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Techno-economic and life cycle analysis of a farm-scale anaerobic digestion plant in Iowa
Waste Management 89 (2019) 154–164
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Techno-economic and life cycle analysis of a farm-scale anaerobic digestion plant in Iowa Alvina Aui, Wenqin Li, Mark M. Wright ⇑ Department of Mechanical Engineering, Iowa State University, Ames, IA 50014, United States
a r t i c l e
i n f o
Article history: Received 24 October 2018 Revised 13 March 2019 Accepted 3 April 2019
Keywords: Techno-economic analysis Life cycle analysis Anaerobic digestion Biogas Renewable natural gas
a b s t r a c t There is growing interest in the use of anaerobic digestion to increase revenues in rural areas and reduce greenhouse gas emissions. This study evaluates the economic and environmental feasibility of a farmscale anaerobic digestion (AD) combined heat and power (CHP) plant co-located with a cattle feedlot. The study evaluates two different scenarios with six cases – Biomass Only (BO) scenario and Biomass and Glycerin (BG) scenario, targeting a power capacity of 950 kWe using combinations of manure, biomass, and crude glycerin. Beef cattle manure with approximately 10.15 wt% of biomass and 10 wt% of glycerin is added into the system. The internal rate of return (IRR) and greenhouse gas emissions (GHG) were calculated for six cases. The IRR ranges between 3.51% and 5.57%, and the GHG emissions range between 82.6 and 498.52 g CO2e/kWh. Glycerin reduces the operating cost by 32%. These results indicate that AD CHP could be profitable at the farm-scale depending on various parameters. Sensitivity analysis indicates that power efficiency, operating capacity and waste generation per cattle have the strongest impact on the IRR, affecting it by over 40%, while glycerin and manure emission factors are the most important for GHG emissions affecting it by over 15%. Uncertainty analysis describes the role of feedstock choice and process performance on minimizing commercialization risks. Ó 2019 Elsevier Ltd. All rights reserved.
1. Introduction Scientists, politicians and activists are actively pursuing solutions to address the challenges of global climate change. In particular, the agriculture sector has been singled-out to be a major contributor of greenhouse gas (GHG) emissions. In 2004, GHG emissions released due to land use change and forestry accounted for 31% of the GHG emissions (Barker et al., 2007). The primary sources of GHGs from agriculture are intensive livestock and fertilizer operations. Intensive livestock and fertilizer operations account for over 50% of the methane, CH4 and almost all the nitrous oxide, N2O emissions respectively (IPCC, 1996). Agriculture alone contributes to a total of 7% of the GHG emissions in the U.S. (Phetteplace et al., 2001). Anaerobic digestion (AD) can transform waste and organic materials into energy, while GHG emissions by converting methane, CH4 into carbon dioxide, CO2 which is a less potent GHG (CARB, 2016). Several studies have estimated the economic performance of a farm scale anaerobic digestion plant. Recent studies by Akbulut (2012) and Klavon et al. (2013) demonstrate that a farm scale AD ⇑ Corresponding author at: Department of Mechanical Engineering, Iowa State University, 2087 Black Engr., 2529 Union Dr., Ames, IA 50011, United States. E-mail address: [email protected] (M.M. Wright). https://doi.org/10.1016/j.wasman.2019.04.013 0956-053X/Ó 2019 Elsevier Ltd. All rights reserved.
plant requires at least 500 cattle to be economically viable. Both studies utilize manure from dairy cattle. The studies also report that the large amount of digestate created from the AD process has economic value, and it can support the sustainability of these systems. In addition to that, tipping fees have also helped reduce the total operating cost and minimum number of cows needed for the plant to be economically viable (Klavon et al., 2013). In Akbulut (2012), the capital cost was estimated to be €10.26 M, and the facility receives a subsidy of €0.1 per kWhe, with a payback time of 3.4 years. It was also reported in Klavon et al.’s (2013) study that the profit obtained from selling livestock bedding is reported to be $100 y1 per cow, which led to a positive cash flow. Additionally, federal, state and local grants, loans, tax exemptions and incentives highly affect the economic viability of a farm scale AD plant. It was reported that over 50% of the cost of the AD plant can be covered through these sources (Lazarus, 2008; AgSTAR, 2012). Similarly, farm scale farms in Ontario, Canada can also take advantage of the Feed-in Tariff (FIT) program which allows farm scale AD plants to be economically attractive (White et al., 2011). Co-digestion can also provide economic benefits by reducing capital costs for the AD system (Usack et al., 2018). Studies have also reported that many AD systems are no longer operating, not due to technical drawbacks, but mostly because
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farmers were unwilling to pay for its substantial costs (Lusk, 1998; Beddoes et al., 2007). Recent studies also investigated the environmental performance of a farm scale AD plant. One of these studies looked at a dairy cattle farm in New York, USA and deduced that AD improves manure management systems on the farm and reduces GHG emissions from storage of manure (Wright et al., 2017; Usack et al., 2018). Similar deduction was made by Agostini et al. (2016) on the author’s study of an AD system for a dairy cattle farm in Italy. Additionally, it has also been studied that while co-digestion of manure with organic waste leads to an increase in environmental emissions on farms, the total life-cycle emissions which includes all indirect emissions were reduced significantly compared to operating without a digester system (Usack et al., 2018). Additionally, the use of digestate from AD in displacing inorganic fertilizers contributes significantly in reducing the GHG emissions (Mezzullo et al., 2013; Lijó et al., 2014; Nayal et al., 2016). Similarly, the adoption of an AD system does not only reduce the emissions from manure management, but also offset GHG emission associated with the burning of fossil fuels for energy. It was reported in the same study that dairy farms can offset 18.9 Mg of CO2e per year from a coal-fired power plant from electricity generation (Kaparaju and Rintala, 2011). While there are a number of studies on the economic and environmental viability of dairy cattle farm scale AD plants, there is limited information on beef cattle farm scale AD plants. Hence, the objective of this study is to evaluate both the economic and environmental feasibility of a farm-scale AD plant co-located with a cattle feedlot, using beef cattle manure and agricultural and industrial residues to generate combined heat and power (CHP). The study investigates AD of cattle manure with three specific feedstock in a local Iowa farm – corn stover, rye, and wheat (planted as cover crops), mixed with crude glycerin which is a by-product from a soybean biodiesel refinery. The study also estimates the internal rate of return (IRR) and GHG emissions. A novel contribution of this study is the uncertainty analysis of the IRR and GHG emissions for various combinations of AD feedstock to help minimize commercialization risk.
2. Methodology This study conducts both a techno-economic (TEA) and life cycle analysis (LCA) of six AD scenarios for CHP applications. Three scenarios involved the conversion of both biomass and glycerin (BG), while the other three involve the conversion of biomass
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and manure only (BO) to heat and power. This system is modelled to produce 950 kW of electricity (kWe) from co-digesting cattle manure, glycerin and biomass such as corn stover, rye, and wheat. Through co-digestion of manure, agricultural, and industrial residues, the system produces biogas, which is combusted to produce heat and power in a gas turbine. This section describes the process design, TEA and LCA methodology for the conversion process.
2.1. Process design Fig. 1 illustrates the process model of the system. The model is designed based on a case study of heat and power generation from a farm-scale AD plant (Akbulut, 2012). The modelled farm has up to 2400 beef cattle, and two digesters with 3670 m3 capacity (Midwest CHP Technical Assistance Partnership). The capacity of the reactor is within the range of a typical farm-scale reactor which is 1000–4000 m3 (Weiland, 2000; Weiland, 2010; Holm-Nielsen et al., 2009). The figure also illustrates the two feed mixtures that are being evaluated in this study. The Biomass Only (BO) case digests only manure and biomass, while the Biomass with Glycerin (BG) case digests manure, biomass and glycerin. The AD process consists of six technical areas such as pre-processing (separation, reduction in size of feedstocks), pre-treatment, which is normally done to enhance the digestibility of the feedstock, mixing of feedstocks, anaerobic digestion, by-products separation, and steam and power generation. Feedstocks used in the AD system often depend on its regional availability. The most common feedstocks used in AD systems in Iowa are manure (47.9 gal/d), food waste (1020 MT/d) and sewage sludge (4.7 mil. gal/d) (American Biogas Council, 2015). Sewage sludge has the largest average of biogas yield, and animal manure, agricultural residues and energy crops have the lowest average of biogas yield (Vasco-Correa et al., 2018). However, studies have shown that co-digestion with crop residues, energy crops and industrial wastes have increased methane production per digester volume and subsequently biogas yields by 16–65% (Lehtomaki et al., 2007; Ye et al., 2013). However, the increase in biogas yield from feedstocks depends on the composition of the feedstock and mixture itself. Glycerin in particular is known to enhance the methane content and biogas yield of AD. This is mainly because it has a high amount of readily biodegradable soluble chemical oxygen demand (COD), which is easily consumed by anaerobic bacteria (Robra et al., 2010; Wohlgemut et al., 2011; Viana et al., 2012). They are also known as methane boosters. Additionally, studies have also
Fig. 1. Simplified block diagram of anaerobic digestion of Biomass Only (BO) and Biomass with Glycerin (BG) for Combined Heat and Power (CHP) generation with life cycle system boundary adapted from Akbulut (2012).
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compared the results of the addition of glycerin from biodiesel production from pure glycerin. The digester did not have any adverse effects from other residual contaminants found in glycerin from biodiesel production. Similarly, the overall behavior of adding equal amounts both pure glycerin and crude glycerin in AD is very much alike. Hence, it is possible to use crude glycerin without additional refining as co-substrates in AD (Wohlgemut et al., 2011; Nghiem et al., 2014). Additionally, this also increases the stability of the process by preventing inhibitors like NH3 and H2S (Wellinger et al., 2013). Due to the location of the modelled AD plant, manure along with agricultural residues such as corn stover, rye and wheat are one of the most available resources in the vicinity of the farm. However, biogas yields can be relatively low when digesting manure or corn stover alone, as reported by Vasco-Correa et al. (2018). Therefore, in this study, manure is co-digested with either agricultural or industrial residue, or both to improve biogas yields. The anaerobic digestion process in this system is modelled as a mesophilic digestion, as it is common for large scale AD plants to operate in this condition (Abbasi et al., 2012). Mesophilic temperatures range from 20 to 40 °C, with 35 °C being the ideal temperature (Monnet, 2003). Although the rate of reaction in chemical processes supposedly increases with temperatures, AD system operating at mesophilic temperatures are known to be more stable when compared to systems operating at thermophilic temperatures (Baldwin et al., 2009). Additionally, thermophilic digestion also requires more energy inputs (Eliyan et al., 2007). Additionally, it has been observed in other studies that farmers operate farmscale AD systems traditionally at lower efficiencies. Hence, a conservative generation potential value of 42% was used in this study to provide a more accurate result (Klavon et al., 2013). The process starts with the feedstocks’ mixture being diluted to under 9 wt% total solids, as this process utilizes a wet digestion process. Wet digestion processes employ feedstocks with less than 20 wt% total solids. Generally, AD is not economically feasible when the solid content of the feedstock is less than 5 wt% due to their low energy content (Baldwin et al., 2009). Then, feedstocks are digested anaerobically, producing biogas which is sent to a CHP unit to be combusted and used to generate electricity and heat, and by-products in solid and liquid form. The by-products consist of solid digestate (6 wt%) and liquid digestate streams. The solids digestate contains 28 wt% organic matter, and the liquid digestate stream contains 2 wt% organic matter (DANETV, 2010). In this system, a fraction of the heat is recycled through the system as process water to maintain the digesters temperature, assuming that the temperature of the feedstocks input is at 8 °C (Akbulut,
2012; Tonini et al., 2016). Similarly, a fraction of the liquid effluent is also cleaned, recycled and heated to provide heat to the system, while the excess is sent back to the farm as fertilizer and livestock bedding. Additionally, the system is able to generate enough electricity to power the plant, while also being able to sell the excess electricity to local utility companies. Using the Energy Research Center of the Netherlands (ECN) Phyllis2 database for biomass and waste and other resources, the proximate and ultimate analysis data for the selected feedstocks were collected and studied. The data was tabulated in Table 1. In this study, a ratio of 5:3 biogas to methane yield in feedstocks is assumed in the absence of detailed gas composition data. This ratio can be found in a few studies (Lizasoain et al., 2017; RodríguezAbalde et al., 2017). Solid manure normally has more than 20 wt % total solids (Lorimor et al., 2004). Hence, a moisture content of 88 wt% for manure was chosen. Corn stover, rye and wheat are assumed to have a moisture content of 60 wt% (Shinners et al., 2007). 2.2. Techno-economic analysis The economic performance of the system is evaluated based on a modified version of the multi-year discounted cash flow rate of return (DCFROR) analysis developed by the National Renewable Energy Laboratory (NREL). The analysis consists of estimating the capital cost, operating costs, and calculating the internal rate of return (IRR). Capital costs are based on equipment cost estimates by NREL in Golden, Colorado, USA (Humbird et al., 2011), and project installation factors provided by Peters et al. (2004). A financial spreadsheet is designed to incorporate all aspects of the economic analysis. The equipment includes anaerobic digesters, mixers, pumps, reactors, combined heat and power units, and heat exchangers. Employing the Economy of Scale Law in capital cost as described by Jenkins (1997), shown in Eq. (1), the cost of the equipment was scaled accordingly based on base costs and mass capacities obtained from Humbird’s et al. (2011) study. The capital costs include installation costs for items such as piping, buildings, and instrumentation; indirect costs such as engineering design and legal fees; and expenses for land and working capital.
n C2 M2 ¼ C1 M2
ð1Þ
where C = the cost of facility, M = mass capacity and n = scaling exponent. For power generator, n = 0.72 (Daugaard et al., 2015), for all other equipment, n = 0.6 (Peters et al., 2004). A cost for storage of liquid by-products was also included in this study. The cost
Table 1 Ultimate & proximate analysis data. Feedstock Manure Corn Rye Wheat Glycerin a b c d e f g h i j k l
Moisture content (%) a
88 60d 60d 60d –
Volatile solids (kg/kg) a
0.85 0.94e 0.96g 0.98i 1.00k
Lorimor et al., 2004. ECN Phyllis2, 2017. Wellinger et al., 2013. Shinners et al., 2007. Li et al., 2013. Lizasoain et al., 2017. Li et at., 2015. National Non-Food Crops Centre (NNFCC), 2011. Cui et al., 2011. Montero et al., 2016. Aguilar et al., 2017. Bohon et al., 2011.
HHV (MJ/ton) b
20,000 18,880a 17,020a 17,678j 16,000l
Biogas potentials (m3/ton) c
333 585f 387.5h 405h 306k
Methane potentials (m3/ton)
Carbon content (%)
200 348 232.5 243 183.6
0.39b 0.44e 0.49b 0.43i 0.88k
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was estimated based on the total heads of cattle per farm and the average liquid effluent produced per head as suggested by the Natural Resource Conservation Service (United States Department of Agriculture, 2003). Operating costs are expenses related to the continuous operation of a business or plant. Operating costs are based on material and energy flows gathered from the process design and prices taken from various sources. With both capital and operating costs estimated, the IRR can be computed using the DCFROR analysis. To analyze the profitability of the plant, the IRR concept is used as a valuation criterion. The IRR estimates a value of the profitability of an investment at a zero net present value (NPV). The key financial assumptions are tabulated in Table 2. All costs calculated in this study are presented on a 2011 basis. Table 3 tabulates the additional assumptions used in estimating the operating costs. Additional economic assumptions include a common feedstock cost on the different agricultural residues considered in this study. Feedstock cost is assumed at $20/MT which also includes logistic cost, with a 5-mile availability radius estimated by the DOE U.S. Billion-Ton study (U.S. Department of Energy, 2011). Raw manure is taken from the cattle operation at a $5/MT opportunity cost (Leibold and Olsen, 2007). Glycerin was assumed to be available at no additional cost based on a negligible tipping fee. In the early 1990s, tipping fees in Iowa have ranged from $0 to $35/MT, and have been increasing since (Iowa Association of Naturalist, 2018). By converting waste into useful energy, the project is also eligible for Renewable Electricity Production Tax Credit (PTC), which allows companies to claim $0.015 per kWhe generated through renewable sources (Bipartisan Act, 2018) and by-product credit claims. The solid and liquid by-products receive $35.25/MT and $2.64/MT credit based on their nitrogen, phosphorus, and potassium content as estimated by (Leibold and Olsen, 2007; Sievers, 2018). Financial incentives have improved the growth of AD installation all over the world (Lisowyj and Wright, 2018). As of today, there are 29 European countries that provide incentives for electricity generation from biogas. For instance, Germany had around 7000 AD plants in the year 2010 (Dressler et al., 2012) that receive between 12 and 25€¢/kWh of electricity generated by biogas (Whiting and Azapagic, 2014). Italy is another European country where AD plants can benefit from similar incentives. Italian AD plants can claim incentives of 8.5–23 €¢/kWh of electricity generated from biogas as well (Whiting and Azapagic, 2014). The cost of electricity in this study is also assumed to be $0.06/kWh. This cost is within the range of $0.06–$0.23/kWh similar to other studies (Akbulut, 2012; Balussou et al., 2012; Vasco-Correa et al., 2018). The cost of electricity in the state of Iowa is about $0.13/kWh (Energy Information Administration, 2018).
Table 2 Financial assumptions (Humbird et al., 2011). Parameter
Assumptions
Equity Plant life Construction period Depreciation period Working capital Plant salvage value Startup time Revenue & cost during startup (% of Normal)
40% 30 years 2.5 years 7 years, 200 DDB 15% of Fixed Capital Cost 0 0.5 years Revenue: 50% Variable cost: 75% Fixed cost: 100% 7.5%/year 39% $0.064/kWh
Interest rate for financing Income tax rate Electricity price
Table 3 Operating costs assumptions. Material
Cost
Reference
Biomass Manure Glycerin Solids handling Solid digestate credit Liquid effluent credit Renewable tax
$20/MT $5/MT $0/MT $5/MT $(35.25)/MT $(2.64)/MT $(0.015)/ kWhe
U.S. Department of Energy (2011) Leibold and Olsen (2007) Iowa Association of Naturalist (2018) Leibold and Olsen (2007) Leibold and Olsen (2007) Sievers (2018) Bipartisan Budget Act
Credit
(2018)
2.3. Life cycle analysis Life Cycle Analysis (LCA) is a very common and established method for evaluating the environmental impacts of a products life. This cradle-to-gate LCA study includes all stages of the life cycle such as material extraction to processing and finally the ‘point of substitution’. In this study, electricity is the main product and is being produced as a service, hence, the point of substitution is defined as electricity generated from the power plant (1 kWh). In different scenarios whereby transportation fuel is the main product and being produced as a service, then the point of substitution is the energy input to the vehicle (Tonini et al., 2016). The LCA study employed in this study follows the International Organization for Standardizations (ISO) 14,040 and 14,044 standards (ISO, 2006a,b), similar to other LCA studies (Whiting and Azapagic, 2014; Tonini et al., 2016; Li et al., 2018). The SimaPro 7.3.3 software (2008) is used in this study to evaluate the GHG emissions for an AD farm-based plant. The functional unit in this study is chosen to be 1 kWh of electricity (kWhe). The system boundary of this LCA study is represented as the dashed lines, as shown in Fig. 1, covers all processes from cradle-to-gate such as feed production, transportation and mixing of feedstocks, anaerobic digestion, separation of by-products, and biogas combustion for heat and power. The system boundary also encompasses the displacement impacts of all the production, transportation and application of avoided mineral fertilizers based on soil application of the digestate. Additionally, the system boundary also includes the electricity and heat used by the plant. All the electricity used to power the plant was assumed to be generated from the plant itself. The inventory of the LCA study and its sources are tabulated in Table 4 below. Biomass emission factors were gathered from SimaPro except for beef cattle manure. Beef cattle manure emission factors were gathered from the U.S. Department of Agriculture (1995) report. GHG emission factors for corn stover include the emissions from seed production, tillage, nitrogen fertilizer and pesticide application, residue management, irrigation and harvesting. There have been many debates on the different allocation methods used in LCA studies. Additionally, GHG emissions for feedstocks not grown specifically as energy crops will vary due to the different allocations applied. Luo et al. (2009) reported that mass and energy allocation to assess corn stover gives better scores in terms of GHG
Table 4 Greenhouse gas emissions by input. Inputs
GHG emissions (kg CO2e/ kg input)
Reference
Manure Corn Rye Wheat Glycerin
0.0741 0.0377 0.00685 0.0401 2.49
Gao et al. (2014) SimaPro (2008) SimaPro (2008) SimaPro (2008) SimaPro (2008)
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emissions compared to economic allocation. The study also observes that the global warming potentials are dependent significantly on the allocation method chosen. In this study, a system expansion approach studied by Kim et al. (2009) was used in the allocation of environmental burden of corn and corn stover. The same allocation method is also employed for rye and wheat on SimaPro. GHG emissions for glycerin in the SimaPro database includes processes such as esterification of vegetable oil to methyl ester and glycerin, storage of the products and treatment of wastewater effluents. SimaPro’s default allocation on estimating its GHG emission is based a ratio of 92:8 of soybean oil to glycerin using an economic allocation method (SimaPro, 2008). Due to the lack of information on AD of beef cattle manure, the SimaPro model was validated using LCA studies on AD with dairy cattle manure. The emissions of dairy cow manure were obtained from data released by the state of California Air Resources Board (CARB) (2016). Using the data from CARB, the total GHG emissions for generating electricity from AD is 220 g Carbon Dioxide equivalent (CO2e). This value is within the range of 395 and 128 g CO2e provided by a study on GHG emission factors (Tonini et al., 2016). The model was also validated using another study to compare the GHG emissions per m3 of biogas generated, which is also within the given range of 4 and 3 kg CO2e per m3 of biogas (Vasco-Correa et al., 2018).
Table 5 Sensitivity analysis variables and pessimistic, base, and optimistic case data. Variables
Pessimistic Case
Base Case
Optimistic Case
Power efficiency (%) Operating capacity (%) Capital cost ($MM) Waste per cattle (tons/day) Manure price ($/ton) Solid digestate price ($/ton) Biomass price ($/ton) Glycerin price ($/ton) Liquid effluent price ($/ton) Biomass emission factor (kg CO2e/kg input) Glycerin emission factor (kg CO2e/kg input) Organic matter emission factor (kg CO2e/kg input) Manure emission factor (kg CO2e/kg input)
33.4 42 68 85 3.75 3.12 0.028 0.035 6 5 28.20 35.25 24 20 10 0 2.11 2.64 *Varies by feedstock
50.4 102 2.50 0.042 4 42.30 16 10 3.17
1.992
2.49
2.988
0.006
–0.0075
0.009
0.059
–0.074
0.089
2.4. Sensitivity and uncertainty analysis Sensitivity analysis was conducted to quantify the impact of the most significant parameters on the process profitability (IRR) and environmental performance (GHG emissions). A sensitivity coefficient, which is the percent change in outputs per inputs around a specific baseline value (Hamby, 1994) was used in the sensitivity analysis. In this study, a sensitivity coefficient of ±20% was used on key variables such as the power efficiency, operating cost, capital costs, waste per cattle, manure price, solid digestate price, liquid effluent price and biomass price. Uncertainty analysis investigates the impact of variability in the key model parameters. Variables used in the sensitivity and uncertainty analysis are similar. A Monte Carlo simulation is employed in this study to account for the uncertainties in the analysis. A triangular distribution is assigned to all variables in this simulation. A triangular distribution was chosen due to limited sample data in the variables. Then, a large data set consisting of at least 10,000 data points are randomly generated and incorporated into the financial spreadsheet. The simulation then iterates to create unique combinations of all parameters. This investigates the probability of variations in multiple parameters at the same time. The output of a Monte Carlo simulation is sampling points that can be fitted into a probability distribution. Table 5 tabulates the key variables and its assumptions used for studying the sensitivity and uncertainty in operating parameters for the economic analysis and environmental analysis respectively. 3. Results and discussion 3.1. Process design Fig. 2 illustrates the results of the mass, energy and carbon balance of the system using corn stover as biomass feedstock in the BG scenario, while Fig. 3 closely examines just the mass flows of the systems for both the BO and BG scenario using corn stover as feedstock. In all scenarios, the same amount of biomass is used, which is approximately 10.15 wt% of the raw manure. In the BG scenario, some of the manure is replaced with about 10 wt% of glycerin to produce the same power capacity of 950 kWe. This
Fig. 2. Mass and energy results of the Biomass and Glycerin (BG) AD scenario with corn stover: (a) Mass flows (tons per day); (b) Energy flows (MJ per day); (c) Carbon flows (tons per day).
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Fig. 3. Mass flows (MT/d) of the Biomass Only (BO) and Biomass with Glycerin (BG) anaerobic digestion for corn stover scenario.
amount of glycerin was added to the AD process because it was proven to be most effective in increasing biogas production and methane content when added to cattle slurry (Robra et al., 2010). Higher glycerin input such as 15 wt% contributes to process instability and can cause system failure. This includes thermic shock of the AD bacteria and drops of more than 50% in biogas production. In all scenarios, the AD system can produce 950 kWe. In the BO scenario, without the addition of glycerin, an average increase of 57% of raw manure input is required to produce the same amount of electricity. Both BO and BG scenarios also generate approximately 8335 m3/d of biogas from 132 and 90 MT respectively, assuming a 42% gas turbine power efficiency. These values are comparable to those reported by the Italian Agroforestry Energy Associations (AIEL) case study on biogas (Mezzadri and Francescato, 2008). The case study reports a biogas yield of 189,000 m3 from a 4418 MT of digester input consisting of cattle slurry and maize. An increase of 47% is observed in digester input in the BO scenario to produce the same amount of biogas. Both scenarios also produce by-products in the form of solid digestate and liquid effluent of 122 and 80 MT respectively. The by-products are then separated into liquid and solid fractions to reduce transport cost (Sanscartier et al., 2012). Both solid digestate and liquid efflu-
Fig. 4. Operating costs of Biomass Only (BO) and Biomass with Glycerin (BG) scenarios by feedstock type.
ent contain carbon, nitrogen, phosphorus, potassium and various soil nutrients which can be employed on-site as fertilizers. This is because the organic nitrogen in the substrates is converted into ammonia during digestion, which is an organic form easily consumed by plants. Additionally, phosphorus and potassium levels are also maintained. Hence, the potential of manure as fertilizers is increased by the process of digestion (Heaton et al., 2004). This also further reduces farm operating costs and the use of artificial fertilizers. Additionally, using these by-products as fertilizers are also a beneficial option for the environment, as it allows nutrients to be recovered and reduces the loss of organic matter suffered by soils from agriculture exploitation (Gómez et al., 2007). This system was also modelled with a continuous demand of electricity and heat to operate equipment such as mixers, blowers, pump, and maintain the digesters at mesophilic temperatures. To ensure the assumptions were kept constant, parasitic energy loads were computed similarly in both cases. Hence, the system has an
Table 6 Internal rate of return (%) by feedstock type and Biomass Only (BO) and Biomass with Glycerin (BG) scenarios. Biomass
BO
BG
Corn Stover Rye Wheat
3.51 5.57 5.56
3.69 5.25 5.22
Fig. 5. GHG emissions by source for Biomass Only (BO) and with Glycerin (BG) scenarios.
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average parasitic electricity consumption that is equal to 14% of the electricity generated by the system and 15% of heat consumption is required to heat substrates to mesophilic temperatures before digestion. Using the study by Li et al. (2018), the parasitic load can be calculated using a factor of 0.0082 kWh/kg of input to the system. This yields a parasitic load of 137 kW. Both scenarios also generate sufficient heat to heat up recycled process water. This will eventually lower operating costs as heat does not need to be purchased from an outside source. To approximate the amount of heat required to maintain the digester at mesophilic temperatures, it is assumed that heat is added into the system via feedstock and recycled process water. Through this it is assumed that the heat
load required by the system is 152 kW. Approximately 43% of the steam is recycled in hot water form to create a slurry for the digester input, and to maintain the digester temperature. The amount of electricity generated by the system translates to 0.40 kWe/cattle. After including both the parasitic and heat consumption, the system generates a net energy of 12.53 GWh annually. 3.2. Techno-economic analysis The capital costs for a farm-scale anaerobic digestion plant is estimated at $3.12 million (MM), with the digester itself contributing to the majority of the cost. We assumed the same capital costs
Fig. 6. Carbon dioxide equivalent (CO2e) flows for Corn Stover: (a) BO and (b) BG case from SimaPro.
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for all scenarios. The digester itself is expensive due to its large capacity and technical considerations when constructing the digester. The capital cost translates to an expense of $0.44/kWhe, which is comparable to the costs reported by FarmWare 3.1, where capital cost is approximately $0.50/kWhe (Enahoro and Gloy, 2008). The breakdown of the operating costs for all feedstock and scenarios is shown in Fig. 4, which includes both variable and fixed costs. The variable costs include the cost of raw materials, waste handling, utilities and by-products credits, while the fixed costs include the costs of labor, depreciation, insurance and taxes. The by-product credits are primarily from reusing both the liquid effluent and solid digestate as fertilizers on the farm. This reduces the farm operating cost as there is no need to purchase artificial fertilizers. Besides by-product credits, the plant is also capable in producing enough electricity to power the whole system, hence, the purchase of electricity is also avoided but not included as part of the renewable electricity tax credit. The operating cost for the BO scenario is greater than the BG scenario. The major contributor of the operating cost is the cost of labor and maintenance which is 49–54% of the total operating costs. The renewable tax credit also contributes to an average of 40% of the total operating cost in all scenarios. Cost of manure is 11–43% of the total operating cost. The average cost of manure increases by 23% when glycerin is absent from the AD process However, this is mainly due to the addition of more manure into the digester in BO vs. BG scenario, as the amount of biomass input is the same in both scenarios. The liquid effluent credit ranges from $(0.002) to $(0.017)/kWhe and is 3–27% of the total operating costs. Using the capital and operating cost, the IRR is computed using the DCFROR analysis. The IRR for all feedstock in the different cases varies accordingly. As observed in Table 6, the average IRR in BG scenarios is lower than the BO scenarios. However, the BO scenarios have a lower NPV compared to the BG scenarios. 3.3. Life cycle analysis Fig. 5 shows the total GHG emission distributions for all feedstocks in their respective scenarios. The red dashed line in the figure represents the average GHG emissions for the US electricity
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grid, which is 540 g CO2e/kWh by the GREET model (Argonne National Laboratory, 2017). There is an average of 88% and 12% of GHG emissions reduction in BO and BG scenarios respectively. The GHG emissions for the BG scenarios are positive emissions and 10 times greater in compared to the emissions from the BO scenarios. This is mainly due to the high emission factor from glycerin which is mainly due to soybean production practices and indirect land use. All cases would reduce emissions when compared to the US electricity grid. Displaced emissions include the avoidance of artificial fertilizer in the form of digester materials and recycled heat to the process in the form of steam. In SimaPro, these aspects were computed as avoided emissions using the system boundary expansion method. Fig. 6(a) and (b) are tree diagrams acquired from SimaPro which details the emission distribution with a node cut-off point of 0.13% to put an emphasis on significant GHGcomponents in the analysis. 3.4. Sensitivity and uncertainty analysis The sensitivity analysis was conducted on various parameters on the economic and environmental performance for the system, as shown in Figs. 7 and 8 respectively. Both the operating capacity and power efficiency have the most impact on the IRR in all cases. An increase in operating capacity from 68% to 85% can cause an increase in the IRR by 2 times. Similarly, an increase in power efficiency from 42% to 50% can increase the IRR from 3.69 to 5.13. The biomass price is the least significant parameter as observed from the tornado plots. A decrease in biomass price from $20/MT to $10/MT, only increases the IRR from 3.69 to 4.04. All values are evaluated based on corn stover in the BG scenario. Since the general trend for all scenarios are similar, hence the effect on IRR for all feedstock in all scenarios are also expected to be along the same lines. Additionally, parameters that could significantly affect the environmental impacts of the system are power efficiency, glycerin and manure emission factors. For the BG scenario, the glycerin emission factor is the most significant parameter, while in the BO scenario, manure emission factor is most significant. By decreasing glycerin’s emission factor from 2.49 kg CO2e/kg input to 1.99 kg
Fig. 7. Internal rate of return sensitivity analysis.
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Fig. 8. Lifecycle greenhouse gas emissions sensitivity analysis.
CO2e/kg input, total emissions decrease by 20%. In addition to that, an increase of 20% in power efficiency also decreases total emissions by 17%. The results also show that biomass emission factor has the least substantial effect, whereby a decrease in 20% of the emission factor, only decreases the total emissions by 0.78%. In the BO scenario, a decrease in manure emission factor from 0.0741 kgCO2e/kg input to 0.059 kgCO2e/kg input, decreases emission factor by 28%, while increasing power efficiency by 20%, also caused a decrease in total emissions by 16%. The least significant parameter is the organic matter emission factor whereby if increased by 20%, will only decrease the total emissions by 0.86%. Similarly, all values for both scenarios are evaluated using corn stover. The trend for all cases is similar, hence it is expected that the results for the other feedstock will also generally be the same. The probability density functions for the economic and environmental performance for the different AD systems are shown in violin plots in Fig. 9. It can be observed that the average of NPV for feedstocks is greater in the BG than BO scenario for the given assumptions. For both rye and wheat in the BG scenario, there is a probability of 86–100% that the project achieves a positive NPV, while for corn with glycerin, there is a 37% probability that the NPV is positive. For the BO scenarios, there is a 90% or greater probability that the NPV is negative. Additionally, the results of the uncertainty analysis on the total GHG emissions show that the median of GHG emission from corn stover (BG) is approximately 300 g CO2e/kWh, which is lesser than 540 g CO2e/kWh. There is also a higher probability that the emissions will fall between 300 and 400 g CO2e/kWh. For rye and wheat in the BG scenario, the median of GHG emission is approximately 850 and 900 g CO2e/kWhe. On the contrary, for feedstocks in the BO scenarios, there is a 100% probability that the total emissions are negative and below the emissions of the average U.S. electricity grid emissions. 4. Conclusions This study evaluated both economic and environmental impacts of the anaerobic digestion system. Two scenarios were compared, and the economic analysis was evaluated based on a DCFROR analysis method. The capital cost of the system is computed to be $0.44/kWhe. Additionally, the analysis also showed that feedstocks
Fig. 9. Uncertainty analysis results for net present value and total greenhouse gas emissions of Biomass Only (BO) and Biomass with Glycerin (BG) anaerobic digestion scenarios.
in the BG scenario had a greater NPV despite a lower IRR than the feedstocks in the BO scenario. In this case, the NPV is a preferred method in accessing economic performance as it is a more meaningful indicator for a capital-budgeting decision. It can also be concluded that the addition of glycerin to the digestion process reduces the amount of manure input, while having the same power capacity. Glycerin of 10 wt% is used in the system because it is proven to significantly increase methane and biogas yields. Codigestion of glycerin with manure and biomass, reduces operating costs by 32% and increases the Return on Investment (ROI) by 27%. The LCA also concludes that generating power and heat via an AD system can reduce the total GHG emissions in compared to generating power and heat from a coal power plant. Sensitivity analysis deduced that operating capacity and power efficiency has the most impact on the economic performance of the system, increasing the IRR by an average of 44% and subsequently the economic feasibility of the plant. Besides that, key variables such as power efficiency and manure or glycerin emission factors can significantly reduce
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the environmental impact by an average of 18%. By increasing power efficiency and decreasing manure or glycerin emission factor, the environmental impacts of an AD system can be improved as well. Acknowledgments This project was supported by the Iowa Economic Development Authority (grant number 17ARRA001ISU). We would also like to thank Bryan Sievers for providing information regarding the operation of his anaerobic digestion system. References Abbasi, T., Tauseef, S., Abbasi, S., 2012. Anaerobic digestion for global warming control and energy generation. An overview. Renew. Sustain. Energy Rev. 16 (5), 3228–3242. Agostini, A., Battini, F., Padella, M., Giuntoli, J., Baxter, D., Marelli, L., Amaducci, S., 2016. Economics of GHG emissions mitigation via biogas production from Sorghum, maize and dairy farm manure digestion in the Po valley. Biomass Bioenergy 89, 58–66. https://doi.org/10.1016/J.BIOMBIOE.2016.02.022. Aguilar-Aguilar, F.A., Nelson, D.L., Pantoja, L.A., Santos, A.S., Aguilar Aguilar, F.A., Nelson, D.L., Santos, D., 2017. Study of anaerobic co-digestion of crude glycerol and swine manure for the production of Biogas Estudo da Codigestão Anaeróbia de Glicerol Bruto e Dejeto Suíno para Produção de Biogás Study of Anaerobic Codigestion of Crude Glycerol and Swine Manure. Rev. Virtual Quim 6. https://doi. org/10.21577/1984-6835.20170142. AgSTAR, 2012. Funding On-Farm Anaerobic Digestion. Retrieved October 4, 2018 from. Akbulut, A., 2012. Techno-economic analysis of electricity and heat generation from farmscale biogas plant: Cßißcekda case study. Energy 44 (1), 381–390. American Biogas Council, 2015. Biogas State Profile: Iowa Biogas Potential. Retrieved October 4, 2018 from . Argonne National Laboratory, 2017. Argonne GREET Model Retrieved from https://greet.es.anl.gov/, . Baldwin, S., Lau, A., Wang, M., 2009. Development of a Calculator for the Technoeconomic Assessment of Anaerobic Digestion Systems. Technical report, British Columbia, Canada. Balussou, D., Kleyböcker, A., McKenna, R., Möst, D., Fichtner, W., 2012. An economic analysis of three operational co-digestion biogas plants in Germany. Waste Biomass Valorization 3 (1), 23–41. Barker, T., Bashmakov, I., Bernstein, L., Bogner, J.E., wa Githendu, M., Bashmakov, I., Bernstein, L., Bogner, J.E., Bosch, P.R., Dave, R., Davidson, O.R., Fisher, B.S., Gupta, S., Halsnaes, K., Heij, G., Kahn Ribeiro, S., Kobayashi, S., Levine, M.D., Martino, D. L., Masera, O., Metz, B., Meyer, L.A., Nabuurs, G.-J., Najam, A., Nakicenovic, N., Rogner, H., Roy, J., Sathaye, J., Schock, R., Shukla, P., H Sims, R.E., Smith, P., Tirpak, D.A., Urge-Vorsatz, D., Zhou, D., 2007. In: Climate Change 2007: Mitigation. Contribution of Working Group III to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change. Technical report, Cambridge, United Kingdom and New York, NY, USA. Retrieved October 4, 2018 from . Beddoes, J.C., Bracmort, K.S., Burns, R.T., Lazarus, W.F., 2007. Production Costs from Anaerobic Digestion Systems on U.S. Livestock Production Facilities. Technical report. Retrieved October 4, 2018 from . Bipartisan Act (2018). H.R. 1892, 115th Congress. Bohon, M.D., Metzger, B.A., Linak, W.P., King, C.J., Roberts, W.L., 2011. Glycerol combustion and emissions. Proc. Combust. Inst. 33 (2), 2717–2724. https://doi. org/10.1016/j.proci.2010.06.154. California Air Resources Board, 2016. Evaluation of Dairy Manure Management Practices for Greenhouse Gas Emissions Mitigation in California Retrieved October 4, 2018 from https://biomass.ucdavis.edu/wp-content/uploads/ARBReport-Final-Draft-Transmittal-Feb-26-2016.pdf, . Cui, Z., Shi, J., Li, Y., 2011. Solid-state anaerobic digestion of spent wheat straw from horse stall. Bioresour. Technol. 102 (20), 9432–9437. https://doi.org/10.1016/j. biortech.2011.07.062. Daugaard, T., Mutti, L.A., Wright, M.M., Brown, R.C., Componation, P., 2015. Learning rates and their impacts on the optimal capacities and production costs of biorefineries. Biofuels, Bioproducts Biorefining 9 (1), 82–94. DANETV, 2010. Verification Statement for GEA Westfalia Decanter Centrifuge for Post-treatment of Digested Biomass. Danish Centre for Verification of Climate and Environmental Technologies (DANETV). Retrieved from www.etvdenamrk.com. Dressler, D., Loewen, A., Nelles, M., 2012. Life cycle assessment of the supply and use of bioenergy: impact of regional factors on biogas production. Int. J. Life Cycle Assess. 17 (9), 1104–1115. ECN Phyllis2, 2017. The composition of biomass and waste. Retrieved October 4, 2018 from
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Techno-economic and life cycle analysis of a farm-scale anaerobic digestion plant in Iowa Alvina Aui, Wenqin Li, Mark M. Wright ⇑ Department of Mechanical Engineering, Iowa State University, Ames, IA 50014, United States
a r t i c l e
i n f o
Article history: Received 24 October 2018 Revised 13 March 2019 Accepted 3 April 2019
Keywords: Techno-economic analysis Life cycle analysis Anaerobic digestion Biogas Renewable natural gas
a b s t r a c t There is growing interest in the use of anaerobic digestion to increase revenues in rural areas and reduce greenhouse gas emissions. This study evaluates the economic and environmental feasibility of a farmscale anaerobic digestion (AD) combined heat and power (CHP) plant co-located with a cattle feedlot. The study evaluates two different scenarios with six cases – Biomass Only (BO) scenario and Biomass and Glycerin (BG) scenario, targeting a power capacity of 950 kWe using combinations of manure, biomass, and crude glycerin. Beef cattle manure with approximately 10.15 wt% of biomass and 10 wt% of glycerin is added into the system. The internal rate of return (IRR) and greenhouse gas emissions (GHG) were calculated for six cases. The IRR ranges between 3.51% and 5.57%, and the GHG emissions range between 82.6 and 498.52 g CO2e/kWh. Glycerin reduces the operating cost by 32%. These results indicate that AD CHP could be profitable at the farm-scale depending on various parameters. Sensitivity analysis indicates that power efficiency, operating capacity and waste generation per cattle have the strongest impact on the IRR, affecting it by over 40%, while glycerin and manure emission factors are the most important for GHG emissions affecting it by over 15%. Uncertainty analysis describes the role of feedstock choice and process performance on minimizing commercialization risks. Ó 2019 Elsevier Ltd. All rights reserved.
1. Introduction Scientists, politicians and activists are actively pursuing solutions to address the challenges of global climate change. In particular, the agriculture sector has been singled-out to be a major contributor of greenhouse gas (GHG) emissions. In 2004, GHG emissions released due to land use change and forestry accounted for 31% of the GHG emissions (Barker et al., 2007). The primary sources of GHGs from agriculture are intensive livestock and fertilizer operations. Intensive livestock and fertilizer operations account for over 50% of the methane, CH4 and almost all the nitrous oxide, N2O emissions respectively (IPCC, 1996). Agriculture alone contributes to a total of 7% of the GHG emissions in the U.S. (Phetteplace et al., 2001). Anaerobic digestion (AD) can transform waste and organic materials into energy, while GHG emissions by converting methane, CH4 into carbon dioxide, CO2 which is a less potent GHG (CARB, 2016). Several studies have estimated the economic performance of a farm scale anaerobic digestion plant. Recent studies by Akbulut (2012) and Klavon et al. (2013) demonstrate that a farm scale AD ⇑ Corresponding author at: Department of Mechanical Engineering, Iowa State University, 2087 Black Engr., 2529 Union Dr., Ames, IA 50011, United States. E-mail address: [email protected] (M.M. Wright). https://doi.org/10.1016/j.wasman.2019.04.013 0956-053X/Ó 2019 Elsevier Ltd. All rights reserved.
plant requires at least 500 cattle to be economically viable. Both studies utilize manure from dairy cattle. The studies also report that the large amount of digestate created from the AD process has economic value, and it can support the sustainability of these systems. In addition to that, tipping fees have also helped reduce the total operating cost and minimum number of cows needed for the plant to be economically viable (Klavon et al., 2013). In Akbulut (2012), the capital cost was estimated to be €10.26 M, and the facility receives a subsidy of €0.1 per kWhe, with a payback time of 3.4 years. It was also reported in Klavon et al.’s (2013) study that the profit obtained from selling livestock bedding is reported to be $100 y1 per cow, which led to a positive cash flow. Additionally, federal, state and local grants, loans, tax exemptions and incentives highly affect the economic viability of a farm scale AD plant. It was reported that over 50% of the cost of the AD plant can be covered through these sources (Lazarus, 2008; AgSTAR, 2012). Similarly, farm scale farms in Ontario, Canada can also take advantage of the Feed-in Tariff (FIT) program which allows farm scale AD plants to be economically attractive (White et al., 2011). Co-digestion can also provide economic benefits by reducing capital costs for the AD system (Usack et al., 2018). Studies have also reported that many AD systems are no longer operating, not due to technical drawbacks, but mostly because
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farmers were unwilling to pay for its substantial costs (Lusk, 1998; Beddoes et al., 2007). Recent studies also investigated the environmental performance of a farm scale AD plant. One of these studies looked at a dairy cattle farm in New York, USA and deduced that AD improves manure management systems on the farm and reduces GHG emissions from storage of manure (Wright et al., 2017; Usack et al., 2018). Similar deduction was made by Agostini et al. (2016) on the author’s study of an AD system for a dairy cattle farm in Italy. Additionally, it has also been studied that while co-digestion of manure with organic waste leads to an increase in environmental emissions on farms, the total life-cycle emissions which includes all indirect emissions were reduced significantly compared to operating without a digester system (Usack et al., 2018). Additionally, the use of digestate from AD in displacing inorganic fertilizers contributes significantly in reducing the GHG emissions (Mezzullo et al., 2013; Lijó et al., 2014; Nayal et al., 2016). Similarly, the adoption of an AD system does not only reduce the emissions from manure management, but also offset GHG emission associated with the burning of fossil fuels for energy. It was reported in the same study that dairy farms can offset 18.9 Mg of CO2e per year from a coal-fired power plant from electricity generation (Kaparaju and Rintala, 2011). While there are a number of studies on the economic and environmental viability of dairy cattle farm scale AD plants, there is limited information on beef cattle farm scale AD plants. Hence, the objective of this study is to evaluate both the economic and environmental feasibility of a farm-scale AD plant co-located with a cattle feedlot, using beef cattle manure and agricultural and industrial residues to generate combined heat and power (CHP). The study investigates AD of cattle manure with three specific feedstock in a local Iowa farm – corn stover, rye, and wheat (planted as cover crops), mixed with crude glycerin which is a by-product from a soybean biodiesel refinery. The study also estimates the internal rate of return (IRR) and GHG emissions. A novel contribution of this study is the uncertainty analysis of the IRR and GHG emissions for various combinations of AD feedstock to help minimize commercialization risk.
2. Methodology This study conducts both a techno-economic (TEA) and life cycle analysis (LCA) of six AD scenarios for CHP applications. Three scenarios involved the conversion of both biomass and glycerin (BG), while the other three involve the conversion of biomass
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and manure only (BO) to heat and power. This system is modelled to produce 950 kW of electricity (kWe) from co-digesting cattle manure, glycerin and biomass such as corn stover, rye, and wheat. Through co-digestion of manure, agricultural, and industrial residues, the system produces biogas, which is combusted to produce heat and power in a gas turbine. This section describes the process design, TEA and LCA methodology for the conversion process.
2.1. Process design Fig. 1 illustrates the process model of the system. The model is designed based on a case study of heat and power generation from a farm-scale AD plant (Akbulut, 2012). The modelled farm has up to 2400 beef cattle, and two digesters with 3670 m3 capacity (Midwest CHP Technical Assistance Partnership). The capacity of the reactor is within the range of a typical farm-scale reactor which is 1000–4000 m3 (Weiland, 2000; Weiland, 2010; Holm-Nielsen et al., 2009). The figure also illustrates the two feed mixtures that are being evaluated in this study. The Biomass Only (BO) case digests only manure and biomass, while the Biomass with Glycerin (BG) case digests manure, biomass and glycerin. The AD process consists of six technical areas such as pre-processing (separation, reduction in size of feedstocks), pre-treatment, which is normally done to enhance the digestibility of the feedstock, mixing of feedstocks, anaerobic digestion, by-products separation, and steam and power generation. Feedstocks used in the AD system often depend on its regional availability. The most common feedstocks used in AD systems in Iowa are manure (47.9 gal/d), food waste (1020 MT/d) and sewage sludge (4.7 mil. gal/d) (American Biogas Council, 2015). Sewage sludge has the largest average of biogas yield, and animal manure, agricultural residues and energy crops have the lowest average of biogas yield (Vasco-Correa et al., 2018). However, studies have shown that co-digestion with crop residues, energy crops and industrial wastes have increased methane production per digester volume and subsequently biogas yields by 16–65% (Lehtomaki et al., 2007; Ye et al., 2013). However, the increase in biogas yield from feedstocks depends on the composition of the feedstock and mixture itself. Glycerin in particular is known to enhance the methane content and biogas yield of AD. This is mainly because it has a high amount of readily biodegradable soluble chemical oxygen demand (COD), which is easily consumed by anaerobic bacteria (Robra et al., 2010; Wohlgemut et al., 2011; Viana et al., 2012). They are also known as methane boosters. Additionally, studies have also
Fig. 1. Simplified block diagram of anaerobic digestion of Biomass Only (BO) and Biomass with Glycerin (BG) for Combined Heat and Power (CHP) generation with life cycle system boundary adapted from Akbulut (2012).
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compared the results of the addition of glycerin from biodiesel production from pure glycerin. The digester did not have any adverse effects from other residual contaminants found in glycerin from biodiesel production. Similarly, the overall behavior of adding equal amounts both pure glycerin and crude glycerin in AD is very much alike. Hence, it is possible to use crude glycerin without additional refining as co-substrates in AD (Wohlgemut et al., 2011; Nghiem et al., 2014). Additionally, this also increases the stability of the process by preventing inhibitors like NH3 and H2S (Wellinger et al., 2013). Due to the location of the modelled AD plant, manure along with agricultural residues such as corn stover, rye and wheat are one of the most available resources in the vicinity of the farm. However, biogas yields can be relatively low when digesting manure or corn stover alone, as reported by Vasco-Correa et al. (2018). Therefore, in this study, manure is co-digested with either agricultural or industrial residue, or both to improve biogas yields. The anaerobic digestion process in this system is modelled as a mesophilic digestion, as it is common for large scale AD plants to operate in this condition (Abbasi et al., 2012). Mesophilic temperatures range from 20 to 40 °C, with 35 °C being the ideal temperature (Monnet, 2003). Although the rate of reaction in chemical processes supposedly increases with temperatures, AD system operating at mesophilic temperatures are known to be more stable when compared to systems operating at thermophilic temperatures (Baldwin et al., 2009). Additionally, thermophilic digestion also requires more energy inputs (Eliyan et al., 2007). Additionally, it has been observed in other studies that farmers operate farmscale AD systems traditionally at lower efficiencies. Hence, a conservative generation potential value of 42% was used in this study to provide a more accurate result (Klavon et al., 2013). The process starts with the feedstocks’ mixture being diluted to under 9 wt% total solids, as this process utilizes a wet digestion process. Wet digestion processes employ feedstocks with less than 20 wt% total solids. Generally, AD is not economically feasible when the solid content of the feedstock is less than 5 wt% due to their low energy content (Baldwin et al., 2009). Then, feedstocks are digested anaerobically, producing biogas which is sent to a CHP unit to be combusted and used to generate electricity and heat, and by-products in solid and liquid form. The by-products consist of solid digestate (6 wt%) and liquid digestate streams. The solids digestate contains 28 wt% organic matter, and the liquid digestate stream contains 2 wt% organic matter (DANETV, 2010). In this system, a fraction of the heat is recycled through the system as process water to maintain the digesters temperature, assuming that the temperature of the feedstocks input is at 8 °C (Akbulut,
2012; Tonini et al., 2016). Similarly, a fraction of the liquid effluent is also cleaned, recycled and heated to provide heat to the system, while the excess is sent back to the farm as fertilizer and livestock bedding. Additionally, the system is able to generate enough electricity to power the plant, while also being able to sell the excess electricity to local utility companies. Using the Energy Research Center of the Netherlands (ECN) Phyllis2 database for biomass and waste and other resources, the proximate and ultimate analysis data for the selected feedstocks were collected and studied. The data was tabulated in Table 1. In this study, a ratio of 5:3 biogas to methane yield in feedstocks is assumed in the absence of detailed gas composition data. This ratio can be found in a few studies (Lizasoain et al., 2017; RodríguezAbalde et al., 2017). Solid manure normally has more than 20 wt % total solids (Lorimor et al., 2004). Hence, a moisture content of 88 wt% for manure was chosen. Corn stover, rye and wheat are assumed to have a moisture content of 60 wt% (Shinners et al., 2007). 2.2. Techno-economic analysis The economic performance of the system is evaluated based on a modified version of the multi-year discounted cash flow rate of return (DCFROR) analysis developed by the National Renewable Energy Laboratory (NREL). The analysis consists of estimating the capital cost, operating costs, and calculating the internal rate of return (IRR). Capital costs are based on equipment cost estimates by NREL in Golden, Colorado, USA (Humbird et al., 2011), and project installation factors provided by Peters et al. (2004). A financial spreadsheet is designed to incorporate all aspects of the economic analysis. The equipment includes anaerobic digesters, mixers, pumps, reactors, combined heat and power units, and heat exchangers. Employing the Economy of Scale Law in capital cost as described by Jenkins (1997), shown in Eq. (1), the cost of the equipment was scaled accordingly based on base costs and mass capacities obtained from Humbird’s et al. (2011) study. The capital costs include installation costs for items such as piping, buildings, and instrumentation; indirect costs such as engineering design and legal fees; and expenses for land and working capital.
n C2 M2 ¼ C1 M2
ð1Þ
where C = the cost of facility, M = mass capacity and n = scaling exponent. For power generator, n = 0.72 (Daugaard et al., 2015), for all other equipment, n = 0.6 (Peters et al., 2004). A cost for storage of liquid by-products was also included in this study. The cost
Table 1 Ultimate & proximate analysis data. Feedstock Manure Corn Rye Wheat Glycerin a b c d e f g h i j k l
Moisture content (%) a
88 60d 60d 60d –
Volatile solids (kg/kg) a
0.85 0.94e 0.96g 0.98i 1.00k
Lorimor et al., 2004. ECN Phyllis2, 2017. Wellinger et al., 2013. Shinners et al., 2007. Li et al., 2013. Lizasoain et al., 2017. Li et at., 2015. National Non-Food Crops Centre (NNFCC), 2011. Cui et al., 2011. Montero et al., 2016. Aguilar et al., 2017. Bohon et al., 2011.
HHV (MJ/ton) b
20,000 18,880a 17,020a 17,678j 16,000l
Biogas potentials (m3/ton) c
333 585f 387.5h 405h 306k
Methane potentials (m3/ton)
Carbon content (%)
200 348 232.5 243 183.6
0.39b 0.44e 0.49b 0.43i 0.88k
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was estimated based on the total heads of cattle per farm and the average liquid effluent produced per head as suggested by the Natural Resource Conservation Service (United States Department of Agriculture, 2003). Operating costs are expenses related to the continuous operation of a business or plant. Operating costs are based on material and energy flows gathered from the process design and prices taken from various sources. With both capital and operating costs estimated, the IRR can be computed using the DCFROR analysis. To analyze the profitability of the plant, the IRR concept is used as a valuation criterion. The IRR estimates a value of the profitability of an investment at a zero net present value (NPV). The key financial assumptions are tabulated in Table 2. All costs calculated in this study are presented on a 2011 basis. Table 3 tabulates the additional assumptions used in estimating the operating costs. Additional economic assumptions include a common feedstock cost on the different agricultural residues considered in this study. Feedstock cost is assumed at $20/MT which also includes logistic cost, with a 5-mile availability radius estimated by the DOE U.S. Billion-Ton study (U.S. Department of Energy, 2011). Raw manure is taken from the cattle operation at a $5/MT opportunity cost (Leibold and Olsen, 2007). Glycerin was assumed to be available at no additional cost based on a negligible tipping fee. In the early 1990s, tipping fees in Iowa have ranged from $0 to $35/MT, and have been increasing since (Iowa Association of Naturalist, 2018). By converting waste into useful energy, the project is also eligible for Renewable Electricity Production Tax Credit (PTC), which allows companies to claim $0.015 per kWhe generated through renewable sources (Bipartisan Act, 2018) and by-product credit claims. The solid and liquid by-products receive $35.25/MT and $2.64/MT credit based on their nitrogen, phosphorus, and potassium content as estimated by (Leibold and Olsen, 2007; Sievers, 2018). Financial incentives have improved the growth of AD installation all over the world (Lisowyj and Wright, 2018). As of today, there are 29 European countries that provide incentives for electricity generation from biogas. For instance, Germany had around 7000 AD plants in the year 2010 (Dressler et al., 2012) that receive between 12 and 25€¢/kWh of electricity generated by biogas (Whiting and Azapagic, 2014). Italy is another European country where AD plants can benefit from similar incentives. Italian AD plants can claim incentives of 8.5–23 €¢/kWh of electricity generated from biogas as well (Whiting and Azapagic, 2014). The cost of electricity in this study is also assumed to be $0.06/kWh. This cost is within the range of $0.06–$0.23/kWh similar to other studies (Akbulut, 2012; Balussou et al., 2012; Vasco-Correa et al., 2018). The cost of electricity in the state of Iowa is about $0.13/kWh (Energy Information Administration, 2018).
Table 2 Financial assumptions (Humbird et al., 2011). Parameter
Assumptions
Equity Plant life Construction period Depreciation period Working capital Plant salvage value Startup time Revenue & cost during startup (% of Normal)
40% 30 years 2.5 years 7 years, 200 DDB 15% of Fixed Capital Cost 0 0.5 years Revenue: 50% Variable cost: 75% Fixed cost: 100% 7.5%/year 39% $0.064/kWh
Interest rate for financing Income tax rate Electricity price
Table 3 Operating costs assumptions. Material
Cost
Reference
Biomass Manure Glycerin Solids handling Solid digestate credit Liquid effluent credit Renewable tax
$20/MT $5/MT $0/MT $5/MT $(35.25)/MT $(2.64)/MT $(0.015)/ kWhe
U.S. Department of Energy (2011) Leibold and Olsen (2007) Iowa Association of Naturalist (2018) Leibold and Olsen (2007) Leibold and Olsen (2007) Sievers (2018) Bipartisan Budget Act
Credit
(2018)
2.3. Life cycle analysis Life Cycle Analysis (LCA) is a very common and established method for evaluating the environmental impacts of a products life. This cradle-to-gate LCA study includes all stages of the life cycle such as material extraction to processing and finally the ‘point of substitution’. In this study, electricity is the main product and is being produced as a service, hence, the point of substitution is defined as electricity generated from the power plant (1 kWh). In different scenarios whereby transportation fuel is the main product and being produced as a service, then the point of substitution is the energy input to the vehicle (Tonini et al., 2016). The LCA study employed in this study follows the International Organization for Standardizations (ISO) 14,040 and 14,044 standards (ISO, 2006a,b), similar to other LCA studies (Whiting and Azapagic, 2014; Tonini et al., 2016; Li et al., 2018). The SimaPro 7.3.3 software (2008) is used in this study to evaluate the GHG emissions for an AD farm-based plant. The functional unit in this study is chosen to be 1 kWh of electricity (kWhe). The system boundary of this LCA study is represented as the dashed lines, as shown in Fig. 1, covers all processes from cradle-to-gate such as feed production, transportation and mixing of feedstocks, anaerobic digestion, separation of by-products, and biogas combustion for heat and power. The system boundary also encompasses the displacement impacts of all the production, transportation and application of avoided mineral fertilizers based on soil application of the digestate. Additionally, the system boundary also includes the electricity and heat used by the plant. All the electricity used to power the plant was assumed to be generated from the plant itself. The inventory of the LCA study and its sources are tabulated in Table 4 below. Biomass emission factors were gathered from SimaPro except for beef cattle manure. Beef cattle manure emission factors were gathered from the U.S. Department of Agriculture (1995) report. GHG emission factors for corn stover include the emissions from seed production, tillage, nitrogen fertilizer and pesticide application, residue management, irrigation and harvesting. There have been many debates on the different allocation methods used in LCA studies. Additionally, GHG emissions for feedstocks not grown specifically as energy crops will vary due to the different allocations applied. Luo et al. (2009) reported that mass and energy allocation to assess corn stover gives better scores in terms of GHG
Table 4 Greenhouse gas emissions by input. Inputs
GHG emissions (kg CO2e/ kg input)
Reference
Manure Corn Rye Wheat Glycerin
0.0741 0.0377 0.00685 0.0401 2.49
Gao et al. (2014) SimaPro (2008) SimaPro (2008) SimaPro (2008) SimaPro (2008)
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emissions compared to economic allocation. The study also observes that the global warming potentials are dependent significantly on the allocation method chosen. In this study, a system expansion approach studied by Kim et al. (2009) was used in the allocation of environmental burden of corn and corn stover. The same allocation method is also employed for rye and wheat on SimaPro. GHG emissions for glycerin in the SimaPro database includes processes such as esterification of vegetable oil to methyl ester and glycerin, storage of the products and treatment of wastewater effluents. SimaPro’s default allocation on estimating its GHG emission is based a ratio of 92:8 of soybean oil to glycerin using an economic allocation method (SimaPro, 2008). Due to the lack of information on AD of beef cattle manure, the SimaPro model was validated using LCA studies on AD with dairy cattle manure. The emissions of dairy cow manure were obtained from data released by the state of California Air Resources Board (CARB) (2016). Using the data from CARB, the total GHG emissions for generating electricity from AD is 220 g Carbon Dioxide equivalent (CO2e). This value is within the range of 395 and 128 g CO2e provided by a study on GHG emission factors (Tonini et al., 2016). The model was also validated using another study to compare the GHG emissions per m3 of biogas generated, which is also within the given range of 4 and 3 kg CO2e per m3 of biogas (Vasco-Correa et al., 2018).
Table 5 Sensitivity analysis variables and pessimistic, base, and optimistic case data. Variables
Pessimistic Case
Base Case
Optimistic Case
Power efficiency (%) Operating capacity (%) Capital cost ($MM) Waste per cattle (tons/day) Manure price ($/ton) Solid digestate price ($/ton) Biomass price ($/ton) Glycerin price ($/ton) Liquid effluent price ($/ton) Biomass emission factor (kg CO2e/kg input) Glycerin emission factor (kg CO2e/kg input) Organic matter emission factor (kg CO2e/kg input) Manure emission factor (kg CO2e/kg input)
33.4 42 68 85 3.75 3.12 0.028 0.035 6 5 28.20 35.25 24 20 10 0 2.11 2.64 *Varies by feedstock
50.4 102 2.50 0.042 4 42.30 16 10 3.17
1.992
2.49
2.988
0.006
–0.0075
0.009
0.059
–0.074
0.089
2.4. Sensitivity and uncertainty analysis Sensitivity analysis was conducted to quantify the impact of the most significant parameters on the process profitability (IRR) and environmental performance (GHG emissions). A sensitivity coefficient, which is the percent change in outputs per inputs around a specific baseline value (Hamby, 1994) was used in the sensitivity analysis. In this study, a sensitivity coefficient of ±20% was used on key variables such as the power efficiency, operating cost, capital costs, waste per cattle, manure price, solid digestate price, liquid effluent price and biomass price. Uncertainty analysis investigates the impact of variability in the key model parameters. Variables used in the sensitivity and uncertainty analysis are similar. A Monte Carlo simulation is employed in this study to account for the uncertainties in the analysis. A triangular distribution is assigned to all variables in this simulation. A triangular distribution was chosen due to limited sample data in the variables. Then, a large data set consisting of at least 10,000 data points are randomly generated and incorporated into the financial spreadsheet. The simulation then iterates to create unique combinations of all parameters. This investigates the probability of variations in multiple parameters at the same time. The output of a Monte Carlo simulation is sampling points that can be fitted into a probability distribution. Table 5 tabulates the key variables and its assumptions used for studying the sensitivity and uncertainty in operating parameters for the economic analysis and environmental analysis respectively. 3. Results and discussion 3.1. Process design Fig. 2 illustrates the results of the mass, energy and carbon balance of the system using corn stover as biomass feedstock in the BG scenario, while Fig. 3 closely examines just the mass flows of the systems for both the BO and BG scenario using corn stover as feedstock. In all scenarios, the same amount of biomass is used, which is approximately 10.15 wt% of the raw manure. In the BG scenario, some of the manure is replaced with about 10 wt% of glycerin to produce the same power capacity of 950 kWe. This
Fig. 2. Mass and energy results of the Biomass and Glycerin (BG) AD scenario with corn stover: (a) Mass flows (tons per day); (b) Energy flows (MJ per day); (c) Carbon flows (tons per day).
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Fig. 3. Mass flows (MT/d) of the Biomass Only (BO) and Biomass with Glycerin (BG) anaerobic digestion for corn stover scenario.
amount of glycerin was added to the AD process because it was proven to be most effective in increasing biogas production and methane content when added to cattle slurry (Robra et al., 2010). Higher glycerin input such as 15 wt% contributes to process instability and can cause system failure. This includes thermic shock of the AD bacteria and drops of more than 50% in biogas production. In all scenarios, the AD system can produce 950 kWe. In the BO scenario, without the addition of glycerin, an average increase of 57% of raw manure input is required to produce the same amount of electricity. Both BO and BG scenarios also generate approximately 8335 m3/d of biogas from 132 and 90 MT respectively, assuming a 42% gas turbine power efficiency. These values are comparable to those reported by the Italian Agroforestry Energy Associations (AIEL) case study on biogas (Mezzadri and Francescato, 2008). The case study reports a biogas yield of 189,000 m3 from a 4418 MT of digester input consisting of cattle slurry and maize. An increase of 47% is observed in digester input in the BO scenario to produce the same amount of biogas. Both scenarios also produce by-products in the form of solid digestate and liquid effluent of 122 and 80 MT respectively. The by-products are then separated into liquid and solid fractions to reduce transport cost (Sanscartier et al., 2012). Both solid digestate and liquid efflu-
Fig. 4. Operating costs of Biomass Only (BO) and Biomass with Glycerin (BG) scenarios by feedstock type.
ent contain carbon, nitrogen, phosphorus, potassium and various soil nutrients which can be employed on-site as fertilizers. This is because the organic nitrogen in the substrates is converted into ammonia during digestion, which is an organic form easily consumed by plants. Additionally, phosphorus and potassium levels are also maintained. Hence, the potential of manure as fertilizers is increased by the process of digestion (Heaton et al., 2004). This also further reduces farm operating costs and the use of artificial fertilizers. Additionally, using these by-products as fertilizers are also a beneficial option for the environment, as it allows nutrients to be recovered and reduces the loss of organic matter suffered by soils from agriculture exploitation (Gómez et al., 2007). This system was also modelled with a continuous demand of electricity and heat to operate equipment such as mixers, blowers, pump, and maintain the digesters at mesophilic temperatures. To ensure the assumptions were kept constant, parasitic energy loads were computed similarly in both cases. Hence, the system has an
Table 6 Internal rate of return (%) by feedstock type and Biomass Only (BO) and Biomass with Glycerin (BG) scenarios. Biomass
BO
BG
Corn Stover Rye Wheat
3.51 5.57 5.56
3.69 5.25 5.22
Fig. 5. GHG emissions by source for Biomass Only (BO) and with Glycerin (BG) scenarios.
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average parasitic electricity consumption that is equal to 14% of the electricity generated by the system and 15% of heat consumption is required to heat substrates to mesophilic temperatures before digestion. Using the study by Li et al. (2018), the parasitic load can be calculated using a factor of 0.0082 kWh/kg of input to the system. This yields a parasitic load of 137 kW. Both scenarios also generate sufficient heat to heat up recycled process water. This will eventually lower operating costs as heat does not need to be purchased from an outside source. To approximate the amount of heat required to maintain the digester at mesophilic temperatures, it is assumed that heat is added into the system via feedstock and recycled process water. Through this it is assumed that the heat
load required by the system is 152 kW. Approximately 43% of the steam is recycled in hot water form to create a slurry for the digester input, and to maintain the digester temperature. The amount of electricity generated by the system translates to 0.40 kWe/cattle. After including both the parasitic and heat consumption, the system generates a net energy of 12.53 GWh annually. 3.2. Techno-economic analysis The capital costs for a farm-scale anaerobic digestion plant is estimated at $3.12 million (MM), with the digester itself contributing to the majority of the cost. We assumed the same capital costs
Fig. 6. Carbon dioxide equivalent (CO2e) flows for Corn Stover: (a) BO and (b) BG case from SimaPro.
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for all scenarios. The digester itself is expensive due to its large capacity and technical considerations when constructing the digester. The capital cost translates to an expense of $0.44/kWhe, which is comparable to the costs reported by FarmWare 3.1, where capital cost is approximately $0.50/kWhe (Enahoro and Gloy, 2008). The breakdown of the operating costs for all feedstock and scenarios is shown in Fig. 4, which includes both variable and fixed costs. The variable costs include the cost of raw materials, waste handling, utilities and by-products credits, while the fixed costs include the costs of labor, depreciation, insurance and taxes. The by-product credits are primarily from reusing both the liquid effluent and solid digestate as fertilizers on the farm. This reduces the farm operating cost as there is no need to purchase artificial fertilizers. Besides by-product credits, the plant is also capable in producing enough electricity to power the whole system, hence, the purchase of electricity is also avoided but not included as part of the renewable electricity tax credit. The operating cost for the BO scenario is greater than the BG scenario. The major contributor of the operating cost is the cost of labor and maintenance which is 49–54% of the total operating costs. The renewable tax credit also contributes to an average of 40% of the total operating cost in all scenarios. Cost of manure is 11–43% of the total operating cost. The average cost of manure increases by 23% when glycerin is absent from the AD process However, this is mainly due to the addition of more manure into the digester in BO vs. BG scenario, as the amount of biomass input is the same in both scenarios. The liquid effluent credit ranges from $(0.002) to $(0.017)/kWhe and is 3–27% of the total operating costs. Using the capital and operating cost, the IRR is computed using the DCFROR analysis. The IRR for all feedstock in the different cases varies accordingly. As observed in Table 6, the average IRR in BG scenarios is lower than the BO scenarios. However, the BO scenarios have a lower NPV compared to the BG scenarios. 3.3. Life cycle analysis Fig. 5 shows the total GHG emission distributions for all feedstocks in their respective scenarios. The red dashed line in the figure represents the average GHG emissions for the US electricity
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grid, which is 540 g CO2e/kWh by the GREET model (Argonne National Laboratory, 2017). There is an average of 88% and 12% of GHG emissions reduction in BO and BG scenarios respectively. The GHG emissions for the BG scenarios are positive emissions and 10 times greater in compared to the emissions from the BO scenarios. This is mainly due to the high emission factor from glycerin which is mainly due to soybean production practices and indirect land use. All cases would reduce emissions when compared to the US electricity grid. Displaced emissions include the avoidance of artificial fertilizer in the form of digester materials and recycled heat to the process in the form of steam. In SimaPro, these aspects were computed as avoided emissions using the system boundary expansion method. Fig. 6(a) and (b) are tree diagrams acquired from SimaPro which details the emission distribution with a node cut-off point of 0.13% to put an emphasis on significant GHGcomponents in the analysis. 3.4. Sensitivity and uncertainty analysis The sensitivity analysis was conducted on various parameters on the economic and environmental performance for the system, as shown in Figs. 7 and 8 respectively. Both the operating capacity and power efficiency have the most impact on the IRR in all cases. An increase in operating capacity from 68% to 85% can cause an increase in the IRR by 2 times. Similarly, an increase in power efficiency from 42% to 50% can increase the IRR from 3.69 to 5.13. The biomass price is the least significant parameter as observed from the tornado plots. A decrease in biomass price from $20/MT to $10/MT, only increases the IRR from 3.69 to 4.04. All values are evaluated based on corn stover in the BG scenario. Since the general trend for all scenarios are similar, hence the effect on IRR for all feedstock in all scenarios are also expected to be along the same lines. Additionally, parameters that could significantly affect the environmental impacts of the system are power efficiency, glycerin and manure emission factors. For the BG scenario, the glycerin emission factor is the most significant parameter, while in the BO scenario, manure emission factor is most significant. By decreasing glycerin’s emission factor from 2.49 kg CO2e/kg input to 1.99 kg
Fig. 7. Internal rate of return sensitivity analysis.
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Fig. 8. Lifecycle greenhouse gas emissions sensitivity analysis.
CO2e/kg input, total emissions decrease by 20%. In addition to that, an increase of 20% in power efficiency also decreases total emissions by 17%. The results also show that biomass emission factor has the least substantial effect, whereby a decrease in 20% of the emission factor, only decreases the total emissions by 0.78%. In the BO scenario, a decrease in manure emission factor from 0.0741 kgCO2e/kg input to 0.059 kgCO2e/kg input, decreases emission factor by 28%, while increasing power efficiency by 20%, also caused a decrease in total emissions by 16%. The least significant parameter is the organic matter emission factor whereby if increased by 20%, will only decrease the total emissions by 0.86%. Similarly, all values for both scenarios are evaluated using corn stover. The trend for all cases is similar, hence it is expected that the results for the other feedstock will also generally be the same. The probability density functions for the economic and environmental performance for the different AD systems are shown in violin plots in Fig. 9. It can be observed that the average of NPV for feedstocks is greater in the BG than BO scenario for the given assumptions. For both rye and wheat in the BG scenario, there is a probability of 86–100% that the project achieves a positive NPV, while for corn with glycerin, there is a 37% probability that the NPV is positive. For the BO scenarios, there is a 90% or greater probability that the NPV is negative. Additionally, the results of the uncertainty analysis on the total GHG emissions show that the median of GHG emission from corn stover (BG) is approximately 300 g CO2e/kWh, which is lesser than 540 g CO2e/kWh. There is also a higher probability that the emissions will fall between 300 and 400 g CO2e/kWh. For rye and wheat in the BG scenario, the median of GHG emission is approximately 850 and 900 g CO2e/kWhe. On the contrary, for feedstocks in the BO scenarios, there is a 100% probability that the total emissions are negative and below the emissions of the average U.S. electricity grid emissions. 4. Conclusions This study evaluated both economic and environmental impacts of the anaerobic digestion system. Two scenarios were compared, and the economic analysis was evaluated based on a DCFROR analysis method. The capital cost of the system is computed to be $0.44/kWhe. Additionally, the analysis also showed that feedstocks
Fig. 9. Uncertainty analysis results for net present value and total greenhouse gas emissions of Biomass Only (BO) and Biomass with Glycerin (BG) anaerobic digestion scenarios.
in the BG scenario had a greater NPV despite a lower IRR than the feedstocks in the BO scenario. In this case, the NPV is a preferred method in accessing economic performance as it is a more meaningful indicator for a capital-budgeting decision. It can also be concluded that the addition of glycerin to the digestion process reduces the amount of manure input, while having the same power capacity. Glycerin of 10 wt% is used in the system because it is proven to significantly increase methane and biogas yields. Codigestion of glycerin with manure and biomass, reduces operating costs by 32% and increases the Return on Investment (ROI) by 27%. The LCA also concludes that generating power and heat via an AD system can reduce the total GHG emissions in compared to generating power and heat from a coal power plant. Sensitivity analysis deduced that operating capacity and power efficiency has the most impact on the economic performance of the system, increasing the IRR by an average of 44% and subsequently the economic feasibility of the plant. Besides that, key variables such as power efficiency and manure or glycerin emission factors can significantly reduce
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the environmental impact by an average of 18%. By increasing power efficiency and decreasing manure or glycerin emission factor, the environmental impacts of an AD system can be improved as well. Acknowledgments This project was supported by the Iowa Economic Development Authority (grant number 17ARRA001ISU). We would also like to thank Bryan Sievers for providing information regarding the operation of his anaerobic digestion system. References Abbasi, T., Tauseef, S., Abbasi, S., 2012. Anaerobic digestion for global warming control and energy generation. An overview. Renew. Sustain. Energy Rev. 16 (5), 3228–3242. Agostini, A., Battini, F., Padella, M., Giuntoli, J., Baxter, D., Marelli, L., Amaducci, S., 2016. Economics of GHG emissions mitigation via biogas production from Sorghum, maize and dairy farm manure digestion in the Po valley. Biomass Bioenergy 89, 58–66. https://doi.org/10.1016/J.BIOMBIOE.2016.02.022. Aguilar-Aguilar, F.A., Nelson, D.L., Pantoja, L.A., Santos, A.S., Aguilar Aguilar, F.A., Nelson, D.L., Santos, D., 2017. Study of anaerobic co-digestion of crude glycerol and swine manure for the production of Biogas Estudo da Codigestão Anaeróbia de Glicerol Bruto e Dejeto Suíno para Produção de Biogás Study of Anaerobic Codigestion of Crude Glycerol and Swine Manure. Rev. Virtual Quim 6. https://doi. org/10.21577/1984-6835.20170142. AgSTAR, 2012. Funding On-Farm Anaerobic Digestion. Retrieved October 4, 2018 from
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