10 - Environmental Review - Organics Recycling ProgramProtecting our community's health and the environment by providing solid waste and sewer collection services. www.cmsdca.gov Costa Mesa Sanitary District ….an Independent Special District Memorandum T o: Board of Directors Via: Scott Carroll, General Manager From: Rob Hamers, District Engineer Date: February 25 , 2014 Subject: Environmental Review – Organics Recycling Program Summary In brief, the project consists of having residents utilize an additional trash container solely for organics, which will be taken to the CR&R anaerobic digestion plant in Perris, California, for advanced recycling. The analysis will conclude that the project will not cause change in the environment but will have positive impacts, which will be the result of raising the diversion or recycling rate from approximately 57% to approximately 75%. Under the California Environmental Quality Act (CEQA), a series of procedural and substantive steps must be taken to determine if CEQA applies to a given activity, proposal or action by a lead agency, and if CEQA does apply, said agency must identify potential environmental impacts and methods of mitigating such impacts. On January 23, 2014, staff presented to the Board the environmental review of the proposed organics recycling project, but questions and/or concerns were raised by the public regarding staff’s findings. Therefore, the Notice of Exemption was not filed with the County Clerk. The following is an amended review of the environmental impacts for implementing an Organics Recycling Program in the Costa Mesa Sanitary District service area. S ta ff Recommendation That the Board of Directors identifies the Organics Recycling Program as a “project,” and determines that said project is categorically exempt from further CEQA review pursuant to CEQA Guidelines Section 15038. ITEM NO. 10
Board of Directors February 25 , 2014 Page 2 of 8 Analysis Under the project proposal, there is no change to the tonnage being collected by CR&R as the organics are currently part of the mixed waste stream placed in the containers; however, the tonnage will now be divided into a two -container system as o pposed to the mixed waste containers now being utilized. CR&R estimates that the fleet servicing the District will increase from nine to ten trucks; where six trucks will be used to collect mixed waste carts and four trucks will be responsible for collect ing the organic carts. The fleet servicing the District will be fueled using Renewal Natural Gas (RNG) that is produced from CR&R’s anaerobic digestion facility in Perris. Two trucks are needed at each household, which means the fleet mileage will double from 4,050 miles to 8,100 miles a week. The environmental impact being reviewed by the District consis ts of the additional fleet mileage from CR&R within the District’s service area. Routes from Stanton to Perris are considered part of CR&R’s Perris anal ysis. Noise – Compressed Natural Gas (CNG) and Renewal Natural Gas (RNG) operate in the same manner where the gas is compressed to less than one percent of the volume it occupies at standard atmospheric pressure. It is stored and distributed in hard containers at a p ressure of 200 –248 bar (2,900 –3,600 psi), usually in cylindrical or spherical. Therefore, the data used in environmental analysis to determine the noise level of the Organics Recy cling Program is from CNG refuse trucks. According to Encana Corporation, a leading North American energy producer, the noise decibel for CNG refuse trucks ranges between 60 to 70 decibels (see Attachment A). In addition, a fact sheet from INFORM, a non -profit group that educates the public about the effects of human activity on the environment and public health indicates that CNG refuse trucks reduces noise by as much as 98%. (see Attachment B). Because natural gas refuse trucks have proven to be quiter than deisel trucks and refuse collection in neighborhoods are considered single noise events staff believes the Organics Recycling Program does not exceed noise level ordinances in the City of Newport Beach and the City of Costa Mesa. Infrastructure (S treets) – Staff believes doubling the mileage of refuse trucks driven in CMSD will not increase or speed up the deterioration of street asphalt because like all paved surfaces, street asphalt is susceptible to deterioration due to the long term exposure of the elements such as water, sunlight and chemical/petroleum exposure. More information about these elements is described below. Water – Over time and especially without proper maintenance water penetrates the asphalt, washes out the base underneath it, causing it to crack, break down and collapse . Sunligh t – Oxidation breaks down and dries out the once flexible liquid asphalt that holds t he aggregate together. The causes raveling and shrinking cracks, which water to penetrate beneath the surface. Chemical/Petroleum – The introduction of chemicals to asphalt, such as gasoline and motor oil, can soften the asphalt and cause it to break down more rapidly. Doubling the mileage of CR&R trucks could increase the risk of a petroleum product spillage such as hydraulic fluid. However, this risk is insignificant when compared to the average daily vehicle trips identified in the
Board of Directors February 25 , 2014 Page 3 of 8 City of Costa Mesa’s General Plan. In 2000, the average daily vehicle trip on residential streets is 316,499 and the average daily trip is projected to increase by 22,594 in 2020. In addition, the City is expecting a 20% increase in traffic volume by 2020. Each CR&R truc k contains a spill kit and drivers are train to contain petroleum spills from entering the storm drain. Spills are ab sorbed using absorbe nt products. No asphalt is exempt from deterioration no matter how well it is constructed. Asphalt deterioration beg ins immediately. Even in normal conditions substantial deterioration can begin to take place after three to five years. Typically after five years the asphalt will be begin to turn gray, become brittle and start cracking. Then, water begins entering the cracks that will cause larger cracks and potholes. When street asphalt is maintained and protected well it will maximize its useful lifespan. Industry experts believe sealing the pavement with a quality asphalt or coal tar based sealant is the best way to protect the street investment. In fact, it is recommended by industry experts that new asphalt be sealed within 60 to 90 days of the application (after it has cured, hardened) to begin protecting it from the elements. Traffic – Doubling the mileage of CR&R refuse trucks could cause traffic disruption in and around schools, especially during drop -off and pick up of school children. Staff recommends CR&R trucks schedule collections near schools after schools begin and before sc hool ends. Emissions – CR&R trucks will be powered by Renewable Natural Gas (RNG). RNG works like Compressed Natural Gas (CNG) where the gas is compressed to less than one percent of the volume it occupies at standard atmospheric pressure. It is stored and distributed in hard containers at a pressure of 200 –248 bar (2,900 –3,600 psi), usually in cylindrical or spherical. The emissions from RNG are similar to CNG where fewer part icular matter and nitrogen oxide is emitted into the atmosphere when compared to diesel powered refuse trucks. According to INFORM, CNG refuse trucks reduce Particulate matter by 67%-94% and Nitrogen oxides by 32%-73% (Attachment B). RNG will achieve the same results. Staff believes doubling the mileage of residential refuse trucks will not have an impact to the atmosphere because natural gas is a proven clean fuel that produces less emissions into the atmoshphere and CMSD will be one of the few jurisdi ctions in Orange County that will pick up solid waste with less than three refuse trucks per household . In addition, there is a benefit to RNG that CNG cannot provide. According to South Coast Air Quality Management District (AQMD), RNG will reduce gr eenhouse gas emissions of hydrocarbons , oxides of nitrogen (NOx), carbon monoxide (CO), as well as carbon dioxide (CO 2 ). The chart shown below is from the Air Quality Management District (AQMD) and assigns a value of 75 for the amount of carbon emitted per unit of energy consumed for compressed natural gas (CNG) vehicles that are filled with standard CNG purchased from a vendor. The chart assigns a value of -15 (negative 15) for the amount of carbon emitted per unit of energy consumed for reusable natural gas (RNG)
Board of Directors February 25 , 2014 Page 4 of 8 that is the result of the project itself. The RNG in this case is the byproduct of the anaerobic digestion plant being constr ucted in Perris. When 4,050 miles per week are driven by the current CNG trucks, this level is at the 75 threshold, while the 8010 miles per week that will be driven are considered to be at the -15 threshold. Because the RNG miles have 90 times less carbon intensity than the CNG miles, the proposed doubling of the mileage results in the proposed emissions from the RNG organics program having a better impact on the environment than the current program. Calculations Existing: 9 trucks @ 35 GGE/day CNG = 1,575 GGE/wk CNG GGE – Gasoline gallon eqivalent Proposed: Assume 10 trucks using RNG Assume 2 x fuel based on doubling mileage 10 trucks @ 70 GGE/day RNG = 3,500 GGE/wk RNG Carbon Intensity * 1 *A mo u n t o f ca r b o n e mi t t e d p e r u n i t o f e n e r g y co n s u me d
Board of Directors February 25 , 2014 Page 5 of 8 Per chart CNG = 90 (75 + (-15)) x carbon emissions/unit of energy RNG = 1 x carbon emissions/unit of energy 1 GGE = 120 MJ/gal energy CNG 1,575 x 120 x 90 = 17.0 x 10 6 MJ RNG 3,500 x 120 x 1 = 0.42 x 10 6 MJ (less energy, and less carbon intensity) MJ – Megajoule, which is a unit of energy. Therefore, the rela tive change in using RNG while doubling the mileage is an improvement to the environment because less greenhouse gas is emitted into the atmosphere . Renewable Natural Gas (RNG) – C R&R’s fleet servicing the District will be powered by RNG that is produced from CR&R’s anaerobic digestion facility in the City of Perris. The fleet will not drive from CMSD to Perris to refuel with RNG and then drive back to CMSD and/or Stanton. Instead, CR&R will use mobile transportat ion to transport the RNG from Perris to Stanton where the fleet can refuel (see sample mobile CNG in Attachment C). Piping RNG underground is the ideal method of transportation (e.g. CNG is piped underground), but according to AQMD officials, RNG is not r eady to be piped and to do so needs approval from the State Legislature. AQMD officials believe in a few years there will be legislation that will allow RNG to be piped underground. In the meantime, CR&R is building the infrastructure for piping RNG. Al ternatives – The proposed Organics Recycling Program will require residents to source separate green waste and food scraps and then the material will be delivered to anaerobic digestion (AD) facility in Perris for conversion into RNG and soil amendments. The following are alternatives the District considered for implementing an Organics Recycling Program. 1. Composting facilities – The only facility in Orange County that is permitted by CalRecycle to accept green waste and food scraps is Tierra Verde Industries that leases 39,000 acres of land owned by Orange County Great Parks Corporation in Irvine. The facility is not in operations and there is no set date when the facility will be open. Other food composting facilities in Southern California include: a. Coachella Valley Composting Facility. This facility is owned by Burrtec Industries and is located approximately 126 miles from CMSD. b. Lancaster Reclaimable Anaerobic Composter. This facility is ow ned by Waste Management and is located approximately 101 miles from CMSD. c. Victor Valley Regional Composting. This facility is owned by Athen Services and is located approximately 92 miles from CMSD.
Board of Directors February 25 , 2014 Page 6 of 8 d. Green Energy Facility. This facility includes the anaer obic digestion facility owned by CR&R and its location is approximately 61 miles from CMSD. CR&R’s facility is the closest facility to CMSD that can accept green waste and food scraps. 2. Do not separate organics – Staff looked at keeping solid waste collect ion within CMSD as status quo where organics does not have to be separated for conversion into green energy. Anaerobic digestion is a process where microorganisms break down organic material in the absence of oxygen. The breakdown of organic material can not occur if organics are contaminated from trash materials such as Styrofoam, plastic grocery bags, clothing, broken glass, plaster, dry paint cans, window glass, etc. 3. Waste Management of Orange Food and Organic Recycling facility – In 2010, Waste Manage ment opened a food and organic recycling facility in the City of Orange that can create green energy. However, the facility does not accept green waste and the facility only accepts food scraps from restaurants. This facility is part of UC Irvine’s succe ssful pilot program that diverted 400 tons of food waste. Environmental Betterment – Below are the environmental benefits to the District’s Organics Recycling Program. Landfill The benefit of diverting 75% of waste from the landfill instead of 50% equates to recycling an additional 10,000 tons of solid waste per year. That is 10,000 tons that does not need to be managed and covered at the landfill by Orange County Waste & Recyc le. This reduction in tonnage will save approximately 7.67 acres of landfill space per year and assists in lengthening the life of the landfill system . For instance, Olinda Landfill, located in the City of Brea, is one of three landfills in Orange County. This landfill is expected to close in fifteen years and by that time Orange County will have only two landfills to serve a population of over three million people. In addition, removing organics from the landfill will help reduce methane gas. When biod egradable ma terials such as food scraps and yard trimming are tossed in the garbage and sent to a landfill, those lettuce heads, grass clippings and apples don’t just break down as they would in nature or in a compost pile. They decompose anaerobically, or without oxygen, and in the process create methane, a greenhouse gas (GHG). Methane is 72 times more potent than carbon dioxide (CO2 ) over a 20 -year period, which means every one ton of methane will trap as much heat in our atmosphere as 72 tons of carbon dioxide! Landfills are a top source of methane, and one that could be easily avoided if we stopped landfilling organic materials and started anaerobic digestion .
Board of Directors February 25 , 2014 Page 7 of 8 The District’s Organics Recycling Program can help reduce methane gas at landfills quickly. Methane only stays in the atmosphere around 8 -12 years while carbon dioxide can last for centuries. But methane has a big effect in its short tim. For instance, me thane is responsible for 75% as much warming as carbon dioxide m easured over any given 20 years . This means methane reductions could have an immediate beneficial effect on our climate, faster than comparable reductions to CO2. Climate change is happening at an alarming rate. Leading nations are calling for emissions reductions of 80% by 2050 and others are saying we need to reduce emissions much sooner. This means we only have a few decades to act, so we need to concentrate on greenhouse gas reductions tha t will have an immediate impact. Our short -term climate actions should focus on reducing methane emissions so we can see the quickest benefit and the District’s Organics Re cycling Program can achieve that benefit . Reduce Greenhouse Gas es CalRecycle , the s tate agency that manages California recycling and waste management programs, is encouraging the development of anaerobic digestion technologies that divert organic waste from landfills and comply with the Global Warming Solutions Act of 2006 (AB 32). AB 32 calls for the reduction of greenhouse gases and the use of low carbon fuel s. The greenhouse effect is a natural process that warms the Earth’s surface. When the Sun’s energy reaches the Earth’s atmosphere, some of it is reflected back to space and the re st is absorbed and re -radiated by greenhouse gases. Greenhouse gas emissions include hydrocarbons , methane, oxides of nitrogen (NOx), carbon monoxide (CO), as well as carbon dioxide (CO 2 ). The problem we now face is that human activities, in particularly burning fossil fuels (coal, oil and natural gas), agriculture and land clearing, are increasing the concentrations of greenhouse gases. This is the enhanced greenhouse effect, which is contributing to warming of the Earth. While the reduction of methane gas at the landfills (mentioned above) is helping to reduce the greenhouse effect, so will Renewable Natural Gas (RNG ) that is produced at CR&R’s anaerobic digestion facility. RNG is a low carbon fuel. In a 2011 study of RNG production pathways (PDF), Argonne National Laboratory concluded that all RNG path ways show significantly less greenhouse gas emissions and fossil fuel consumption than conventional fossil fuel natural gas and gasoline . Attached is a copy of the study. Even though the study is referencing animal w aste conversion to RNG, it is the same concept converting organics to RNG. Zero Waste The organics recycling program is a step toward zero waste and achieving sustainability . As the population is growing resources from the environment are becoming limited. Therefore, e ach material must be used as efficiently as possible and must be chosen so that it may either return safely to a cycle within the environment or remain viable in the industrial cycle. Zero Waste can be seen as a solutio n to these needs and a key to the future. Zero solid waste, zero hazardous waste, zero toxic emissions, zero material waste, zero energy waste and zero waste of human resources will protect the environment and lead to a much more productive, efficient, an d sustainable future.
Board of Directors February 25 , 2014 Page 8 of 8 Zero Waste promotes reu se and recycling along with new designs, such as anaerobic digestion, that consider the entire product life cycle. In addition, zero Waste provides an easy to understand stretch goal that can lead to innovative ways to identify, prevent and reduce wastes of all kinds. It strongly supports sustainabilit y by protecting the environment and handling of wastes b ack into the industrial cycle. Conclusion Staff has concluded its preliminary review of the Organics Recycling Program and found the program is in conformance to Section 15060(c)(2) of the California Environmental Quality Act (CEQA) whereas the activity will not result in a direct or reasonably foreseeable indirect physical change in the env ironment. Furthermore, staff finds the program to be catigorcially exempt from further CEQA review under Section 15308 of CEQA Guidelines because the Costa Mesa Sanitary District will have an adopted organic recycling ordinance that will involve procedures for protecting the environment Strategic Plan Element & Goal This item complies wi t h the objective and strategy of Strategic Element 2 .0, Solid Waste and Strategic Goal No. 2.4, Develop strategies for Zero Waste. Legal Review District Counsel has reviewed and provided his comments to this report. Environmental Review The proposed Organics Recycling Program is in conformance to Section 15060(c)(2) of the California Environmental Quality Act (CEQA) whereas the activity will not result in a direct or reasonably foreseeable indirect physical change in the environment. Staff recommends this project be catigorcially exempt from further CEQA review under Section 15308 of CEQA Guidelines because the Costa Mesa Sanitary District will h ave an adopted organic recycling ordinance that will involve procedures for protecting the environment Financial Review There are no financial impacts for preparing this environmental review. Public Notice Process Copies of this report are on file and will be included with the entire agenda packet for the February 25 , 201 4 Board of Directors regular meeting at District Headquarters and on District’s website. Alternative Actions 1. Direct staff to report back with mor e information. Attachments : A: Fueling Change by Encana Natural Gas B: INORM Fact Sheet C: Mobile RNG D: Argonne National Laboratory Study – Waste -to -Wheel Analsysis of Anaerobic Digestion Based Renewal Natural Gas Pathways with the GREET Model
iii CONTENTS ACKNOWLEDGMENTS ........................................................................................................ vi ABSTRACT .............................................................................................................................. 1 1 INTRODUCTION .............................................................................................................. 2 1.1 Fuel Cycle Analysis ................................................................................................... 3 1.2 Fuel Cycle Analysis of Renewable Natural Gas ........................................................ 4 1.3 Scope .......................................................................................................................... 6 2 FUEL CYCLE ANALYSIS OF RENEWABLE NATURAL GAS FROM ANAEROBIC DIGESTION OF ANIMAL WA STE ......................................................... 8 2.1 Biogas Production in AD Pathways and Reference Case .......................................... 9 2.2 Animal Waste Transportation and Operation of Anaerobic Digester ........................ 11 2.3 Application of AD Res idue to Soil ............................................................................ 13 2.4 Biogas Processing for RNG Production .................................................................... 15 2.5 RNG Compression, Liquefaction, Transportation, Distribution and Vehicle Use ................................................................................................................ 16 3 RESULTS ........................................................................................................................... 17 3.1 Energy and GHG Emissions per MJ .......................................................................... 18 3.2 Energy and GHG Emissions per Mile ....................................................................... 22 3.3 Sensitivity Analysis ................................................................................................... 25 4 CONCLUSIONS ................................................................................................................. 27 5 REFERENCES ................................................................................................................... 29 FIGURES 1 Stages in a CNG Pathway ................................................................................................... 4 2 Carbon Cycle of Anaerobic Digestion -Based RNG from Animal Waste ........................... 4 3 Disposition of Animal Waste .............................................................................................. 6 4 System Boundary of Renewable Gas Production from AD of Animal Waste ................... 8 5 Carbon Left in Soil After AD Residue is Applied to Soil .................................................. 14
iv FIGURES (CONT.) 6 WTW Total Energ y Use for AD -B ased RNG Pathways Compared to Conventional NG, Gasoline and Diesel Pathways .............................................................. 19 7 WTW Fossil Fuel Use for AD -B ased RNG Pathways C ompared to Conventional NG, Gasoline and Diesel Pathways .............................................................. 19 8 WTW GHG Emissions for AD -B ased RNG Pathways Compared to Conventional NG, Gasoline and Diesel Pathways .............................................................. 20 9 GHG Emissions from Renewable CNG, Foss il CNG and Petroleum Gasoline Pathways .............................................................................................................. 21 10 Disposition of Carbon from Animal Waste in AD Gas Pathways and the Reference Case .............................................................................................................. 22 11 WTW Total Energy Use for AD -B ased RNG Pathways Compared to Conventional NG, G asoline and Diesel P athways .............................................................. 23 12 WTW F ossil Fuel Use for AD -B ased RNG Pathways Compared to Conventional NG, Gasoline, and Diesel Pathways ............................................................. 23 13 WTW GHG Emissions for AD -B ased RNG Pathways Compared to Conventional NG, Gasoline and Diesel Pathways .............................................................. 24 14 Sensitivity of GHG Emissions from AD -Based Renewable CNG Pathways ..................... 26 TABLES 1 “New” RNG Pathways and Reference -Case Parameters in the GREET Model ................ 7 2 Manure Share and Methane Conversion Factor of Manure Management Systems ............ 10 3 Operational Anaerobic Digesters and Methane Reduction by Livestock Type and Biogas End Use in the United States .................................................................. 10 4 Methane Conversion Factors of Anaerobic Digesters ........................................................ 11 5 Characteristics of Manure ................................................................................................... 12 6 Process He at and Electricity Inputs for Anaerobic Digesters of Manure ........................... 13
v TABLES (CONT.) 7 Direct and Indirect N 2 O Emission Factors and Indirect N 2 O Loss Factors ........................ 15 8 Key Parametric Assumptions .............................................................................................. 17 9 WTW R esults for AD -Bi ased R enewable CNG P athways C ompared to C onventional CNG and G asoline P athways ....................................................................... 20 10 WTW Results for AD -Based Renewable LNG Pathways Compared to Conventional LNG and Diesel Pathways ........................................................................... 21 11 WTW Results for AD -Based Renewable CNG Pathwa ys Compared to Conventional CNG and Gasoline Pathways ....................................................................... 24 12 WTW Results for AD -Based Renewable LNG Pathways Compared to Conventional LNG and Diesel Pathways ........................................................................... 25
vi ACKNOWLEDGMENTS The authors gratefully acknowledge Dennis Smith, National Clean Cities Director and Vehicle Technologies Deployment Manager at the U.S. Department of Energy, who supported this effort. We also acknowledge Edward Frank, Anant Vyas , and Amgad Elgowainy of Ar gonne’s Center for Transportation Research for providing their time and expertise to analyze and interpret data and advise us of its strengths and limitations, as well as our three technical reviewers: Jim Wegrzyn of Brookhaven National Laboratory, Jim Jensen of Washington State University , and Gary Radloff of the Wisconsin Bioenergy Initiative. We sincerely appreciate the time, insight , and expertise they contributed to this effort.
1 WASTE -TO -WHEEL ANALYSIS OF ANAEROBIC -DIGESTION -BASED RENEWABLE NATURAL GA S PATHWAY S WITH THE GREET MODEL Jeongwoo Han, Marianne Mintz, a nd Michael Wang ABSTRACT In 2009 , manure management accounted for 2,356 Gg or 107 billion standard cubic ft of methane (CH 4 ) emissions in the United States , equivalent to 0.5% of U.S. natural gas (NG) consumption. Owing to the high global warming potential of methane, capturing and utilizing th is methane source could reduce greenhouse gas (GHG) emissions. The extent of that reductio n depends on seve ral factors —most notably, how much of this manure -based methane can be captured, how much GHG is produced in the course of converting it to vehicular fuel, and how much GHG was produced by the fo ssil fuel it might displace . A life -cycle analysis was conducted to quantify these factors and , in so doing , assess the impact of convertin g methane from animal manure in to renewable NG (RNG) and utilizing the gas in vehicles. Several manure -based RNG pathways were characterized in the GREET (Greenhouse g ases , Regulated Emissions, and Energy u se in Transportation) m odel, and their fuel -cycle energy use and GHG emissions were compared to petroleum -based pathway s as well as to conventional fossil NG pathw ays. Results show that despite increased total energy use, both fossil fuel use and GHG emissions decline for most RNG pathways as compared with fossil NG and petroleum . However, GHG emission s for RNG pathways are highly depend ent on the specifics of the reference case, as well as on the process energy emis s ions and methane conversion factors assumed for the RNG pathways . The most critical factor s are the share of flared controllable CH 4 and the quantity of CH 4 lost during NG extraction in the reference ca se, the magnitude of N 2 O los t in the anaerobic digestion (AD ) process and in AD residue , and the amount of carbon sequestered in AD residue . In many cases, data for these parameters are limited and uncertain. Therefore, more research is needed to gain a better understanding of the range and magnitude of environmental benefits from converting animal manure to RNG via AD .
2 1 INTRODUCTION In 2009, the United States consumed 23.4 quadrillion Btu of natural gas (NG) for energy, equivalent to 25% of total primary energy consumption , according to the U.S. Energy Information Administration (U.S. EIA, 2011). In the process, 1 ,221 million metric tons (MMT ) of carbon dioxide equivalent (CO 2 e ) were released to the atmosphere , accounting for 22.6% of U.S. greenhouse gas (GHG ) emissions from energy consumption (U.S. EIA, 2011). In addition to its use as a fuel for boilers and other combustion equipment, NG is also used as a feedstock in the production of ammonia, plastics and other products, as a dehumidifie r or desiccant , and for various other industrial processes . Although GHG emissions are also produced from these uses, they are at a much lower level (e.g., <0.01 MMT in ammonia production , according to the U.S. Environmental Protection Agency [U.S. EPA ], 2011b ). While most of the NG consumed in the United States is from conventional wells located in North America, an increasing portion comes from shale deposits, coal beds and other unconventional sources. The shift to these unconventional sources o f fossil NG has raised a number of environmental concerns, including the effect of effluent discharges from pro duction fluids on groundwater and the potential for increased GHG emiss ions. Newly revised results obtained with the Greenhouse g ases, Regulate d Emissions, and Energy u se in Transportation (GREET) model show less relative advantage for fossil NG as compare d with conventional gasoline in vehicular applications. This is primarily because of upward revisions to EPA’s methane leakage and venting assu mptions for conventional gas production. Renewable NG or RNG (also known as biogas, landfill gas [LFG] or digester gas) typically contains 50% or more methane and is itself a significant source of GHG emissions that may be released to the atmosphere —either as a mixture of methane (CH 4 ), CO 2 and other gases , or as the CO 2 combustion product from the flaring of those gases. EPA estimates that in 2009 over 190 MMT of CO 2 e emissions came from landfills, animal manure and wastewater treatment (WWT) facilities (U .S. EPA , 2011b), while another 98 MMT and 16 MMT, respectively, were avoided by LFG -to -energy and manure biogas recovery projects (U.S. EPA, 2011b, 2011c). By avoiding the release of CH 4 and instead recovering and using the RNG in vehicles, very large redu ctions in GHG emissions can be realized relative to petroleum gasoline. At present, there is no generally accepted estimate of the potential RNG resource base, although several sources have investigated portions of it (QSS Group Inc., 1998; Milbrandt, 20 05; Saber and Takach, 2009). Hamberg (2011) reports that in the 2035 2050 timeframe , RNG production could reach 4.84 tcf, with 0.5 4 tcf coming from LFG, municipal wastewater , and livestock manures.1 .The RNG potential from the anaerobic digestion (AD) of food waste is highly speculative . Assuming t hat it could double production from municipal wastewater and livestock manures, the resulting renewable gas resource base (excluding gasification) could be 1 The bulk of the 4.84 tcf comes from the gasification of e n ergy crops, agricultural waste , and other waste. Although t he 0.5 tcf from a naerobic digestion does not explic itly include food waste , some may be included as a co -digestate . More complete estimates are currently under development and may be available later in 2011.
3 0.7 4 tcf per year , which is comparable to t he A merican Gas Foundation (2011) estimate of 0.34 0.87 tcf per year. Although the resource base may be limited, an understanding of RNG pathways and their GHG reduction potential has important policy implications. Because it is chemically identical to fossil NG yet produces far fewer GHG emissions, the blending of relativel y small quantities of RNG with fossil gas can provide significant GHG benefits. For example, o ur previous analysis o f compressed NG (CNG ) and liquefied NG (LNG ) from LFG show ed 77 –101% reduction s in GHG emissions as compared with petroleum gasoline (Mintz et al., 2010). Even blends of 20% RNG and 80% fossil NG were found to yield reductions of 30% or more (Mintz and Han, 2011). In those analyse s, individual pathways differentiated by the source of process electricity and the method of distribut ing the CNG/LNG produced substantially different results. Likewise, for manure -based pathways , reductions in GHG emissio ns and fossil fuel use are likely to differ by feedstock and pathway because c ollection, composition, conversion and purification processes diff er b y type of manure as well as by climate, the composition of digester residue and the fate of that residue. Thus, typica l pathways must be defined and analyzed on a life -cycle basis, and co mpared using a tool like GREET. 1.1 FUEL CYCLE ANALYSIS Unde rstanding the impacts of a fuel on en ergy use and emissions requires life -cycle analysis (LCA), a systematic accounting of the energy use and emissions at every stage of the fuel’s production and use . The stages included in LCA are raw -material acquisition , transportation and processing and product manufacturing , distribution, use and disposal o r recycling. LCA of a fuel is also called fuel -cycle analysis. A fuel cycle typically includes feedstock recover y and transportation , fuel production, transportation and distribution, and combustion a s an end use. For example, as shown in Figure 1 , a CNG pathway for today’s CNG -fueled vehicles includes stages corresponding to gas exp loration and recovery, gas venting and flaring and NG upgrading and compression , a s we ll as stages accounting for non -NG inputs like petroleum , coal , and renewable s . T he stages from exploration and recovery (well) to transpo rtation and distribution (pump) are collectively called well -to -pu mp (WTP), while the last stage, corresponding to combustion by an internal combustion engine (ICE), is called pump -to -wheel (PTW). The enti re pathway is known as well -to -wheel or, in the case of RNG, waste -to -wheel (WTW). In other words, WTW is a term specific to a fuel -cycle analysis of transportation f uel, and a WTW analysis is a LCA of transportation fuels . To conduct fuel -cycle analyses, Argonne National Laboratory has developed and continuously updated the GREET model . Since 1995, GREET improvements have been supported with funding from several pro grams within DOE’s Office of Energy Efficiency and Renewable Energy. Developed in Microsoft® Excel with a graphical user interface, GREET is structured to systematically account for a range of potential feedstocks, fuels and conversion processes for any de fined WTW pathway. GREET calculates emissions of three GHGs (CO 2 , CH 4 and N 2 O ) and six criteria poll utants (VOC s , CO, NO x , SO x , PM 10 and PM 2.5 ), and consumption of each of the following: total energy, fossil fuel, petroleum, NG, and coal.
4 FIGURE 1 Stages in a CNG P athway Downloadable at http://greet.es.anl.gov/, GREET c urrently has more than 15,000 registered users worldwide from academia, industry and government . 1.2 FUEL CYCLE ANALYSIS OF RE NEWABLE NATURAL GAS Figure 2 illustrates the carbon cycle corresponding to RNG produced from the AD of animal waste. Currently , manure management systems treat most animal waste, recovering some energy in the form of a nutrient -rich residue but typically not producing energy . With AD, RNG also can be produced from animal waste and used to fuel vehicl e s or generate electricity . FIGURE 2 Carbon C ycle of A naerobic D igestion -B ased R NG from Animal Waste E x p l o r a t i o n a n d R e c o v e r y N a t u r a l G a s P r o c e s s i n g N a t u r a l G a s T r a n s p o r t a n d D i s t r i b u t i o n N a t u r a l G a s C o m p r e s s i o n C o m p r e s s e d N a t u r a l G a s C o m b u s t i o n E n e r g y S o u r c e s E n e r g y S o u r c e s R e n e w a b l e s R e n e w a b l e s C o a l C o a l N a t u r a l G a s N a t u r a l G a s O i l O i l N a t u r a l G a s N a t u r a l G a s E m i s s i o n s t o W a t e r a n d L a n d E m i s s i o n s t o W a t e r a n d L a n d E m i s s i o n s t o A i r , i n c l u d i n g C O 2 E m i s s i o n s t o A i r , i n c l u d i n g C O 2 C in Air C in Plant C in Animal Waste Current Manure Management Renewable NG Production C in Renewable Natural Gas Renewable NG Combustion C in Emissions (CO 2 , CO, VOC, CH 4 )C in Emissions (CO 2 , CO, VOC, CH 4 )C in Soil C in Soil
5 This study examines the fuel cycle of animal waste conversion to CNG and LNG vehicle fuels. As c ompared with fossil NG (shown in Figure 1 ), WTW analysi s of RNG is more complicated, since it must account for e nergy use and emissions in a reference or base case as well as in a “new” pathway (i.e., conversion of AD gas to RNG and its use in vehicles). Unlike fossil fuels, where no fossil carbon -associated emissions occur if the fuel stays in the ground, renewable fuels involve a recycling of carbon and emissi ons in both a reference case and a “new” pathway that is being modeled. The reference -case pathway consume s energy and generate s emissions in the absence of conversion to vehicular fuel. These reference -case emissions must be subtracted from emissions that occur in the “new” pathway being modeled. Portions (or stages) of the reference and “new” pathway that are unchanged can be ignored , since they do not affect the calculation. C onstructing an appropriate reference case for RNG pathways is further complic ated by current and future diversity in manure management systems and energy recovery . For example, only 16 7 of the more than 2 million farm s in the United States currently recover AD -based RNG to produce electricity for on -site consumption or export to the grid , a handful produce pipeline -quality gas for injection to the natural gas system , and only one farm currently produces NG as a vehicle fuel from animal waste because of the technical and financial difficulties of producing and injecting RNG into a NG pipeline (U.S. EPA , 2011a).2 Even if AD gas -based C NG /LNG for vehicle fuels is not developed in the future, the number of farms producing electricity from AD gas c ould increase as illustrated by the solid blue line in Figure 3 . Thus , if AD gas -based C NG /LNG were introduced as a transport fuel , the reference -case feedstock for the C NG /LNG would include not only the animal wa s te traditionally treated by manure management (the pale red area in the figure) but also animal waste that is converted to electricity (the pale blue area in the figure). Therefore, following the introduction of AD gas -based CNG/LNG , the share of animal waste for electrici ty generation with AD gas -based C NG /LNG would be smaller than that without AD gas -based C NG /LNG . E ither marginal analysis or energy a llocation can be applied to deal with this issue . In marginal analysis , the reference case is assumed to include a mix of animal waste disposal options with some waste treated by conventional manure management and some converted to electricity , with the actual share illustrated by the solid blue line in Figure 3 and the amount of animal waste that otherwise would gene rate electricity noted as A in the figure . If “A” were to be used for RNG, t hen conventional fuel (such as fossil NG) w ould be needed to replace the electricity that otherwise would be produced from animal waste . This fuel should be added to the calculation for the RNG pathway. However, the size of “A” is difficult to measure. Moreo ver, it depends on various fac tors (such as policy i ncentives , regulation, relative NG and electricity prices , and technology development) that change over time . Thus , estimating the mix of supplement al electricity generation is highly uncertain . 2 Another, Fair Oaks Dairy, is expected to begin production by the end of 2011.
6 FIGURE 3 Disposition of A nimal W aste On the other hand , using the energy allocation approach, the reference case is considered to be simply traditional manure management and both C NG /LNG and electricity are considered to be products of the “new” pathway . Then, energy an d emissions associated with the avoided emission s from manure manageme nt can be allocated to LFG -based electricity and RNG by their respective energy share s . Since energy allocation does not require uncertain projection s (e.g., future shares of waste -to -electricity conversion and the marginal supplies used to offset waste -to -electricity conversion displaced by RNG conversion ), t his study uses that approach . Once WTW results for a given fuel pathway are estimated, they should be compared with those for baseline or reference -case pathways that the examined fuel may displace. Since CNG is expected to be used primarily in light -duty vehicles (LDVs), the baseline pathway for CNG should be petroleum gasoline, which is used widely for passenger cars in the United States . Similarly, since LNG is expected to be used in heavy -duty vehicles (HDVs), the baseline pathway for LNG should be petroleum diesel. Table 1 summarizes some of the parameters of “new” and reference -case AD gas -, LFG - and f ossil NG -to -CNG/LNG pathways . 1.3 SCOPE This report summarizes WTW analyses of RNG produced from the AD of animal waste. It describes the pathways, feedstock characteristics, conversion processes and efficiencies, co -products and indirect emissions assumed for the analysis and presents results. The report is organized into four section s. Following this i ntroduction, Section 2 describes the RNG pathways, their key stages and important features of the fuel cycle analysis. In Sections 3 and 4 , estimates of WTW energy use and GHG emissions are presented and conclusions are discussed. 0.0%Shares of manure for each end use Current manure management Recovered to RNG from manure otherwise current management Recovered to RNG from manure otherwise recovered to electricity Recovered to electricity Manure share w/o AD-based RNG developed Manure share w/ AD-based RNG developed 0.0%Shares of manure for each end use Current manure management Recovered to RNG from otherwise current management Recovered to RNG from otherwise recovered to electricity Recovered to electricity Manure share w/o AD-based RNG developed Manure share w/ AD-based RNG developed 2011 Future A
7 TABLE 1 “New” RNG Pathways and R eference -C ase Parameters in the GREET Model RNG (“New”) Pathway and Fuel Reference Case of Feedstock and Fuel Displaced AD gas -based CNG Current m anure m anagement 1 Petroleum g asoline AD gas -based LNG Current m anure m anagement 1 Petr oleum d iesel LFG -based CNG Flaring LFG 1 Petroleum g asoline LFG -based LNG Flaring LFG 1 Petroleum d iesel Fossil CNG No a ctivity Petroleum g asoline Fossil LNG No a ctivity Petroleum d iesel 1 With the energy allocation methodology, the reference case does not include c ompeting pathways (e.g., electricity generation from AD gas or LFG ).
8 2 FUEL CYCLE ANALYSIS OF RENEWABLE NATURAL GA S FROM ANAEROBIC DIGESTION OF ANIMAL WASTE Figure 4 shows the system boundary of R NG pathways from animal waste . The system begins with collected animal waste because waste is collected in both the reference case (cu rrent manure management) and the “new” AD cases. In the latter, collected waste is transported to an AD facility where bio gas and AD residue are produced. Some biogas is combusted to produce electricity and heat for the digestion process, biogas cleanup, and on -site liquefaction, while the rest is purified to produce commercial -grade NG. The produced NG is then either 1) tran sported as a gas to off -site refueling stations, compressed to 3,600 psi and dispensed to CNG vehicles or 2) liquefied on -site, transported as a liquid to off -site stations, and dispensed to LNG vehicles.3 FIGURE 4 System B oundary of R enewable G as P roduction from AD of A nimal W aste T he liquid AD residue from the digestion process contain s significant quantities of N, P and K . I t is transported off -site and applied to soil to displac e synthetic fertilizer s . The solid portion of the residue may be recycled for animal bedding or as a soil amendment , but this is not considered in our analysis . 3 Alternatively, the gas could be injected into a pipeline and liquefied off -site. This pathway is not modeled explicitly in GREET. A n i m a l W a s t e N G P r o d u c t i o n A n i m a l W a s t e T r a n s p o r t a t i o n C u r r e n t M a n u r e M a n a g e m e n t A n a e r o b i c D i g e s t i o n I n t e r n a l E l e c t r i c i t y G e n e r a t i o n T r a n s p o r t a t i o n & D i s t r i b u t i o n C o m p r e s s i o n L i g h t D u t y V e h i c l e O p e r a t i o n L i q u e f a c t i o n H e a v y D u t y T r u c k O p e r a t i o n T r a n s p o r t a t i o n & D i s t r i b u t i o n E m i s s i o n s R e f e r e n c e C a s e H e a t E l e c t r i c i t y N G B i o g a s S o i l A p p l i c a t i o n D i s p l a c e m e n t o f S y n t h e t i c F e r t i l i z e r s C a r b o n S e q u e s t r a t i o n C O 2 E m i s s i o n s N 2 O a n d N O x E m i s s i o n s C P ,K N S o i l A p p l i c a t i o n D i s p l a c e m e n t o f S y n t h e t i c F e r t i l i z e r s C a r b o n S e q u e s t r a t i o n C O 2 E m i s s i o n s N 2 O a n d N O x E m i s s i o n s C P ,K N A D R e s i d u e R e s i d u e
9 2.1 BIOGAS PRODUCTION IN AD PATHWAYS AND REFE RENCE CASE Unlike the LFG pathways already contained in GREET (see Mintz et al ., 2010), AD pathways include the bio -methane production step and soil application of AD residue . Therefore, the amount of bio -methane produced is important and can be estimated for a given livestock type as follows: where CH 4,Manure is the amount of CH 4 produced in ft 3 /lb of volatile solid (VS), B 0 is the maximum methane -producing capacity for manure of a given livestock type in ft 3 /lb of VS, MCF S,k is the methane conversion factor (MCF) for each manure management technology S by climate region k in %, and MS S,k is the manure share (MS) handled by manure management technology S by climate region k (IPCC, 2007). B 0 depends on species and diet ; the MCF of conventional manure management varies by technology and climate while the MCF of anaerobic digesters depends on technology, residence time and temperature . T he energy use and emissions associated with current manure management should be taken as a credit , which depend s not only on the manure management system but also on climate, livestock species and diet. IPCC provides recommended values for B 0 and MS for nine regions (i.e. North America , Latin Americ a, Western Europe , Eastern Europe , Oceania , Africa , Middle East , Asia , and Indian Subcontine nt ) and MCF s for a range of annual average temperature s from 10 °C to 28 °C (IPCC, 2007). The U.S. EPA summarizes B 0 for livestock in the United States from various sources in the literature , and estimates MS for manure management systems used in the United States by state (U.S. EPA, 2011a). EPA also estimates MCFs of wet (e.g. anaerobic lagoon, liquid slurry and deep pit) and dry (e.g. daily spread and pasture) manure management systems for cool, temperate and warm climate zone s . EPA uses the same climate zone definitions as the IPCC (i.e., cool climate zone: 10 –14 °C, temperate climate zone: 15 –25 °C and warm climate zone: 26 –28 °C) and annual average temperature s reported by t he U.S. National Oceanic and Atmospheric Administration (NOAA, 2011). Average MS and MCF values for the United States as a whole are obtained u sing the livestock population from the U.S. Department of Agriculture (USDA , 2011). All data available from IPCC, EPA, NOAA and USDA are included in GREET. F or this s tudy, we use average U.S. values for dair ies as reported by U.S. EPA and USDA , and conduct sensitivity analyses using data for California and Wisconsin , the two states with the largest dairy populations . Table 2 summarizes average MS s and MCFs for the United States , California , and Wisconsin. Note that in Wisconsin , solid storage is used for a larger share and anaerobic lagoons for a smaller share of manure than in the United States as a whole, while in California more than 50% of manure is treated by anaerobic lagoon. B 0 for dairy cow s is 0.24 m 3 CH 4 /kg of VS (USDA , 2011). According to EPA ’s AgS TAR Program, 167 farm -based AD projects are currently recovering energy in the United States , redu c ing CH 4 emissions by 1.1 Tg CO 2 e , as shown in Table 3 TABLE 3 (U.S. EPA, 2011b). Despite this reduction, EPA estimates that total methane emissions from manure management in the United States were still 49.5 Tg CO 2 e in 2009 , which is significant compared to other sectors (U.S. EPA, 2011a). For example, in 2009, NG systems in
10 the United States generated 130.3, 17.5, 44.4 and 29.0 Tg CO 2 e emissions from field production, processing, transmission, and storage and dis tribution, respectively. Owing to increasing concerns about global warming , manure management systems are expected to capture or flare an increasing share of CH 4 from manure. Therefore, we assume that CH 4 from all manure management systems except pasture a nd daily spread will be flared , and provide a sensitivity analysis for the share of flared CH 4 from current manure management (see Sec. 3.3). Pasture and daily spread are excluded from this assumption because of the difficulty in collecting CH 4 from land application. TABLE 2 Manure Share (MS) and Methane Conversion Factor (MCF) of Manure Management Systems Waste Management System Pasture Daily Spread Solid Storage Liquid/ Slurry Anaerobic Lagoon Deep Pit MS by s ystem and location U.S. Average 7 % 15 % 2 3% 2 1% 32 % 2 % California 1 % 11 % 9 % 21 % 58 % 0 % Wisconsin 7 % 12 % 42 % 24 % 12 % 4 % MCF by system and location U.S. Average 1.2% 0.2% 2.6% 28.6% 69.9% 28.6% California 1.5% 0.5% 4.0% 35 % 75 % 35 % Wisconsin 1.0% 0.1% 2.0% 22 % 66 % 22 % TABLE 3 Operational Anaerobic Digesters and Methane Reduction by Livestock Type and Biogas End Use in the United States Dairy Beef Swine Poultry No. of Projects Methane Reduction (Gg CO 2 e/yr) No. of Projects Methane Reduction (Gg CO 2 e/yr) No. of Projects Methane Reduction (Gg CO 2 e/yr) No. of Projects Methane Reduction (Gg CO 2 e/yr) Heat 6 31 2 84 Electricity 38 284 7 102 2 2.2 Cogeneration 78 392 1 2.7 7 20 3 15 Vehicle Fuel 1 1.9 Pipeline NG 1 1.4 Flared 11 123 5 17 Not Specified 2 27 1 2 7 Total 137 861 2 2.7 23 230 5 17
11 MCFs for AD pathways are estimated from EPA ’s AgStar project database (U.S. EPA, 2011b). From a consideration of 92 projects without co -digestion among the 137 active dairy projects in the United States , total CH 4 emissions are estimated by subtracting CH 4 emission reductions from baseline CH 4 emissions. Here, the baseline CH 4 emissions are estimated from the baseline MCFs by states provided in the GHG Inventory (U.S. EPA, 2011a) while the CH 4 emission reductions are converted from metric tons of CH 4 /yr to m 3 /kg VS using the livestock population feeding the digester and the VS production rate provided in the GHG Inventory (U.S. EPA, 2011a). Then, MCFs of AD are estimated as follows: Table 4 summarizes MCFs estimated from the AgStar project database along with those from two other source s . The values of Frost and Gilkinson (2011) are based on measure d AD yield s from dairy cow slurry at Hillsborough, UK , while the values of Berglund and Börjesson (2006) are based on several reports from Sweden. Values within parentheses represent minimum an d maximum values reported in the literature, which show large variations in MCFs. Typically, covered lagoon systems have a lower average and a wider range of MCFs than other systems because the temperature and homogeneity of the mixture in the reactor are less controllable. TABLE 4 Methane C onversion F actors (MCFs) of Anaerobic Digesters Source Covered Lagoon Complete Mix Horizontal Plug Flow Mixed Plug Flow AgStar (U.S. EPA, 2011b) 70 % (37%–90% ) 85 % (61%–92% ) 82 % (69%–97% ) 81 % (53%–97% ) Frost and Gilkinson (2011) 65 % (54%–72% ) Berglund and Börjesson (2006) 81 % (63%–99% ) 2.2 ANIMAL WASTE TRANSPO RTATION AND OPERATIO N OF ANAEROBIC DIGESTER From the CH 4 production estimated using the equation above, the VS required to produce 1 mmBtu of NG can be calculated. When VS is transported ,4 other solid s and water are also transported. Therefore, the total mass , in lb, to be transported (M T ) per 1 mmBtu of methane produced can be expressed as 4 In the reference case, manure is not transported off site. In our analysis , transportation may occur as part of the collection process.
12 where r VS/TS and r moisture are the ratio s of VS to total solid s (TS) and the moisture content is calculated using the values s ummarized in Table 5 . Also , is the C H 4 produced from AD in ft 3 /lb VS and LHV CH 4 is the lower heating value of methane in Btu/ft 3 . In t his study , the default assumption is that all manure is transported by truck for 3 miles to a central digester . Depending on the scale of the AD project , pipeline transportation of manure can be used to reduce the process energy inputs and operating costs, negating large capital cost s (Ghafoori et al., 200 7 ). TABLE 5 Characteristics of M anure (U SDA , 2010) wt% Dairy Cow Other Cattle Market Swine Breeding Cattle Moisture 88% 88% 90% 90% VS/TS 85% 50% 54% 54% N/TS 3.9% 2.6% 4.2% 4.7% P/TS 0.6% 0.9% 1.6% 1.5% K/TS 2.5% 1.9% 2.2% 3.0% C/TS 47% 28% 29% 28% As shown in Table 6 , heat and electricity demands of AD facilities vary significantly depending on feedstock characteristics, plant size and technology (mesophilic vs. thermophilic, batch vs. continuous , and single stage vs. multiple stages). For example, mesophilic digesters operating at ambient temperature (65 –110 ○F) require less heat than thermophilic ones operating at elevated temperature (120 –160 ○F ). Mesophilic digesters are typically more stable but less productive than thermophilic. On the oth er hand, simple batch reactors (e.g. covered lagoon) require much smaller energy inpu ts than continuous reactors (e.g. continuous ly stirred tank reactors [CSTRs ] and plug -flow reactors). Continuous reactors typically produce more biogas faster because of the homogeneity of manure in reactors. Moreover, it is possible to use multiple v essel s for different stages of digestion, such as h ydrolysis , a cidogenesis , a cetogenesis and m ethanogenesis , so that each stage can occur under optimal conditions. In a typical two -stage reactor, h ydrolysis , a cidogenesis and a cetogenesis occur in the first vess el while methanogenesis occurs in the second vessel. Even though the biogas yields are higher, multistage reactors are more complex and require more energy. The AD systems reported by Frost and Gilkinson (2011) and Berglund and Börjesson (2006) are a CSTR and a continu ous -tank reactor operating at meso ph il ic temperature, respectively, while Börjesson and Berglund (2006) d o not specify the type of reactor. This study assumes average value s for AD except for covered lagoons. Since covered lagoons have li ttle maintenance cost, they are assumed to consume half the heat required by the other systems and no electricity. For reference manure management, no energy inputs are assumed because major
13 TABLE 6 Process H eat and E lectricity I nputs for A naerobic D igesters of M anure Heat (Btu/wet ton of manure) Electricity (kWh/wet ton of manure) Frost and Gilkinson (2011) Farm Scale 96,000 (68,000 –121,000)1 4.9 Berglund and Börjesson (2006) Farm Scale 215,000 8.3 Large Scale 94,600 (60,000 –155,000) 16.6 (13.9 –20.2) Börjesson and Berglund (2006) Farm Scale 163,000 6.5 Large Scale 73,000 13.4 Average 116,000 12.0 1 Values within pa rentheses indicate the range observed in the literature . waste management practices in the United States (daily spread, solid storage, liquid/slurry storage, anaerobic lagoon and deep pit) require negligible amoun ts of energy other than for transferring the waste.5 I n this study i t is assumed that all on -site e lectricity and heat demands f or AD, NG production , and on -site liquefaction are supplied by an on -site generator powered by RNG . If the heat produced from an on -site generator meets the heat demand and produces excess electricity , the electricity is assumed to be exported , di splacing the U.S. average generation mix. On the other hand, if excess heat is produced , it is assumed to be discarded. 2.3 APPLICATION OF AD R ESIDUE TO SOIL Regardl ess of manure management system , residue from manure management or anaerobic digesters is eventua lly applied to soil. AD residue is assumed to be backhauled by the same trucks that transport animal waste. Since residue still contains VS, AD still occurs , emitting a small amount of CH 4 . This study assumes the same MCF for CH 4 emission s from residue as from manure daily spread . Also, the carbon in the residue is not stable and is easily oxidize d to CO 2 . Bruun et al. (2006) estimate that 14 –37%, 63 –83% and 84 –9 8 % of C applied becomes CO 2 in 10, 50 and 100 years , respectively (Figure 5 ). Using the data in Bruun et al. (2006) and averag ing over a 100 -year time horizon , 62 % of the C in the residue is assumed to become CO 2 , and the rest (38%) is assumed to remain stored in the soil. The C in the residue is calculated by subtracting the C converted to CH 4 from the total C in the manure. The typical nitrogen (N), phosphorus (P) and potassium (K) contents of manure we re shown in Table 5 . Since there is little loss of nutrients during manure management, the nutrients are applied to the soil when resi due is applied, displacing synthetic fertilizers. However, when N 5 This assumption is also consistent with longer retention times which would provide more opportunity for methane leakage.
14 FIGURE 5 Carbon L eft in S oil A fter AD R esidue is A pplied to S oil 6 is applied, N 2 O and NO x are emitted. T he se impacts —that is , synthetic fertilizer displacement and N 2 O and NO x emissions —should be included in LCA s of AD pathways. N 2 O emission s can occur (1) by nitrification and denitrification, (2) by volatilization as nitrate (some of which is then converted to N 2 O), and (3) by leaching as nitrate from soil to streams and groundwater via runoff (some of which is then converted to N 2 O). This study applies the emission factors set by the IPCC and adapted in EPA ’s GHG Inventory . These are summarized in Table 7 . Note that while the emission factors for direct N 2 O emissions by nitrification and denitrification are defined as kg (N 2 O – N )/kg excreted N , those for indirect N 2 O emissions are defined as kg (N 2 O – N )/kg volatilized or leach ed N . Thus, in order to calculate i ndirect N 2 O emissions by volatilization and leaching, the fraction of N lost through volatilization and leaching should be determined (see Table 7 ). For AD , EPA specifies no direct N 2 O emission s because nitrification requires oxygen. Little information is availabl e for indirect N 2 O emissions from AD and A D residue. Therefore, we assume that the volatilization and runoff/leaching N 2 O loss factors are 43% and 0.6%, respectively , which are consistent with those for open anaerobic lagoon s. With N 2 O emissions calculated for the reference case and the AD pathways , the differences in N stored in the soil between the reference case and the AD pathways can be estimated . The stored N is taken by plants, displacing synthetic fertilizers. Therefore, the differences in the stored N can be used to estimate the amount of displaced N fertilizers. In this study, we assume 50% of the stored N 6 Note that the percent of carbon stored in soil could asymptote over time with repeated application of AD residue. However, since we assume systematic rotation of the soil to which AD residue is ap plied, we do not adjust carbon uptake assumptions . 0%10%20%30%40%50%60%70%80%90%100%0 10 20 30 40 50 60 70 80 90 100 Carbon left in soil Year
15 TABLE 7 Di rect and I ndirect N 2 O Emission Factors and I ndirect N 2 O L oss F actors Pasture Daily Spread Solid Storage Liquid/ Slurry Anaerobic Lagoon Deep Pit Direct N 2 O Emission Factors (kg [N 2 O – N ]/kg N) 0 0 0.005 0.005 0 0.002 Volatilization Indirect N 2 O Emission Factors 0.010 kg (N 2 O – N )/kg N Volatilized Volatilization Indirect N 2 O Loss Factor 0% 10% 27% 26% 43% 24% Runoff/Leaching Indirect N 2 O Emission Factor 0.010 kg (N 2 O – N )/kg N from Runoff/Leaching Runoff/Leaching Indirect N 2 O Loss Factor Central 0.0% 0.0% 0.2% 0.2% 0.2% 0.0% Pacific 0.0% 0.0% 0.0% 0.8% 0.8% 0.0% Mid -Atlantic 0.0% 0.0% 0.0% 0.7% 0.7% 0.0% Midwest 0.0% 0.0% 0.0% 0.4% 0.4% 0.0% South 0.0% 0.0% 0.0% 0.9% 0.9% 0.0% U.S. Average 0.0% 0.0% 0.0% 0.6% 0.6% 0.0% displaces synthetic N fertilizers for the reference case and AD pathways .7 Therefore, this study includes the energy and emissions associated with synthetic fertilizers displaced , assuming the soil is cornfield. 2.4 BIOGAS PROCESSING FO R RNG PRODUCTION In NG production, biogas from an anaerobic digester is converted into pipeline -quality NG through pre -purification and purification processes. Pre -purification removes impurities including corrosive hydrogen compounds, low concentrations (parts per million) of non -methane organic compounds , and water; purification removes CO 2 and increases CH 4 concentration . We examine four major technologies (membrane separation, adsorption, absorption, and cryogenic distillation ) and define the process efficiency of NG production , i.e., the energy in the produced NG divided by the sum of the energy in the biogas feed to the pre -purification step and the process e nergy for pre -purification and purification (Mintz et al., 2010). The electricit y for NG product ion and subsequent processes is assumed to be generated from ICEs powered by pre -purified biogas . The efficiency of ICEs is assumed to be 35% (Mintz et al., 2010). CH 4 v ented or leaked from equipment during AD , NG production or upgrading is a major source of GHG emissions. On the basis of several Swedish reports, Börjesson and Berglund (2006) estimate that 2% of the biogas produced is vented or leaked during the se stage s . This value is significantly larger than t he 0.15% emission rate for conventional NG upgrading 7 Lack of oxygen in AD keeps ammonia from being nitrified , thereby increas ing its ammonia content . Th us , AD residue might displace more N fertilizer than manure in the reference case . Moreover, N uptake occurs over a longer portion of the growing season, permitting multiple applications of re s idue to nearby (and perhaps more distant) fields. However, since data on actual application rates and travel dist an ces from residue storage tanks to fields are not available , displacement ratios are assumed to be the same for the reference case and the AD pathways.
16 facilities, but could be attributed to difference s in scale (Burnham et al., 2011). Therefore, this study assumes that 2% of the produced renewable gas is leaked. As indicated by Börjesson and Berglund (2006), more research on CH 4 emissions from anaerobic digesters and small -scale NG processing facilities is warranted for a more comprehensive understanding of biogas -based pathways. A substantial amount of methane , which could correspond to 5 –10% or even up to 20% of produced renewable gas , could also leak during the storage and transport of waste to the digester or as AD residue and during digester maintenance (Börjesson and Berglund , 2006). Assuming an 80% MCF for AD, 5 –10% CH 4 emission during storage means th at MCFs for AD residue storage c ould be 20 –40%, which corresponds to the range of MCFs for liquid/slurry, deep pit and anaerobic lagoon storage . Because AD residue is not stored as long as manure in liquid/slurry, deep pit and anaerobic lagoon s (typically more than 2 –3 months), MCFs of 20 –40% m ay be too high (particularly if GHG reduction measures become widely adopted). Moreover, m ost losses can be reduced by reducing the storage period or collecting the gas during transport, storage and maintenance. Owing to the uncertainty of the leakage rate and the possibility of reducing it, t his study does not include potential losses from lea kage during transport, storage, or maintenance. 2.5 RNG COMPRESSION, LIQUEFA CTION, TRANSPORTATION, DIST RIBUTION AND VEHICLE USE In GREET, RNG can be dispensed as a gas to CNG LDVs or as a cryogenic liquid to LNG HDVs . For the former, RNG is assumed to be shipped 50 miles 8 by pipeline to off -s ite refueling stations where it is compressed to 4,000 psia by electric compressors powered by grid electricity. F or the latter, RNG is liq uefied by on -site liquefi ers (whose efficiency is assumed to be 89%, assuming single mixed refrigerant and expander processes ) and then truck ed to off -site stations located 50 miles from the RNG production site . This study assumes that LDVs operate on CNG while HDVs operate on LNG. Thus, results for CNG - and LNG -fueled v ehicles are compared to those for petroleum gasoline car s and diesel HDVs, respectively. We also assume that gasoline cars achieve the GREET default fuel economy, which is 23.4 mpgge (miles per gallon gasoline equivalent), and that CNG car s are as efficie nt as gasoline cars (Mintz et al., 2010). For HDVs, we reviewed several sources. The 2002 Vehicle Inventory and Use Survey conducted by the U.S. Census Bureau (2002) reports that class 7 –8 diesel trucks achieve 5.92 mpg d e (miles per gallon diesel equivalent) in regional service while on -road driving data from manufacturers suggest a value of 6.2 mpgde for long -haul heavy -duty diesel trucks.(Vyas et al., 2002). By contrast, r ecent E PA and NHTSA estim ate s, using a vehicle simulation model for their regulation impact analysis of Greenhouse Gas Emissions Standards and Fuel Efficiency Standards for Medium - and Heavy -Duty Engines and Vehicles (2011), report a value of 4.95 mpgde . This study assumes that class 7 -8 diesel trucks achieve an average fuel eco nomy of 6 mpg d e and that LNG trucks are 10% less fuel -efficient than diesel trucks (Mintz et al., 2010). 8 The distance is estimated by assuming a local refueling station, and can vary by scenario.
17 3 RESULTS WTW results , in units of total energy and fossil energy consumption per MJ and per mi and in GHG emissions per MJ and per mi, are presented below. K ey parameters are s ummarized in Table 8 . Results of sensitivity analyses for seven key parameters related to AD pathways are presented in Section 3.3. These parameters include percent of controllable CH 4 that is flared in the reference case, MCF of the anaerobic digester, process energy demand for AD, MCF of the AD residue, percent of C in the AD residue applied to the soil that is sequestered , indirect N 2 O loss factors , and CH 4 losses in NG processing . TABLE 8 Key P arametric A ssumptions Parameter Unit Value Animal Waste Transportation Animal Waste Transportation Distance mile 3 Animal Waste Moisture Content % 88% AD and Reference Case B 0 m 3 CH 4 /kg of VS 0.24 MS and MCF of Reference Case % See Table 2 % of Flaring Controllable CH 4 in Reference Case % 100% MCF of Anaerobic Digester % See Table 4 Heat Demand for AD Btu/wet ton of m anure See Table 6 Electricity Demand for AD kWh/wet ton of m anure See Table 6 AD Residue MCF of AD Residue % 0.2 % % of Sequestration of C in AD Residue Applied to Soil % 38 % Volatilization Indirect N 2 O Loss Factor % 43% Runoff/Leaching Indirect N 2 O Loss Factor % 0.6% NG Processing and Upgrading NG Processing Efficiency % 94% CH 4 Loss Rate from NG Processing % 2%1 Internal Engine Generation Efficiency % 35% Heat Recovery Efficiency of Internal Engine Generation % 80% Compression Efficiency % 97% Liquefaction Efficiency % 89% NG Transportation/Distribution and Vehicle Operation Distance to CNG/LNG Refueling Station s mi 50 Fuel Economy of Baseline Gasoline Cars mpgge 23.4 Rat io of CNGV Fuel Economy to That of Gasoline Cars % 100 % Fuel Economy of Baseline Diesel HDVs mpgde 6 Rat io of LNGV Fuel Economy to That of Diesel HDV s % 90% 1 Percent of CH4 produced that is lost in processing.
18 3.1 ENERGY AND GHG EMISS IONS P ER MJ Figure 6 c ompares WTW total energy use for each RNG pathway with similar results for CNG from c onventional N orth A merican N atur al G as (NA NG ), LNG from NA NG , petroleum gasoline , and petroleum diesel . WTW total energy use depends largely on system efficiency. Thus, RNG -based pathways typically require more total energy than conventional NG , gasoline or diesel . Moreover, LNG requires more total energy than CNG b ecause compression is more energy efficient than liquefaction . Figure 7 c ompar es WTW fossil fuel use for the pathways , and show s a significant reduction for RNG -based pathways relative to fossil -based pathways . Note that for RNG pathways, vehicle op eration (PTW) uses no fossil fuel (since RN G -based fuels are renewable ), while R NG processing and liquefaction require little or no fossil fuel and only a small amount of fossil fuel is needed for animal waste and AD residue transport and R NG trans portation and distribution. For CNG, RNG is compressed at off -site refueling station s using electricity produced by the U.S. average electricity mix ; for LNG, RNG is liquefied on site using electricity generated by biogas from the digestion process itself . Therefore, AD -based renewable LNG consumes much less fossil energy than does CNG . Among AD -based pathways, covered lagoon s use less total and fossil fuel because of their smaller process energy demands. Figure 8 shows WTW GHG emissions for RNG pathways , as co mpared with petroleum gasoline , petroleum diesel , fossil CNG (from NA NG) and fossil LNG (from NA NG) pathways. Note that GHG emissions are expressed as g CO 2 e per unit energy produced and used , and include CO –, CH 4 and N 2 O . Because of credits from m anure management in the reference case , RNG pathways generate far fewer GHG emissions than fossil fuel pathways. Similar to the fossil energy results, AD -based renewable LNG emits much less GHG than CNG because RNG is liquefied on -site using electricity pr oduced from biogas . Also, the smaller process energy demands of anaerobic lagoon s result in lower GHG emissions. Tables 9 and 10 provide detailed WTW results for total energy use , fossil fuel use and GHG emissions for CNG and LNG pathway s , respectively . In the GHG calculation s , GHGs are assumed to be captured and stored in the fuel during fuel production and released during vehicle operation. Thus, GHG emissions for WTP are largely negative. Figure 9 shows GHG emissions from AD -based renewable CNG, foss il CNG and petroleum gasoline pathways by stage . F o r AD -based renewable CNG, results are shown for the mixed plug flow digester . Excluding emissions from vehicle operation, the largest share of GHG emission in all three pathways occurs during the recovery and processing stages. For fossil CNG, recovery generates large GHG emissions because of methane leak age and vent ing during well workovers (Burnham et al., 2011). For AD -based renewable CNG , processing accounts for a larger share of emissions because of ou r 2% leakage assumption (discussed in Section 0 ) as well as the lower processing efficiency of the relatively small -scale reactors .
19 FIGURE 6 WTW Total E nergy U se for AD -B ased R NG P athways Compared to Conventional NG , G asoline and D iesel P athways (MJ/MJ Produced and Used ) FIGURE 7 WTW F ossil F uel U se for AD -B ased R NG P athways C ompared to C onventional NG , G asoline and D iesel P athways (MJ/MJ Produced and Used ) 0.00 0.20 0.40 0.60 0.80 1.00 1.20 1.40 1.60 1.80 Covered Lagoon Complete Mix Plug Flow Mixed Plug Flow Conventional NA NG Gasoline Covered Lagoon Complete Mix Plug Flow Mixed Plug Flow Conventional NA NG Diesel AD -based RNG AD -based RNG Total Energy (MJ/MJ)PTW WTP CNG LNG 0.00 0.20 0.40 0.60 0.80 1.00 1.20 1.40 Covered Lagoon Complete Mix Plug Flow Mixed Plug Flow Conventional NA NG Gasoline Covered Lagoon Complete Mix Plug Flow Mixed Plug Flow Conventional NA NG Diesel AD -based RNG AD -based RNG Fossil Fuels (MJ/MJ)PTW WTP CNG LNG
20 FIGURE 8 WTW GHG Emissions for AD -B ased R NG P athways C ompared to C onventional NG , G asoline and D iesel P athways (g CO 2 e/MJ Produced and Used ) TABLE 9 WTW R esults for AD -B ased R enewable CNG P athways C ompared to C onventional CNG and G asoline P athways (MJ or g CO 2 e/MJ P roduced and Used ) Fuel CNG Gasoline Feedstock AD Gas NA NG AD Type Covered Lagoon Complete Mix Horizontal Plug Flow Mixed Plug Flow Total Energy (MJ/MJ ) WTP 0.28 0.45 0.45 0.46 0.18 0.25 PTW 1.00 1.00 1.00 1.00 1.00 1.00 WTW 1.28 1.45 1.45 1.46 1.18 1.25 Fossil Fue ls (MJ/MJ ) WTP 0.05 0.08 0.08 0.08 0.17 0.23 PTW 0.00 0.00 0.00 0.00 1.00 0.98 WTW 0.05 0.08 0.08 0.08 1.17 1.21 GHGs (g CO 2e/MJ ) WTP -49 -40 -39 -41 26 20 PTW 58 58 58 58 58 74 WTW 9 18 18 17 83 94 GHG Emissi ons Reduction Relative to Gasoline Vehicles -91% -81% -81% -82% -11% -60 -40 -20 0 20 40 60 80 100 Covered Lagoon Complete Mix Plug Flow Mixed Plug Flow Conventional NA NG Gasoline Covered Lagoon Complete Mix Plug Flow Mixed Plug Flow Conventional NA NG Diesel AD -based RNG AD -based RNG GHG Emissions (gCO2e/MJ)PTW WTP WTW CNG LNG
21 TABLE 10 WTW R esults for AD -Based R enewable LNG P athways C ompared to C onventional LNG and D iesel P athways (MJ or g CO 2 e /MJ P roduced and Used ) Fuel LNG Diesel Feedstock AD Gas NA NG AD Type Covered Lagoon Complete Mix Horizontal Plug Flow Mixed Plug Flow Total Energy (MJ/MJ ) WTP 0.34 0.52 0.52 0.53 0.22 0.22 PTW 1.00 1.00 1.00 1.00 1.00 1.00 WTW 1.34 1.52 1.52 1.53 1.22 1.22 Fossil Fue ls (MJ/MJ ) WTP 0.02 0.02 0.02 0.02 0.22 0.22 PTW 0.00 0.00 0.00 0.00 1.00 1.00 WTW 0.02 0.02 0.02 0.02 1.22 1.22 GHGs (g CO 2e/MJ ) WTP -51 -44 -44 -45 25 20 PTW 57 57 57 57 57 75 WTW 5 13 13 11 82 96 GHG Emissi ons Reduction Relative to Diesel Vehicles -94% -87% -86% -88% -14% FIGURE 9 GHG E missio ns from R enewable CNG, F ossil CNG and P etroleum G asoline P athways (g CO 2 e/MJ) -180 -130 -80 -30 20 70 120 Renewable CNG Fossil CNG Gasoline GHG Emissions (gCO 2 e/MJ)Vehicle Operation Additives Compression T&D Processing Recovery Credit from Reference Case N 2 O CH 4 CO 2 N 2 O CH 4 CO 2 Credit From Current Management Emission From AD Residue WTW
2 2 Figure 9 also breaks down the emission credits and burdens for the AD -based renewable CNG pathway. All GHG credits result from the reference case (i.e., the difference between the emission s generated by current manure management and the emission burdens fr om AD residue ). Compared to CH 4 emissions from current manure management, the CH 4 emissions from AD residue are significantly smaller because most digestible carbon is recovered by AD, and AD residue is applied to soil , where much of the remainder is digested aerobically. The CO 2 emissions from AD residue are also much smaller than the CO 2 credits from current manure management because a large amount of carbon is converted into RNG , as shown in Figure 10 . Even though the difference is small, N 2 O emiss ions from the AD residue are also smaller than those produced from current manure management. FIGURE 10 Disposition of C arbo n from A nimal W aste in AD G as P athway s and the R eference C ase 3.2 ENERGY AND GHG EMISS IONS P ER MI LE Figure s 11 –13 show WTW total energy use, fossil fuel use and GHG emissions on a per -mile basis for AD -based RNG pathways . Separate c omparisons highlight differences for cars fueled with AD -based r enewable CNG versus petroleum gasoline and fossil CNG (from NA NG), and for HDVs fueled with AD -based renewable LNG versus petroleum diesel and fossil LNG (from NA NG). Tables 10 and 1 1 provide detaile d results for WTW total energy and fossil fuel use and GHG emissions for CNG cars and LNG trucks as compared to gasoline cars and diesel trucks . Since automobile fue l econom y is about 10 times better than HDV fuel economy (in mpg equivalent), WTW results for cars are about 10 -fold smaller than for HDVs. Note that in Figures 11 –13 , the units on the right vertical axis (for HDVs ) are 5 times those on the left vertical axis (for cars ). Because CNG cars are as efficient as gasoline cars, their per -mile PTW energy use and GHG emissions look very similar to the per -MJ results shown in F igures 6 –8 . However, owing to the 10% fuel economy advantage of diesel s over NG -fueled trucks, total energy use and fossil fuel use for diesel HDVs are smaller than for fossil LNG HDVs. Nonetheless, the advantages of renewable LNG pathways with respect to fossil fuel use and GHG emissions result in significant reduction s i n WTW fossil fuel use and GHG emissions (Figures 12 and 13). C in Animal Waste AD Residue Recovered CH 4 into RNG Released CH 4 Converted into CO 2 Stored in Soil CH 4 Emission during Manure Treatment Flared CH 4 Released CH 4 Residue Converted into CO 2 Stored in Soil 79%21%49%0%30%7%93%7%1%57%35%AD Gas Pathway Reference Case
23 FIGURE 11 WTW T otal E nergy U se for AD -B ased R NG P athways C ompared to C onventional NG , G asoline and D iesel P athways (Btu/mi) FIGURE 12 WTW F ossil F uel U se for AD -B ased R NG P athways C ompared to C onventional NG , G asoline , and D iesel P athways (Btu/mi) 0 5,000 10,000 15,000 20,000 25,000 30,000 35,000 40,000 0 1,000 2,000 3,000 4,000 5,000 6,000 7,000 8,000 Covered Lagoon Complete Mix Plug Flow Mixed Plug Flow Conventional NA NG Gasoline Car Covered Lagoon Complete Mix Plug Flow Mixed Plug Flow Conventional NA NG Diesel HDV AD-based RNG AD-based RNG Total Energy for HDVs (Btu/mi)Total Energy for Cars (Btu/mi)PTW WTP CNG Cars LNG HDVs 0 5,000 10,000 15,000 20,000 25,000 30,000 35,000 0 1,000 2,000 3,000 4,000 5,000 6,000 7,000 Covered Lagoon Complete Mix Plug Flow Mixed Plug Flow Conventional NA NG Gasoline Car Covered Lagoon Complete Mix Plug Flow Mixed Plug Flow Conventional NA NG Diesel HDV AD-based RNG AD-based RNG Fossil Fuels for HDVs (Btu/mi)Fossil Fuels for Cars (Btu/mi)PTW WTP CNG Cars LNG HDVs
24 FIGURE 13 WTW GHG Emissions for AD -B ased R NG P athways C ompared to C onventional NG , G asoline and D iesel P athways (g CO 2 e/mi) TABLE 11 WTW R esults for AD -B ased R enewable CNG P athways C ompared to C onventional CNG and G asoline P athways (Btu or g CO 2 e /mi ) Fuel CNG Gasoline Feedstock AD Gas NA NG AD Type Covered Lagoon Complete Mix Horizontal Plug Flow Mixed Plug Flow Total Energy (Btu/mi ) WTP 1,387 2,232 2,214 2,273 862 1,230 PTW 4,908 4,908 4,908 4,908 4,908 4,908 WTW 6,295 7,140 7,122 7,181 5,770 6,138 Fossil Fu els (Btu/mi ) WTP 241 375 373 379 810 1,125 PTW 0 0 0 0 4,908 4,806 WTW 241 375 373 379 5,718 5,931 GHGs (g CO2e/mi ) WTP -254 -206 -204 -212 132 105 PTW 298 298 298 298 298 381 WTW 44 92 95 86 431 486 GHG Emiss ions Reduction Relative to Gasoline Vehicles -91% -81% -81% -82% -11% -1,500 -1,000 -500 0 500 1,000 1,500 2,000 2,500 3,000 -300 -200 -100 0 100 200 300 400 500 600 Covered Lagoon Complete Mix Plug Flow Mixed Plug Flow Conventional NA NG Gasoline Car Covered Lagoon Complete Mix Plug Flow Mixed Plug Flow Conventional NA NG Diesel HDV AD-based RNG AD-based RNG GHG Emissions for HDVs (gCO2e/mi)GHG Emissions for Cars (gCO2e/mi)PTW WTP WTW CNG Cars LNG HDVs
25 TABLE 12 WTW R esults for AD -B ased R enewable LNG P athways C ompared to C onventional LNG and D iesel P athways (Btu or g CO 2 e/mi ) Fuel LNG Diesel Feedstock AD Gas NA NG AD Type Covered Lagoon Complete Mix Horizontal Plug Flow Mixed Plug Flow Total Energy (Btu/mi ) WTP 8,261 12,527 12,438 12,731 5,191 4,869 PTW 23,999 23,999 23,999 23,999 23,999 21,818 WTW 32,261 36,527 36,437 36,731 29,190 26,687 Fossil Fuels (Btu/mi ) WTP 551 520 512 540 5,161 4,786 PTW 0 0 0 0 23,999 21,818 WTW 551 520 512 540 29,161 26,604 GHGs (g CO2e/mi ) WTP -1,299 -1,116 -1,102 -1,147 640 471 PTW 1,439 1,439 1,439 1,439 1,439 1,729 WTW 139 323 336 292 2,079 2,200 GHG Emissions Reduction Relative to Diesel Vehicles -94% -85% -85% -87% -6% 3.3 SENSITIVITY ANALYSIS Detailed sensitivity analyses of WTW GHG emissions per MJ from AD -based CNG pathways are presented below . Results are shown for mixed plug flow AD reactors, the most common type currently in use in the United States (U.S. EPA, 2011b). Bars correspond to the deviations in GHG emissions due to replacing GREET default inputs with l ow and high values for the parameters shown . The se values are define d as 90% and 110% of the average values reported . As shown in Figure 14 , two parameters related to the reference case, the share of flar ed controllable CH 4 and location, dominate impacts on WTW GHG emissions in AD -based renewable CNG pathways. When 81% of the controllable CH 4 in the reference case (from solid storage, liquid/slurry, anaerobic lagoon and deep pit ) is flared (10% lower than the baseline), the emission credits in the reference case increase owing to the avoidance of more CH 4 emissions , and WTW GHG emissions from renewable CNG drop by 130 % (meaning net GHG sequestration). Con versely, if the share of flar ed controllable CH 4 in the reference case increases by 10% (to 99%), the emission credits in the reference case decrease, and WTW GHG emissions from renewable CNG increase by 130 %. In 2009, only 1.1 Tg CO 2 e of methane out of 50.6 Tg CO 2 e was eliminated by EPA’s AgS TAR program (U.S. EPA, 2011a, 2011b). Owing to the large uncertainty and impact on GHG emissions, the current share of flar ed controllable CH 4 is a critical environmental i ssue and the dominant factor affecting GREET results . L ocation , which in turn affects MS and MCFs in the reference case , is the second most important factor affecting our results . For California , the reference manure management system emits more GHGs than the baseline case because 1) reference systems are mainly anaerobic lagoons (58%) whose MCF is 75% and 2) annual average temperature is higher than either the
26 FIGURE 14 Sensitivity of GHG Emissions from AD -B ased R enewable C NG P athways (p er MJ) U.S. average or Wisconsin’s average, also resulting in higher MCFs. Owing to the higher GHG emissions from the reference case , AD implemented in California would avoid more GHG emissions than on average in the United States or in Wisconsin, and WTW GHG emissions of AD -based CNG pathways in California result in a 118 % decrease in GHG emissions as compared to a California reference case (meaning net GHG sequestration). Con versely, the reference case in Wisconsin e mits less GHGs than the average in the United States , which results in WTW GHG emission s in Wisconsin increas ing by 62 % as compared to the U.S. average case . In addition, AD in Wisconsin would require greater process heat to warm up manure. This study does not take into account the different heat demands by location , which would make the variation wider. The i ndirect N 2 O loss factor from AD residue also has a large impact on WTW GHG emissions. Even though there are only small differences in N 2 O loss f actors between the reference case and the AD pathways (shown in Figure 9 ), the high global warming potential of N 2 O (298 time s that of CO 2 ) produces a considerable increase in GHG emissions. R esults for MCFs appear counterintuitive —with lower MCFs producing lower GHG emissions —but can be explained easily . With reduced MCFs, more manure is needed to produce 1 MJ of renewable CNG. As manure input increases, 1) larger emission burdens from increased process energy demands and 2) larger emission cr edits from the reference case are incurre d. Since emission credits are much larger than emission burdens , net WTW GHG emissions decline as MCFs drop . T his illustrates a tradeoff between prod uctivity and GHG emissions , an important topic for further analysi s . -80%-60%-40%-20%0%20%40%60%80%% Flared Location Indirect N2O from Residue MCF of AD % Seq. of C in AD Residue CH4 Loss: NG Processing Process Energy MCF of AD Residue Changes relative to the baseline mixed plug flow case Low High CNG -130%-118%,CA 130%WI, 62%-120%120%
27 4 CONCLUSIONS This report documents a WTW analysis of RNG from animal waste and compare s resulting energy use, fossil fuel use and GHG emissions to those for conventional NG and gasoline pathways . A reference case wa s defined from current manure management practices and differences between it and AD -based pathways were determined . C ritical issues, including nutrient recovery and other emissions (N 2 O) from soil application , are examined in the context of constructing the reference and AD pat hways . On the basis of data and assumptions from the literature , a ll RNG pathways show significantly less fossil fuel consumption and GHG emissions than conventional fossil NG and gasoline. Assuming that 90% of controllable CH 4 (from solid storage, liquid/slurry, anaerobic lagoon and deep pit) in current manure management systems is flared and that U.S. average MSs and MCF s are achieved , GHG emission reductions of 81 –91 % on a per -MJ basis are estimated for AD -based renewable C NG relative to petroleum gasoline , depending on reactor types. Similarly, GHG emission reductions for AD -based renewable LNG relative to petroleum diesel on a per -MJ basis are estimated to be 86 –94 %. GHG emission reductions by AD -based pathways vary widely depending on the reference case, the indirect N 2 O loss factors from both AD and AD residue , and the MCF s of AD . The most critical factor appears to be the share of flar ed controllable CH 4 in the reference case because the flaring of bio -methane reduces GHG emission by a factor of nine . Location, which in turn determin es MS and MCFs in the reference case , is nearly a s important as the share of flar ed controllable CH 4 . Unfortunately, estimates for all these parameters are limited , coverage is spotty and resulting assumptions are highly uncertain . Clearly, m ore reliable data would provide greater precision and certainty in WTW analysis of AD pathways. This analysis represents an important step in understanding the environmental benefits of AD and renewable gas. AD is promising not only because of its environmental benefit s , but also because of the productivity of biogas. Many other opportunities and pathways, in addition to those based on animal manures, are possible. For example, WWT facilitie s are a major producer o f bio -methane and a major consumer of electricity and heat . In 2007, a mong over 16,000 WWT facilities operating in the United States , only 544 utilize d AD to treat wastewater (U.S. EPA, 2007). M oreover, only 106 WWT facilities produce d electricity or heat from AD . If all 544 WWT facilities w ith anaerobic digesters produce d electricity , EPA estimates that approximately 340 MW of renewable electricity could be produced annually reducing 2.3 MMT of CO 2 e emissions . C o -digestion o f organic waste with manure or wastewater is another option that has received increasing a ttention as a mean s to increase AD productivity despite challenges of contamination and yield variation. In the United States , 44 out of 167 operating AD projects co -digest organic waste (such as crop waste, food waste and food -processing wastewater) with animal waste (U.S. EPA, 2011b). Renewable fuel pathways based on WWT facilitie s and co -d igestion represent important extensio ns to this work.
28 Finally, while this study assumes a robust market for RN G -based fuel s , market issues are beyond the scope of this analysis. Because there are many renewable sources for electricity but few for NG , R NG may be an increasingly attractive op tion for entities required to implement low -carbon fuel standards and renewable -portfolio standards. Owing to historically low NG price s and recent advances in shale gas technology , projects to produce pipeline -quality NG are less viable in today’s economic climate than they were a few years ago. However, the price differential between NG for stationary applications versus competing motor fuels remains a significant incentive for RNG, as do recently enacted low -carbon fuel standards.
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