewater has been reported to produce more usable energy than anaerobic digestion alone15.

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Also, the variability of the feedstock could affect the microbiological activity in the anaerobic

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digester which is sensitive to changes in feedstock composition particularly long-chain fatty

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acids15. Separating lipids from GTW for biodiesel production has several potential benefits for

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waste management facilities including reducing the volume of GTW that is processed for

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disposal and replacing petroleum diesel combustion by a renewable fuel.

; however, extracting the lipids for biodiesel production and anaerobic digestion of the

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Life Cycle Assessment (LCA) has been used to estimate the life cycle impact assessment

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(LCIA) metrics for biodiesel produced from a variety of feedstocks. Dufour and Iribarren

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performed LCA on biodiesel production from inedible and low-quality biodiesel feedstocks such

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as sewage sludge and used vegetable oil. They showed that the production of biodiesel from

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used vegetable oil and from sewage sludge reduced greenhouse gas (GHG) emissions by 79.7%

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and 24.5%, respectively as compared to low sulfur diesel16. The sewage sludge GHG reduction

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is smaller because 10,000 kg of sewage sludge was needed to be processed to produce 1000 kg

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of biodiesel whereas only 1205 kg of waste vegetable oils is needed for 1000 kg of biodiesel16.

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The variability of lipid concentration in sewage sludge is similar to that of GTW; the lower the

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lipid content, the greater volume of starting waste material is needed to produce the same amount

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of fuel.

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greenhouse gas emissions of GTW-biodiesel processing and performed Monte Carlo simulation

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for a sensitivity analysis. They found that the GTW-biodiesel process had potentially lower

Tu and McDonnell recently published an analysis of the life cycle energy and


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greenhouse gas emissions and energy usage than conventional fuels primarily when anaerobic

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digestion was used for waste disposal17. However, this analysis relies on literature for the GTW-

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biodiesel production and excludes the biodiesel purification step necessary for compliance with

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ASTM-grade biodiesel.

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Life cycle assessment (LCA) is a systematic framework for examining the implications of

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products, processes, and activities, using specific metrics through life cycle impact assessment

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that approximate environmental damages18. This research focuses on the production of biodiesel

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from GTW utilizing a process model created from laboratory data from our recent reactor and

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purification research. The LCA includes the entire life cycle of the fuel from the collection of

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the GTW feedstock to the combustion of the biodiesel in a vehicle. This paper also includes a

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parametric study on lipid content of the GTW to analyze the 100-year global warming potential,

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fossil cumulative energy demand, and selected air pollution emissions associated with the

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combustion of the fuel.

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RESEARCH SCOPE AND METHODS

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Goal and Scope

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LCA was used to evaluate the energy and selected LCIA metrics of producing and combusting

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1 MJ of biodiesel (the functional unit) from GTW. The LCIA metrics analyzed were midpoint

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life cycle impact assessment metrics of 100-year global warming potential (GWP100)11, fossil

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cumulative energy demand (CEDfossil)19, and the criteria air pollutant emissions20:

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monoxide (CO), mono-nitrogen oxides (NOx), sulfur oxides (SOx), and particulate matter (PM).

carbon

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LCA can be performed using an attributional or a consequential framework. Attributional

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LCA is used to determine the total emissions from the process21, 22 whereas consequential LCA is

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used for analyzing the change in emissions which is due to a change in process for handling


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GTW21, 22. In this paper, an attributional LCA of the GTW-biodiesel process was first used to

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determine the LCIA metrics of the entire biodiesel production process including the

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transportation of GTW from restaurants to the grease hauler’s aggregation location (transfer

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station), separation of GTW lipids, disposal of GTW wastewater and waste solids, conversion of

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lipids into fuel, and operation of fuel in a vehicle. Despite lipid separation, there is still a large

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volume of wastewater and solids that need to be disposed of, which leads to high greenhouse gas

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emission17; therefore, a consequential LCA was also used to examine the GWP100 of the GTW-

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biodiesel process and to compare it with current disposal of the same amount of GTW (lipids

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not separated for biodiesel production). GTW disposal at a landfill was chosen; other waste

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disposal options such as incineration and anaerobic digestion are outside the scope of this paper

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and will be addressed in a future study. Lastly, the sensitivity of LCA impacts due to the

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variability of lipid content in the GTW-biodiesel process was evaluated using a Monte Carlo

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simulation.

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This study utilized various tools for the LCA to determine the LCIA metrics of the fuel

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processes. SimaPro823 and the EcoInvent database20 were used to analyze the impacts for the

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GTW-biodiesel production except for the natural gas used for steam production. GREET-201424

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was used to determine the life cycle impacts of natural gas used for steam production, the

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soybean-biodiesel process, the LSD process, and vehicle operation because GREET is specific

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towards the production and combustion of these fuels in the United States. Oracle Crystal ball25

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was used to run a Monte Carlo simulation for the sensitivity of lipid content on the LCIA

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metrics.

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System Boundary

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Three fuel production processes and one comparative process were studied for the production

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of biodiesel from GTW (Figure 1), current GTW disposal, biodiesel from soybeans, and LSD.

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The system boundary of the life cycle model of the fuel production process included three stages:

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1) Pre-treatment, 2) fuel production, and 3) vehicle operation. A full system boundary figure is

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shown in the supporting information (SI).

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GTW-Biodiesel Process Description

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A process model was created using laboratory data26, 27 and unit operation material balances

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from design projects28-30 to estimate energy and material requirements. The process model

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simulated a GTW-biodiesel plant with a capacity for producing 840 L/day of biodiesel and

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analyzed the material and energy requirements for the GTW-biodiesel process. The model

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included the extraction of grease lipids from GTW, conversion of lipids into biodiesel with

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methanol recycling, washing of crude biodiesel, and purification using vacuum distillation (see

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SI for more details).

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GTW Transportation to Transfer Station

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The GTW was delivered to the transfer station in a 16 metric-ton truck with a round-trip

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transportation distance of 286 km using data collected from routes traveled by a grease hauler

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collecting GTW31.

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Pre-treatment

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The GTW pre-treatment stage included two sub stages: oil extraction (separation of lipids) and

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waste management (WM).

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Oil Extraction


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The oil extraction separated the lipids from the remaining GTW (floating solids, wastewater,

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and sediments) with heating to liquefy the lipids for faster separation. The volumetric balance of

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the GTW lipids was varied from 1-40%, wastewater 25-64%, and the floating solids and

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sediments were kept constant at 10% and 25%, respectively.

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Pre-treatment Waste Management

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The waste management included the transportation and treatment of GTW wastewater (GTW-

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WW) at a wastewater treatment plant as well as the transportation and disposal of GTW waste

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solids (GTW-WS) at a landfill. The transportation distance of both materials was 50 km in a 16

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metric-ton truck.

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The GHG emissions associated with the landfill was estimated using landfill data of food waste

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from the EPA. Food waste data were used for the GTW-WS because the GTW comes from

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kitchen waste32. The landfill gas emitted was analyzed using two methods: A) Flaring and B)

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Co-generation for electricity and heat production. The co-generation products were treated as

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avoided emissions of electricity and natural gas.

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Fuel Production The fuel production stage contained four sub-stages:

conversion, purification, waste

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management, and service station. A co-product “bio-bunker” was produced which is similar to a

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heavy fuel oil.

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Conversion

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The lipids were reacted using a bubble column reactor that has been developed by researchers

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at Drexel University26 and was inspired by experiments done by Kocsisová et al.33. The lipids

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contained 97% free fatty acids which was the typical amount found at the time of creating the

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process model. Oleic acid was used to represent the free fatty acids because it is the most


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prevalent fatty acid in waste oils8, 34. Transesterification was not included due to the high level

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of free fatty acids. The esterification was performed at atmospheric pressure and 120 ⁰C for 2

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hrs utilizing 0.5% w/w sulfuric acid as catalyst and 4.5 molar ratio of methanol to lipids.

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Methanol was recovered and recycled to the reactor using a partial condenser and a distillation

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column.

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Purification

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The crude biodiesel was first neutralized and water washed and then distilled in a short-path

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evaporator for further purification and sulfur reduction.

GTW-biodiesel has a high sulfur

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concentration because the sulfur content of GTW lipids is 200 PPM on average. Vacuum

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distillation is necessary to reduce the sulfur concentration35 to meet the 15 PPM sulfur

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specifications for on-road biodiesel36. The evaporator was run at 0.1 bar and 260 °C. This

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condition is conservative compared to more recent experimental data (performed after this

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analysis) operating at 1 mbar and 190 °C with optimization ongoing. Therefore, the energy

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demand for the evaporator in this paper is likely higher than necessary. This stage is essential for

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sulfur reduction and was not considered by Tu and McDonnell17.

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The high-boiling point material remaining after distillation was described as “bio-bunker.”

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Because allocation methods can change the life cycle impact of a process37 and the

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mass/volume/energy of bio-bunker compared to biodiesel is small, no allocation method was

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applied to the LCA. Thus, the GTW-biodiesel LCIA metrics estimated in this analysis are

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conservative. Potentially, future work could assign an allocation to the bio-bunker, or it could be

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evaluated as a substitute for industrial use of natural gas or of heavy fuel oil.

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Fuel Conversion Waste Management


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The wastewater produced from the methanol recovery and water washing was transported 50

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km by a 7.5 metric-ton truck for treatment at a wastewater treatment plant.

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Service Station

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The purified biodiesel was transported 100 km by a 7.5 metric-ton truck to the service station.

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Service station operations were evaluated according to SimaPro8.

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Vehicle Operation

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The use and end-of-life of biodiesel was combustion in a vehicle. The CO2 credit for biodiesel

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was represented in the fuel’s combustion.

The CO2 produced from biogenic sources was

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considered zero because of the recent sequestration of carbon from the atmosphere as opposed to

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that of petroleum fuels38. Biodiesel from oleic acid is treated as methyl oleate, which consists of

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19 carbons: 18 renewable, biogenic carbon atoms from the lipids and one non-renewable, non-

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biogenic carbon from the methanol. Therefore, the biodiesel combustion emissions from non-

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renewable sources are 1/19th of the total CO2 combustion emissions.

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The vehicle operation of the GTW and soybean biodiesel were evaluated in the same way

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using GREET-201424 vehicle combustion data combined with biodiesel emission data reported

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in a review by the EPA. The EPA performed a study and fit an emissions curve to data from a

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compilation of literature reports on the engine combustion of biodiesel for CO, NOx, and PM

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which was then represented as a percent change from LSD39. The sulfur content of the fuel was

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used to determine SOx emissions during vehicle operation.

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Comparative Scenarios

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The current GTW disposal represented the transportation of GTW to the transfer station,

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gravity settling of the GTW where GTW-WS and lipids were dewatered and sent to the landfill

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while the GTW-WW was sent to wastewater treatment plants. The LCIA metrics associated


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with the transportation, wastewater treatment, and landfill were treated the similarly to the GTW-

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biodiesel process description. It was assumed that the emissions associated with the GTW

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gravity settling are negligible.

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Soybean-biodiesel and low sulfur diesel life cycle assessments were reported using the

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GREET-2014 fuel processes24. Details on these processes can be found in the SI.

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Life Cycle Inventory (LCI)

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A process-based LCI model was developed from sequential material and energy balances

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following ISO methods18. Data were used to create an inventory of the materials and utilities

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required for producing 1 MJ of biodiesel from GTW, shown in the SI.

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Uncertainty/Model Fitting

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A model was developed to analyze and test trends in the LCIA metrics of producing biodiesel

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from GTW with varying lipid contents shown in equation 1. The derivation of the equation is

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found in the SI.

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= ∆ + ∆ + ∆ (1)

Where,

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Îi = LCIA metric intensity of process stage i per unit mass of input (PT = pre-treatment, FP = fuel

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production, and VO = vehicle operation: combustion emissions)

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∆ĤC = heat of combustion of biodiesel (lower heating value, MJ/kg)

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Φ = yield of fuel production process

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x = lipid content

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EBiodiesel = energy content of biodiesel produced


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This equation predicts that the total LCIA metrics are linearly proportional to the reciprocal of

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the GTW lipid content, 1/x. Linear regression of Equation 1 to the LCIA metrics versus 1/x was

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used to estimate the slope and intercept.

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Sensitivity to GTW Composition and Monte Carlo Simulation

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Monte Carlo simulation was used to test the effects of lipid variability on GWP100 and other

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LCIA metric metrics. A lognormal distribution function was fit to lipid percentages that were

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found during a longitudinal study of GTW composition31. LCIA metrics were found by utilizing

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the fitted equation described in the previous section where lipid content, x, was varied using the

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distribution curve. Oracle Crystal Ball was used to determine the distribution curve of the lipids

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running Monte Carlo in 5000 trials.

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The GTW-biodiesel process is also sensitive to the FFA content of the GTW lipids. The

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process model assumed a 97% FFA content representative of laboratory data collected at the

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time of its development. A scenario of using a lower FFA content was examined and is shown in

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the SI. Tu and McDonnell also performed a sensitivity analysis on lipid content, FFA content,

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and other anaerobic digestion conditions17.

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emissions was due to changes in lipid concentration; therefore, the Monte Carlo analysis in this

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paper is only applied to lipid content sensitivity.

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RESULTS & DISCUSSION

They found the greatest variability for GHG

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The parametric life cycle results represent the LCIA metrics for 1 MJ of biodiesel produced

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from GTW with a lipid content of 2-40%. The GWP100, CEDfossil, and selected air emissions for

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the GTW-biodiesel process were studied and compared to soybean-biodiesel and LSD and are

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shown in detail in the SI.

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100-year Global Warming Potential


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Attributional LCA Approach for GTW-Biodiesel

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The life cycle GWP100 of GTW-biodiesel by process stage, with lipid content ranging from 2-

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40% is evaluated in two scenarios for treatment of the landfill gases from disposal of the GTW-

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WS, flaring (Figure 2A) and co-generation of heat and electricity (Figure 2B). The impact due

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to the waste management of the pre-treatment is presented separately from the rest of the pre-

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treatment (steam production and electricity) because of its large contribution to the emissions.

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For lipid contents less than 10%, the GWP100 of the GTW-biodiesel process is dominated by the

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emissions from delivery of the GTW to the transfer station and the pre-treatment waste

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management (pre-treatment WM; the transportation and treatment of GTW-WW and GTW-WS).

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As lipid content decreases, there is an increased amount of GTW transported to the transfer

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station and an increase in waste sent for disposal which contributes to the higher GWP100. The

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impact from the pre-treatment without WM and the fuel process stages are all dominated by

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combustion of natural gas for steam production; as the lipid content increases, there is less

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natural gas consumed to produce steam to separate the lipids, which leads to a lower GWP100.

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The difference in the total GWP100 between the flaring and co-generation scenarios is smaller

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at higher lipid contents. The flaring scenario has 11% higher total GWP100 compared to co-

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generation (difference of 3 g-CO2-eq/MJ-fuel) at 40% lipid content. At 2% lipid content, the

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flaring scenario has 28% higher total GWP100 (difference of 64 g-CO2-eq/MJ-fuel). The benefits

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of co-generation are the avoided GWP100 from the electricity and natural gas (shown as a credit -

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negative contribution to GWP100 in Figure 2B) which causes lower GWP100 for the co-generation

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scenario.

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The curves in Figure 2 are a correlation based on a linear regression of Equation 1 to the

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GWP100 results (equation shown in each graph). In the correlation, the constant (~18 g-CO2-


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eq/MJ-fuel) represents the GWP100 of the fuel production and vehicle operation which is

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independent of lipid content because the feedstock volume is adjusted to have the lipid volume

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needed to produce 1 MJ of biodiesel. When the lipid content is below 5.5% and 3.7% in the

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flaring and co-generation scenarios, respectively, the net GWP100 for GTW is higher than that for

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LSD (93-gCO2-eq/MJ-fuel). The flaring and co-generation scenarios results show that for lipid

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contents above 6%, without accounting for fuel offsets, the net GWP100 of the proposed GTW-

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biodiesel process are lower than the LSD.

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Consequential LCA Approach for GTW-Biodiesel Process v. Current GTW Disposal

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When accounting for the avoided emissions for replacing current GTW disposal with the

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proposed GTW-biodiesel, the consequence or change leads to reduced LCIA metrics for all lipid

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contents of GTW studied. In Figure 3, the GWP100 associated with current GTW disposal and

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offset LSD consumption are treated as avoided emissions. The avoided emissions are treated as

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a credit because both offset LSD and current GTW disposal are not needed when the GTW-

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biodiesel is implemented. In Figure 3B, the co-generated heat and electricity associated with

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biodiesel production (teal) is treated as a credit; however, the co-generated heat and electricity

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associated with the current GTW disposal (teal stripes) is treated as a penalty – the difference

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between these two contributions in Figure 3B is the net change in avoided co-generation utilities

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when converting from current GTW disposal to the proposed GTW-biodiesel process. The net

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difference between the co-generated avoided utility emissions in the current GTW disposal and

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GTW-biodiesel process avoided utilities is equal to the co-generation impacts associated with the

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lipids (5.5 g-CO2eq/MJ-fuel avoided utility). Tables for the current GTW disposal and the

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biodiesel production are in Table SI-S6 and Table SI-S7 in the SI.


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In both waste solid treatment scenarios, the total GWP100 associated with the GTW-biodiesel

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process is always higher than the current GTW disposal because of additional GWP100 associated

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with the biodiesel conversion and purification processes. However, the net GWP100 is negative

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for both scenarios and all lipid contents when accounting for avoided emissions of LSD and

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current GTW disposal.

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In Figure 3, at low lipid contents, the largest impacts are due to waste management: positive

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contribution of pre-treatment waste management in the GTW-biodiesel process (orange with

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blue dots) and negative contribution of avoided waste management of the current GTW disposal

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(orange hashed). The difference in impacts between current GTW-disposal (avoided) and pre-

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treatment waste management is due to the lipids that are removed from GTW during pre-

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treatment and is independent of lipid content for the chosen functional unit (1 MJ biodiesel

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corresponds to a constant amount of lipids). The GWP100 difference between the current GTW

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disposal and the waste disposal from biodiesel production is 7.7 and 4.5 g-CO2-eq/MJ-fuel for

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flaring and co-generation, respectively. The co-generation has a smaller difference because of

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the reduction in avoided electricity and heat when the lipids are removed from the waste

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treatment (-5.5 g CO2-eq/MJ-fuel).

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The removal of the lipids and production of biodiesel results in avoiding LSD (93 g-CO2-

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eq/MJ-fuel). In the attributional LCA analysis of Figure 2, the GWP100 for the GTW-biodiesel

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process is lower than GWP100 of LSD for lipid contents above 5%.

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consequential analysis of Figure 3, which represents replacing current GTW disposal and LSD

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use with the GTW-biodiesel process, the net emissions (black bar) are negative for all lipid

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contents studied.

However in the


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The GTW-biodiesel process has a better net GWP100 making fuel production more favorable

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than current GTW disposal. A new system boundary is proposed to omit the impacts associated

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with the current GTW disposal. For the remaining LCIA metrics we include the “without waste

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management” scenario (w/o WM) where the pre-treatment WM and the transportation of GTW

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to the transfer station are omitted in the LCA of the GTW-biodiesel process which better

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represents the effect of implementing a GTW-biodiesel process in the current waste management

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system.

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Sensitivity to GTW Composition and Monte Carlo Simulation

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The yield of biodiesel produced from GTW is sensitive to the composition of GTW – both the

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lipid content of GTW and the percent FFA of the lipids extracted from GTW. The variability of

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percent FFA in lipids extracted from GTW is relatively small (70% to 98%) compared to the

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variability in lipid content (0.15-65%); so the variability in percent FFA has a smaller effect on

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the LCA impacts17. The SI includes a table showing the sensitivity of LCA impacts to FFA

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content; decreasing FFA from 97% to 80% causes a 10%-20% increase in GWP100 for the

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consequential LCA depending upon the lipid content. Future research will include sensitivity

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analysis for FFA content and incorporate a two-step biodiesel conversion process for both FFA

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and acyl glycerides.

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The Monte Carlo analysis results presented in Figure 4 correspond to three scenarios: 1)

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attributional LCA with landfill gas flaring, 2) attributional LCA with landfill gas co-generation,

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and 3) LCA omitting impacts that are from GTW waste management (impacts associated with

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GTW transportation to the transfer station and waste solid and wastewater disposal). To evaluate

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the sensitivity that lipid content has on the LCIA metrics, a Monte Carlo simulation was

338

performed using two experimental lognormal distributions of lipid contents for GTW: the results


15

Page 17 of 27


339

in Figure 4A and 4B correspond to a lipid content distribution for raw GTW (named Raw GTW)

340

with median and mean lipid contents of 2% and 5%, respectively and the results in Figure 4C

341

and 4D correspond to a lipid content distribution for partially-dewatered GTW (named

342

Dewatered GTW) with median and mean lipid contents of 27% and 29%, respectively31. Both

343

lipid distributions were truncated between 0.15%-65%. Box plots of the normalized results are

344

shown in Figure 4; each LCIA metric was normalized by dividing the impact by that of LSD. In

345

Figure 4, the LCIA metric of LSD equals one (red lines), and the green lines represent the

346

impacts of soybean biodiesel.

347

statistical results (mean, median, standard deviation, 10 and 90 percentiles, etc. are shown in the

348

SI).

The lipid content distribution functions and a table of the

349

For all but one of the LCIA metrics in Figure 4, the flaring scenario has the highest median

350

value and largest range, and the without waste management scenario has the lowest median value

351

and smallest range. The magnitudes of the LCIA metrics for the raw GTW distribution (Figure

352

4A and 4B) are significantly larger than for the dewatered GTW distribution (Figure 4B and 4C)

353

because raw GTW has a much larger volume and produces more wastes that are landfilled than

354

dewatered GTW. Compared to flaring, the co-generation scenario reduces in median impact by

355

1% for CO, 25% for GWP100 , 25% for NOx, 42% for CEDFossil, and 66% for PM.

356

The SOx emissions in Figure 4 are negative for the landfilling with co-generation scenario due

357

to offset electricity from co-generation, and raw GTW has more negative SOx emissions than

358

dewatered GTW.

359

sulfuric acid used in the phosphorous fertilizer. In this analysis the electricity is generated

360

primarily from coal (46%) and other fossil sources (18%) which contribute to high SOx

361

emissions. GTW with a lower lipid content produces a higher volume of waste solids that is sent

Also, the soybean-biodiesel SOx emissions are higher than LSD due to


16


Page 18 of 27

362

to the landfill, degrades to methane, generates electricity that offset grid demand and reduces

363

SOx emissions. This is a case of a co-product (electricity) producing a benefit in one LCIA

364

metric that favors the co-product over the product. A similar model sensitivity was observed for

365

ethanol produced from corn stover, where the authors found improved LCIA results at lower

366

ethanol yields40. However, as noted by the authors, an ethanol biorefinery would never operate

367

at a lower yield in order to optimize offsets from its co-products40. Similarly, a GTW-biodiesel

368

conversion process would be optimized for higher biodiesel yield for economic benefits.

369

Adding a dewatering step dramatically reduces the LCIA metrics because the higher lipid

370

content of dewatered GTW (shown in Figure 4C and 4D) requires less pre-treatment process

371

energy and contains a lower volume of solid wastes for disposal.

372

dewatering reduces the median value of the LCIA metrics by about 80%. In co-generation, the

373

median values of all LCIA metrics (with the exception of SOx) are reduced 55-80% between raw

374

GTW and dewatered GTW.

375

increase in lipid content in the dewatered GTW distribution, there is less solid waste sent to the

376

landfill resulting in lower electricity credits than in the raw GTW scenario. In general, the

377

environmental burden of producing GTW-biodiesel is highly dependent on the lipid content of

378

the GTW; if the lipid content is below 10%, LCIA metrics increase hyperbolically (specifically

379

shown for GWP100 in Figure 2 and in model equation 1). This result suggests that a grease

380

dewatering process should be employed to concentrate the lipids prior to heating.

For flaring scenarios,

Alternatively, SOx shows a slight increase because with the

381

The LCIA metrics for the GTW-biodiesel process without GTW waste management are similar

382

to that of soybean-biodiesel. In the all disposal scenarios with dewatered GTW, the GWP100 and

383

CEDFossil results for GTW-biodiesel are also comparable to published LCA results on biodiesel

384

produced from other waste materials and inedible feedstocks such as jatropha. In this paper, the


17

Page 19 of 27


385

average GWP100 of biodiesel produced from dewatered GTW for flare, co-gen, and w/o WM

386

scenarios are 37, 31, and 22 g-CO2-eq/MJ-fuel. Published GWP100 for waste cooking oil,

387

sewage sludge, and jatropha biodiesels are approximately 18, 20 and 35 g-CO2-eq/MJ-fuel16,41.

388

In this paper, the average CEDFossil for the GTW flare, co-gen, and w/o WM are 0.41, 0.34, and

389

0.33 MJ/MJ-fuel.

390

biodiesels are approximately 0.2, 0.8 and 0.6 MJ/MJ-fuel, respectively16,41. The GWP100 and

391

CEDFossil for biodiesel produced from GTW were determined by Tu and McDonnell. GWP100 for

392

scenarios with and without anaerobic digestion are approximately 12 and 40 g-CO2-eq/MJ-fuel,

393

respectively and the CEDFossil are approximately 0.3 and 0.6 MJ/MJ-fuel, respectively17. More

394

information on the comparison to Tu and McDonnell is in the SI.

Published CEDFossil for waste cooking oil, sewage sludge, and jatropha

395

There is a trade-off between using vegetable oil such as soybean oil versus GTW lipids as

396

feedstocks for biodiesel production. For vegetable oils, the LCIA metrics are largely due to the

397

pre-treatment (e.g. soybean grain to soybean oil) and the conversion of vegetable oils to biodiesel

398

is a low energy, low material process. In contrast, the LCIA metrics from the GTW-biodiesel

399

process are primarily due to the fuel production because of the high energy required for reaction

400

and purification into ASTM-grade biodiesel or the waste treatment (depending on the system

401

boundary).

402

Because GTW-biodiesel is produced from a waste source, it is important not only that GTW-

403

biodiesel have environmental impacts comparable to or better than soybean-biodiesel and LSD,

404

but also that it should be a better alternative to current GTW disposal techniques.

405

consequential LCA boundary demonstrates that while producing GTW-biodiesel increases GHG

406

emissions in the waste management system by 13-43 g-CO2-eq/MJ-fuel, it reduces GHG

The


18


Page 20 of 27

407

emissions by 20-75% (Figure 3) when displacing LSD in the fully expanded system with flaring

408

or co-generating electricity for every MJ of biodiesel produced.

409

ASSOCIATED CONTENT

410

Supporting Information

411

This material is available free of charge via the Internet at http://pubs.acs.org. The SI consists of

412

11 figures, 37 tables, and 11 equations. The content of this document includes: full system

413

boundaries, process model description and full life cycle inventory, in-depth landfill gas analysis,

414

all selected LCIA metric results for full range of lipid contents, effect of FFA content in GTW

415

lipids, and statistical results for Monte Carlo simulations.

416

Corresponding Author

417

*E-mail: [email protected], Phone: 215-895-2230

418

Notes

419

The authors declare no competing financial interest.

420

Funding Sources

421

The funding for this project comes from the EPA P3 Design Award—SU-83352401, GAANN

422

RETAIN—Award No. P200A100117, and WERF Research Grant—U3R13, Extraction of Lipids

423

from Wastewater to Produce Biofuels.

424

REFERENCES

425 426 427 428 429 430 431 432 433

1. Eisentraut, A. Sustainable Production of Second-Generation Biofuels; International Energy Agency: France, 2010. 2. Haas, M. J.; McAloon, A. J.; Yee, W. C.; Foglia, T. A., A process model to estimate biodiesel production costs. Bioresource technology 2005, 97, (4), 671-8. 3. Wiltsee, G., Waste Grease Resources in 30 US Metropolitan Areas. In BioEnergy '98: Expanding BioEnergy Partnerships, Great Lakes Regional Biomass Energy Program: Madison, Wisconsin, 1998. 4. Ragauskas, A. M. E.; Pu, Y.; Ragauskas, A. J., Biodiesel from grease interceptor to gas tank. Energy Science & Engineering 2013, 1, (1), 42-52.


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5. Ward, P. M., Brown and black grease suitability for incorporation into feeds and suitability for biofuels. Journal of food protection 2012, 75, (4), 731-7. 6. Austic, G., Feasibility Study: Evaluating the profitability of a trap effluent dewatering facility in the Raleigh area. In LLC, P. B., Ed. ECO Collections: Raleigh, NC, 2010. 7. Canakci, M., The potential of restaurant waste lipids as biodiesel feedstocks. Bioresource technology 2007, 98, (1), 183-90. 8. Long, J. H.; Aziz, T. N.; Reyes, F. L. d. l.; Ducoste, J. J., 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), 231-245. 9. Landfilling; EPA: 2012; pp 1-22. 10. Sundqvist, J. O. Life cycles assessments and solid waste; Swedish Environmental Research Institute: Stockholm, Sweden, 1999. 11. Foster, P. V.; Ramaswamy, P.; Artaxo, T.; Berntsen, R.; Betts, D. W.; Fahey, J.; Haywood, J.; Lean, J.; Lowe, D. C.; Myhre, G.; Nganga, J.; Prinn, R.; Raga, G.; Schulz, M.; Van Dorland, R. 2007: Changes In Atmospheric Constituents and in Radiative Forcing; Cambridge University Press: Cambridge, United Kingdom and New York, NY, USA, 2007. 12. Silvestre, G.; Rodriguez-Abalde, A.; Fernandez, B.; Flotats, X.; Bonmati, A., Biomass adaptation over anaerobic co-digestion of sewage sludge and trapped grease waste. Bioresource technology 2011, 102, (13), 6830-6. 13. Razaviarani, V.; Buchanan, I. D.; Malik, S.; Katalambula, H., Pilot-scale anaerobic codigestion of municipal wastewater sludge with restaurant grease trap waste. Journal of environmental management 2013, 123, 26-33. 14. Gough, H. L.; Nelsen, D.; Muller, C.; Ferguson, J., Enhanced Methane Generation During Thermophilic Co-Digestion of Confectionary Waste and Grease-Trap Fats and Oils with Municipal Wastewater Sludge. Water Environment Research 2013, 85, (2), 175-183. 15. Lopez, R. J.; Higgins, S. R.; Pagaling, E.; Yan, T.; Cooney, M. J., High rate anaerobic digestion of wastewater separated from grease trap waste. Renewable Energy 2014, 62, 234-242. 16. Dufour, J.; Iribarren, D., Life cycle assessment of biodiesel production from free fatty acid-rich wastes. Renewable Energy 2012, 38, (1), 155-162. 17. Tu, Q.; McDonnell, B. E., Monte Carlo analysis of life cycle energy consumption and greenhouse gas (GHG) emission for biodiesel production from trap grease. Journal of Cleaner Production 2015. 18. Environmental management -- Life cycle assessment -- Principles and framework. In International Organization and Standardization: 2006; Vol. ISO 14040: 2006, pp 1-20. 19. Frischnect, R.; Jungbluth, N.; Althaus, H.; Bauer, C.; Doka, G.; Dones, R.; Hischier, R.; Hellweg, S.; Humbert, S.; Köllner, T.; Loerincik, Y.; Margni, M.; Nemecek, T. Implementation of Life Cycle Impact Assessment Methods; Swiss Centre for Life Cycle Inventories: Dübendorf, 2007. 20. Jungbluth, N.; Chudacoff, M.; Dauriat, A.; Dinkel, F.; Doka, G.; Faist Emmenegger, M.; Gnansounou, E.; Kljun, N.; Schleiss, K.; Spielmann, M.; Stettler, C.; Sutter, J. Life Cycle Inventories of Bioenergy Data v2.0 (2007); Swiss Centre for Life Cycle Inventories: Dübendorf, CH, 2007. 21. Rehl, T.; Lansche, J.; Müller, J., Life cycle assessment of energy generation from biogas—Attributional vs. consequential approach. Renewable and Sustainable Energy Reviews 2012, 16, (6), 3766-3775.


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22. Brander, M.; Tipper, R.; Hutchinson, C.; Davis, G. Consequential and Attributional Approaches to LCA: a Guide to Policy Makers with Specific Reference to Greenhouse Gas LCA of Biofuels; Ecometrica Press: 2008. 23. SimaPro 8.0.3.14 PhD; PRé Consultants The Netherlands, 2014. 24. Greenhouse Gases, Regulated Emissions, and Energy Use in Transportation Model, Argonne National Laboratory: 2014. 25. Oracle Crystal Ball, 11.1.2.4.000; Oracle 2014. 26. Stacy, C. J.; Melick, C. A.; Cairncross, R. A., Esterification of free fatty acids to fatty acid alkyl esters in a bubble column reactor for use as biodiesel. Fuel Processing Technology 2014, 124, (0), 70-77. 27. Mohammed, M. Mathematical Modeling of a Two-Phase Bubble-Column Reactor for Biodiesel Production from Alternative Feedstocks. Drexel University, Philadelphia, PA, 2011. 28. Lam, A.; Matthew, S.; Melick, C.; Mohammed, M. Technoeconomic assessment of biodiesel production from alternative feedstocks via three processes; Unpublished Senior Design Report. Drexel University: Philadelphia, PA, 2010. 29. Haas, F. M.; Sanchez, J.; Letterle, K. Design of a Waste Cooking Oil Upgrader for BioFuel Processing: Trap Grease Pretreatment; Unpublished Senior Design Report. Drexel University: Philadelphia, PA, 2005. 30. Bucher, L.; DeVitis, D.; Morris, M.; Wallowitch, G. Technoeconomic feasibility study of a brown grease to biodiesel process; Unpublished Senior Design Report. Drexel University: Philadelphia, PA, 2014. 31. Cairncross, R. A.; Olson, M. S.; Spatari, S., Extraction of Lipids from Wastewater to Produce Biofuels: Statistical Variability of Grease Composition and Quantity. In Unpublished Interim Report for WERF, Drexel University: Philadelphia, PA, 2015. 32. Eleazer, W. E.; William S. Odle, I.; Wang, Y.-S.; Barlaz, M. A., Biodegradability of Municipal Solid Waste Components in Laboratory-Scale Landfills. Environmental Science and Technology 1997, 31, (3), 911-917. 33. Kocsisova, T.; Cvengros, J.; Lutisan, J., High-temperature esterification of fatty acids with methanol at ambient pressure. European Journal of Lipid Science and Technology 2005, 107, (2), 87-92. 34. Canakci, M.; Van Gerpen, J., Biodiesel Production from Oils and Fats with High Free Fatty Acids. Transactions of the ASAE 2001, 44, (6), 1429-1436. 35. Gardner, E. R.; Shang, Y.; Yuan, Y.; Gray, D. M. D., Producing Biodiesel from Fat, Oil and Greases (FOG) and Other Waste Material at Wastewater Treatment Plants. In Proceedings of the Water Environment Federation, Water Environment Federation: 2013. 36. Standard Specification for Biodiesel Fuel Blend Stock (B100) for Middle Distillate Fuels. In ASTM D6751-15, ASTM International: West Conshohocken, PA, 2015. 37. Lopez, D. E.; Mullins, J. C.; Bruce, D. A., Energy Life Cycle Assessment for the Production of Biodiesel from Renedered Lipids in the United States. Industrial & Engineering Chemical Research 2010, 49, 2419-2432. 38. Goedkoop, M.; Oele, M.; de Schryver, A.; Vieira, M. SimaPro Database Manual Methods Library; PRé Consultants: the Netherlands, 2008. 39. A Comprehnsive Analysis of Biodiesel Impacts on Exhaust Emissions; EPA420-P-02-001; Assessment and Standards Division, Office of Transportation and Air Quality: 2002.


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40. Spatari, S.; MacLean, H. L., Characterizing Model Uncertainties in the Life Cycle of Lignocellulose-Based Ethanol Fuels. Environmental Science and Technology 2010, 44, 87738780. 41. Kumar, S.; Singh, J.; Nanoti, S. M.; Garg, M. O., A comprehensive life cycle assessment (LCA) of Jatropha biodiesel production in India. Bioresource technology 2012, 110, 723-729.

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22


Transportation Material Flow

Materials/Utilities GTW Delivery

Select Emissions

GTW-BIODIESEL

(1) PRE-TREATMENT

(2) FUEL PRODUCTION (a) Conversion

(a) Oil Extraction Lipids

GTW-Waste Solids

(i) Reaction

(b) Waste Management (i) WS Transportation

(ii) WS Treatment

(iii) WW Transportation

(iv) WW Treatment

(ii) Methanol Recovery

(3) VEHICLE OPERATION

(b) Purification (i) Washing

(ii) Distillation

Production Wastewater

(c) Waste Management (i) WW Transportation

(ii) WW Treatment

Biobunker Biodiesel (d) Service Station (i) Biodiesel Transportation

(ii) Operation

Biodiesel

GTW-Wastewater

Page 24 of 27

529 530 531 532 533

Figure 1. System boundary for the GTW-biodiesel process. Each of the three main stages include the material and energy inputs and emission outputs for 1) Pre-treatment (orange), 2) Fuel production (yellow), and 3) Vehicle operation (gray). Some process stages have a sub-stage marked with letters a-d and some sub-stages have individual steps marked i-iv.

534


23

Page 25 of 27


300

300

200

250 200 150

100

100

50

50

0

0

-50

-50

40%

30%

-150 20%

-150 10%

-100 7%

-100

Flaring: Biodiesel from GTW with Starting Lipid Content 300

300

250

250

200

150

1 = 2.8 + 18 % ' 2 = 1.0

200

100

150

100

100

50

50

0

0

-50

-50

40%

-150 20%

-150 10%

-100 7%

-100 2% 3% 4% 5%

GWP100 g-CO2-eq/MJ-fuel

B)

1 = 4.1 + 18 % ' 2 = 1.0

100

150

2% 3% 4% 5%

GWP100 g-CO2-eq/MJ-Fuel

250

30%

A)

Co-Generation: Biodiesel from GTW with Starting Lipid Content

535 536 537 538 539 540 541 542

GTW Delivery to Transfer Station

Fuel Production

Avoided Natural Gas

Pre-Treatment WM

Vehicle Operation

Total

Pre-Treatment w/o WM

Avoided Electricity

Theoretical Total

Figure 2. Attributional LCA approach for the parametric study on the affect of lipid content on the total GWP100 for GTW-biodiesel process for A) Flared Landfill Gas and B) Co-Generation of Landfill Gas. The stacked bars represent GTW-biodiesel stages: delivery of GTW to transfer station (red), pre-treatment WM (orange with blue dots), pre-treatment without WM (orange), fuel production (yellow), vehicle operation (gray), avoided electricity production from cogeneration (light green), and avoided natural gas from co-generation (teal). The total GWP100 and modeled curve (black line) are also shown.

543


24


450

450

350

350

250

250

150

150

50

50

-50

-50

-350

-450

-450

40%

-350

30%

-250

20%

-250

10%

-150

7%

-150

2% 3% 4% 5%

GWP100, g-CO2-eq/MJ-fuel

A)

Page 26 of 27

Flaring: Biodiesel from GTW with Starting Lipid Content 450

350

350

250

250

150

150

50

50

-50

-50

-350

-450

-450

40%

-350

30%

-250

20%

-250

10%

-150

7%

-150

2% 3% 4% 5%

GWP100, g-CO2-eq/MJ-fuel

B) 450

Co-Generation: Biodiesel from GTW with Starting Lipid Content Current GTW Delivery to Transfer Station

Displaced Current Processes

544 545 546 547 548 549 550 551 552 553

GTW Delivery to Transfer Station

Current GTW Disposal

Pre-Treatment WM

Current GTW Co-Gen Avoided Utilities

Biodiesel Co-Gen Avoided Utilities

LSD

GTW-Biodiesel Rest of Process

Proposed GTW-Biodiesel Process

Total

Figure 3. Consequential LCA approach to compare GTW-biodiesel production to current GTW disposal. GWP100 shown for GTW-biodiesel process for A) Flared Landfill Gas and B) Cogeneration of Landfill Gas. The lipid content of the GTW was varied from 2-40%. The colored negative bars represent avoided impacts including current GTW transportation (red striped), current GTW disposal (orange hashed) and avoided impacts due to Co-generation (electricity and natural gas, blue striped) and LSD (purple). The positive bars represent the GTW transportation (red), GTW-biodiesel process (green) and the no longer avoided impacts (electricity and natural gas) from the current GTW disposal (teal). The total emissions (black bar) represent the difference between total biodiesel process and avoided emissions.


25

Page 27 of 27


C) Low Sulfur Diesel Soybean Biodiesel

8

1.0 ImpactGTW/ImpactLSD

6 4 2

CED

0.4 0.2

GWP100

CO

Raw GTW Distribution

15 0 -15 -30

CG

W/O

F

W/O

CO

3.0 ImpactGTW/ImpactLSD

30

CED

Dewatered GTW Distribution

D)

45

CG

F

W/O

F

CG

W/O

CG

F

W/O

F

CG

CG

W/O

F

GWP100

2.0 1.0 0.0 -1.0

NOx

SOx

Raw GTW Distribution

PM

NOx

W/O

CG

F

W/O

CG

W/O

CG

F

W/O

CG

F

W/O

F

CG

PM

F

-2.0

-45

W/O

ImpactGTW/ImpactLSD

0.6

0.0

0

B)

0.8

F

ImpactGTW/ImpactLSD

10

CG

A)

SOx

Dewatered GTW Distribution

554 555 556 557 558 559 560 561 562 563 564 565 566

Figure 4. Monte Carlo analysis of the sensitivity of six LCIA metrics for several GTW waste management scenarios and two distributions of lipid content. All impacts are normalized to the corresponding impact for low sulfur diesel (LSD). Panels A and C display 100-year Global Warming Potential (GWP100), Fossil Cumulative Energy Demand (CED), and carbon monoxide (CO), and panels B and D display Particulate Matter (PM), mono-nitrogen oxides (NOx), and sulfur oxides (SOx) emissions. Panels A and B display results based on lipid content distributions in raw GTW, and panels C and D display results for dewatered GTW. Scenarios compared are landfill gas flare (F), landfill gas co-generation (CG) and without waste management (W/O). The line in the middle of each box represents the median, the upper half of the box represents the 3rd quartile, and the lower half of the box represents the 2nd quartile. The positive and negative error bars represent the 90% and 10% percentile intervals. The green line represents soybean-biodiesel and red line represents LSD.


26

Life-Cycle Assessment of Biodiesel Produced from Grease Trap

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