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Field-to-Fuel Performance Testing of Lignocellulosic Feedstocks for Fast Pyrolysis and Upgrading: Technoeconomic Analysis and Greenhouse Gas Life Cycle Analysis Pimphan Aye Meyer, Lesley J. Snowden-Swan, Kenneth G. Rappe, Susanne B. Jones, Tyler L. Westover, and Kara G. Cafferty Energy Fuels, Just Accepted Manuscript • DOI: 10.1021/acs.energyfuels.6b01643 • Publication Date (Web): 30 Sep 2016 Downloaded from http://pubs.acs.org on October 2, 2016

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Field-to-Fuel Performance Testing of Lignocellulosic Feedstocks for Fast Pyrolysis and Upgrading: Techno-economic Analysis and Greenhouse Gas Life Cycle Analysis Pimphan A. Meyer*, †, Lesley J. Snowden-Swan†, Kenneth G. Rappé†, Susanne B. Jones†, Tyler L. Westover‡ and Kara G. Cafferty‡ †

Pacific Northwest National Laboratory, P.O. Box 999, Richland, Washington 99352 United States Idaho National Laboratory, 2525 Fremont Avenue, Idaho Falls, Idaho 83415 United States

‡

Abstract This work shows preliminary results from techno-economic analysis and life cycle greenhouse gas analysis of the conversion of seven (7) biomass feedstocks to produce liquid transportation fuels via fast pyrolysis and upgrading via hydrodeoxygenation. The biomass consists of five (5) pure feeds (pine, tulip poplar, hybrid poplar, switchgrass, corn stover) and two blends. Blend 1 consists of equal weights of pine, tulip poplar and switchgrass, and blend 2 is 67% pine and 33% hybrid poplar. Upgraded oil product yield is one of the most significant parameters affecting the process economics, and is a function of both fast pyrolysis oil yield and hydrotreating oil yield. Pure pine produced the highest overall yield, while switchgrass produced the lowest. Interestingly, herbaceous materials blended with woody biomass performed nearly as well as pure woody feedstock, suggesting a non-trivial relationship between feedstock attributes and production yield. Production costs are also highly dependent upon hydrotreating catalyst-related costs. The catalysts contribute an average of ~15% to the total fuel cost, which can be reduced through research and development focused on achieving performance at increased space velocity (e.g., reduced catalyst loading) and prolonging catalyst lifetime. Greenhouse gas reduction does not necessarily align with favorable economics. From the greenhouse gas analysis, processing tulip poplar achieves the largest greenhouse gas emission reduction relative to petroleum (~70%) because of its lower natural gas requirement for hydrogen production. Conversely, processing Blend 1 results in the smallest GHG emission reduction from petroleum (~58%) because of high natural gas demand for hydrogen production. 1. INTRODUCTION The Renewable Fuel Standard (RFS) program sets aggressive targets to produce 136 million m3 of renewable fuel per year and to reduce greenhouse gas (GHG) emissions from the transportation sector by 2022.1 In an effort to meet this target, the US department of Energy (US DOE) and biomass industries are currently working together to develop feasible biomass conversion technologies. Among the factors that ultimately determine the feasibility of biofuel production technologies, the most critical are feedstock availability, process economics, and environmental sustainability. Fast pyrolysis followed by bio-oil hydrodeoxygenation (HDO, a.k.a. upgrading) represents a compelling route for production of liquid transportation fuels.2 It is suggested to be the most economic favorable compared to other biomass conversion pathways such as gasification and biochemical3,4. Fast pyrolysis is a means of direct liquefaction of a wide array of biomass sources and is emerging as a potential near1 ACS Paragon Plus Environment

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term deployable technology for front-end processing of cellulosic feedstocks into bio-oil.5,6 Fast pyrolysis consists of rapidly heating the raw feedstock to moderate temperatures in the absence of oxygen, followed by rapid quenching of the vapor product. Fast pyrolysis produces an organic oil fraction that contains as much as 75% of the energy content of the raw feedstock, with the remaining distributed amongst solid char, ash, and light gases. However, bio-oil is thermally unstable, has a high oxygen content, and contains hundreds of chemical species with a large array of oxygen functionalities.6 Consequently, a widely-applicable de-oxygenation upgrading strategy is necessary. In order to accurately compare different fuel production technologies, the full supply chain must be assessed (i.e., from field to fuel), including all pertinent supply chain logistics as well as conversion performance and cost. Techno-economic analysis (TEA) and life-cycle analysis (LCA) are widely accepted tools that are used to evaluate the costs and environmental impacts of biofuel production. For the analysis works of fast pyrolysis and upgrading technology, most of the published literature either only report the TEA results or the LCA results. There are only a few articles that include both TEA and LCA results together. Jones et al.7 published the TEA and conversion-stage sustainability metrics results of the fast pyrolysis and upgrading design case as a part of the US Department of Energy’s Bioenergy Technologies Office Program. The analysis assumptions are based on a single blended lignocellulosic feedstock and the yield is predicted as it is a future target case. In addition, Patel et al.8 published a review article of TEA and LCA of lignocellulosic thermochemical conversion pathways. In the review article, the TEA results of six thermochemical pathways are compared while the LCA assumptions of four thermochemical pathways are summarized and discussed. It is difficult to compare TEA and LCA results from different works to each other because they are typically based on different feedstock, process configuration (or system boundary), process scale, product type and yield, functional unit, and financial assumptions. Due to a variety of analysis assumptions, a review of TEA of thermochemical cellulosic biofuel pathways published by Brown9 shows that minimum fuel selling prices fall into a wide range from $0.51 to $1.88 per liter gasoline equivalent ($1.93 to $7.11 per gallon gasoline equivalent). This article provides preliminary results from TEA and LCA of fast pyrolysis and upgrading of various biomass feedstocks. This work is different from previous fast pyrolysis TEAs7,10 and LCAs7 because it leverages the uniquely complete experimental data set recently reported by Howe et al.11 The experimental data are the integrated results from feedstock conversion (fast pyrolysis section) and hydrocarbon production (upgrading section). The economic analysis method and assumptions are based on a fast pyrolysis and upgrading target design case previously reported by Jones et al.7 Process parameters that can significantly improve process economics are also identified and discussed. 2. TECHNO-ECONOMIC ANALYSIS The process model and simulation are developed in CHEMCAD12 based on the design case by Jones et al.,7 modified with experimental results generated by Howe et al.11 The heat and material balances from the simulation models are used to estimate capital and operating costs. The capital costs, operating costs and fixed costs are then assembled in a Microsoft Excel© spreadsheet employing a discounted cash flow analysis to estimate the minimum fuel selling price (MFSP) on a gasoline gallon equivalent (gge) basis. All costs presented are on a 2011 constant US dollar basis. Indices used to convert capital and operating costs to the 2011 US dollars can be found in Jones et al.7 2 ACS Paragon Plus Environment

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The “nth” plant design and assumptions are applied in this study. These assumptions do not account for additional first of a kind plant costs, including special financing, equipment redundancies, large contingencies and longer startup times necessary for the first few plants. For nth plant designs, it is assumed that the costs reflect a future time when the technology is mature and several plants have already been built and are operating. 2.1 Feedstock and Plant Size This study considers seven lignocellulosic feedstocks: pine (PN), tulip poplar (TP), hybrid poplar (HP), switchgrass (SG), corn stover (CS), Blend 1 that consists of equal weight percentages of pine, tulip poplar and switchgrass (1-PN:1-TP:1-SG) and Blend 2 that consists of 67% pine and 33% hybrid poplar (2-PN:1HP). The elementary compositions, ash contents, and heating values of the feedstocks as reported by Howe et al.11 are used in the process models. As-received feedstocks are fast pyrolysis reactor-throatready, sized 2 mm and containing 10% moisture content and are assumed to be supplied from the biomass feedstock supply system.15 The modeled plant capacity is 2,000 metric ton per day (2,205 dry U.S. tons per day) of dry biomass. 2.1.1 Feedstock Cost Assumptions A biomass feedstock supply system is a complex organization of operations necessary to transform raw biomass into a usable energy form up to the point of actual conversion. These costs are highly variable and differ substantially between regions, and depend on weather, cropping systems, transport load limits and other regulations. For example, regional differences in load limits can change supply-logistics costs by more than $2 per metric ton.16 In this study, logistics and feedstock costs are combined into a total delivered feedstock cost, referencing values reported by BETO in the multi-year project plan (MYPP) reports.1,17 The 2014 BETO MYPP17 shows a blended feedstock approach in its 2015 projections. However, blending strategies to reduce feedstock related costs are not the focus of this paper. For this work, individual feedstock costs reported by BETO (in the MYPP reports) and developed by the Idaho National Laboratory’s Biomass Logistics Model are used.15,18 These projections were not meant to represent all feedstock logistics systems, but rather serve as an example of a supply system that could achieve the U.S. DOE desired cost target. Since the real cost of a supply chain will vary regionally, seasonally, and yearly, a sensitivity of approximately $30 per metric ton was chosen as an example to represent the uncertainty in these variables and highlight the impact of a dynamic and changing market. Table 1 lists delivered feedstock costs used for the base case study in this paper. 2.2 Financial Assumptions This analysis was supported by the US DOE’s Bioenergy Technologies Office, which uses standard economic assumptions such as the nth plant for every biomass technology across the program to be able to compare one technology to another. The specific assumptions are shown in Table 2. 2.3 Capital and Operating Costs 2.3.1 Capital Cost Estimation

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The equipment-based costs and design can be found in the design case report.7 Exponential factors are applied for costing the equipment at different size.13 Process equipment operating at different process conditions from the design case are re-costed using AspenPlus Economic Evaluation V7.3.2.14 The total direct cost is the sum of all the installed equipment costs, plus the costs for buildings, additional piping, and site development (calculated as 4%, 4.5% and 10% of purchased equipment, respectively). Indirect costs are estimated as 60% of the total installed costs. The sum of the direct and indirect costs is the fixed capital investment (FCI). Total capital investment (TCI) is the sum of the FCI, working capital (5% of FCI), and the cost of land. 2.3.2. Operating and Fixed Costs Table 3 lists the assumptions used to calculate the operating costs. Process economic analysis is conducted in 2011 US dollars. Fixed costs are comprised of salaries, benefits, general overhead, maintenance, insurance and taxes. Salaries and other fixed cost assumption shown in Table 4 are also taken from Jones et al.7 2.4 Process Model Description A simplified block diagram of the overall process is illustrated in Figure 1. There are four major processing areas: (1) Fast Pyrolysis, (2) Hydrotreating, (3) Hydrocracking and Product Separation, and (4) Hydrogen Plant, Steam and Power Generation. In the fast pyrolysis section, a bubbling fluidized bed reactor is assumed for the fast pyrolysis reactor. Biomass feedstock is introduced to hot sand fluidized by recirculated product gas. Fast pyrolysis occurs at approximately 500 °C in less than two seconds and under atmospheric pressure. The reactor effluent is then rapidly cooled to stop the reaction. The cooled pyrolysis products are primarily fast pyrolysis oil, commonly referred to as bio-oil, a mixture of organic compounds and water. Solid char is also produced and burned to reheat the sand. Hot sand and non-condensable gases are recycled back to the reactor. The hydrotreating process is designed to upgrade the fast pyrolysis oil to infrastructure-compatible fuels. The fast pyrolysis oil is deoxygenated by catalytic hydrotreating at elevated temperatures and pressures in an excess of hydrogen. Based on the design case report7, multiple fixed bed reactors are staged in series with increasing process severity in each reactor to allow processing at elevated temperatures. Bio-oil is first pretreated in a stabilization bed under relatively mild process conditions, 140 °C to 180 °C (284 °F to 356 °F) and 8.3 MPa (1200 psia), followed by processing under more severe hydrotreating conditions in the 1st and 2nd stage hydrotreating reactors. The 1st and 2nd stage hydrotreating reactors are designed as catalytic fixed bed reactors and operated under the conditions suggested by Howe et al.11 The pressure of 10.8 MPa (1565 psia) is used in both 1st and 2nd stage hydrotreating. The operating temperature is 180 °C to 250 °C (356 °F to 482 °F) in the 1st stage reactor and is 350 °C to 425 °C (662 °F to 797 °F) in the 2nd stage reactor. The upgraded oil is deoxygenated to less than 2 wt% oxygen. The heavy fraction of the pyrolysis oil that boils at a temperature above the final boiling point of diesel are assumed to be hydrocracked to additional fuel. The finished oils from hydrotreating have a wide range of boiling points and are fractionated into blendstocks with boiling 4 ACS Paragon Plus Environment

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points that are in gasoline- and diesel-boiling point ranges. Fast pyrolysis yields, hydrotreating process yields and hydrogen consumption from various biomass feedstocks are obtained from experimental data11 and shown in Table 5. Hydrogen for hydrotreating is assumed to be produced in a conventional hydrogen plant. Additional natural gas is also used to achieve sufficient hydrogen production. Process steam is produced via heat integration with the hydrogen plant, and additional available steam is used to generate power on-site. The plant utilities consist of cooling water, boiler feed water, and electricity. Cooling water usage is minimized through the use of air fin coolers where applicable. Most of the cooling tower water is used to indirectly cool the fast pyrolysis bio-oil that is recirculated in the quench system. Boiler feed water makeup compensates water consumed during steam reforming and boiler blowdown. Most of the electricity is purchase from the grid. Wastewater from the hydrotreaters typically contains less than 1 wt% carbon making recovery uneconomical. Thus, wastewater from hydrotreating is assumed to be treated by aerobic digestion before discharge to a public water treatment facility. 3. GREENHOUSE GAS ANALYSIS LCA is applied to evaluate GHG emissions associated with fuels from fast pyrolysis and upgrading of each feedstock examined in the study. The SimaPro 8 software20 is used to construct the LCA model. The system boundaries included in the analysis are shown in Figure 2. The scope of the LCA is greenhouse gas emissions, represented in grams of CO2-equivalents (CO2-e), using a 100-year global warming potential21, and the functional unit is 1 MJ of fuel combusted in an automobile. It is assumed that carbon uptake during growth of biomass is balanced by carbon emissions during the processing and combustion of fuel and therefore, biogenic carbon emissions are not tracked in the analysis. Material and energy data for the life cycle inventory comes from a variety of sources, including results of the process models developed for the TEA, data developed by INL, the GREET Model,22 and the EcoInvent database.23 Each stage of the fuel life cycle and associated assumptions for the analysis is described in the following sections. 3.1 Feedstock Establishment and Growing The key parameters for crop establishment and growing operations are listed in Table 6. This stage of the fuel life cycle includes tractor operations necessary for preparing the ground, planting and weeding, and application of chemicals. It does not include harvesting operations, which is included in the next life cycle stage. Energy for machinery use for establishing and growing of corn is 100% allocated to corn product, and therefore is not included in the corn stover inventory. However, removal of corn stover from the field requires application of additional fertilizer to the corn crop, as indicated in Table 6. Greenhouse gas emissions resulting from diesel use is adapted from the GREET model (92.2 g CO2-e/MJ diesel LHV).22 As explained in the Table 6 notes, tulip and hybrid poplars are assumed to require similar operations and chemical application rates for establishment and growth. All chemical application rates are taken from GREET22, with the exception of rates for pine, which are taken from Perlack and Stokes.24


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Emissions associated with fertilizer and herbicide production and application to the field were adapted from the GREET model. The default mix of nitrogen (N) fertilizer chemicals from GREET22 is assumed (ammonia 31%; urea 23%; ammonium nitrate 4%; urea-ammonium nitrate solution 32%; monoammonium phosphate 4%; diammonium phosphate 6%). GHG emissions stem from both fertilizer production and use. As part of nitrification and denitrification processes, GREET assumes that 1.525% of nitrogen contained in the applied fertilizer and in the biomass remaining after harvest is released as N2O. The release of nitrogen in poplar biomass is not included in the GREET model at this time for lack of available data. To investigate the impact of this assumption on the life cycle GHGs, the GREET model was run to include N2O emissions from poplar biomass (assuming 90% harvest/removal rate and 0.4% N content). The complete fuel life cycle GHGs were increased by 0.9% over the GREET default (with N2O emissions from poplar biomass omitted) and therefore, this assumption does not substantially impact the fuel carbon footprint. For corn stover, GREET assumes that release of nitrogen from fertilizer is balanced by the removal of corn stover (and contained nitrogen) from the field and thus there are no additional N2O emissions for corn stover beyond those associated with upstream production of fertilizer. GREET also assumes that all carbon contained in urea [CO(NH2)2] is released as CO2 from the field. 3.2 Feedstock Harvesting, Preprocessing, and Transport Figure 3 shows the processing steps that constitute the feedstock harvesting and logistics stage. This stage of the fuel life cycle begins with harvesting (or collection for corn stover) and ends with a reactorready feedstock at the biorefinery. After harvesting, the feedstocks are assumed to be naturally dried in the field to 30% for wood and corn stover and to 20% for switchgrass. They are then size-reduced (wood and corn stover), baled (corn stover and switchgrass) and transported to a centralized location (depot) where they are pelletized and dried to 10% moisture. The prepared feedstocks are then shipped to the conversion plant where the pellets are crumbled into the size particle required for fast pyrolysis. Table 7 gives the energy consumption and fuel type for the primary logistics steps for each pure feedstock. These data were derived from INL’s Biomass Logistics Model15,18 that calculates fuel consumption for its evaluation of biomass supply systems using industry standard equations and experimental data. In the absence of specific data for poplar feedstocks, it is assumed that poplar behaves similarly to pine, and thus the data for “pulpwood” is applied to both pine and poplar feedstocks. It is believed that these data provide conservative estimates for poplar, as it is generally less energy-intensive to grind as compared to pine. Pine is assumed to require all of the pulpwood steps except the delimbing step. The energy for harvesting corn is fully allocated to corn product and therefore is not counted in the energy required for corn stover production. Note that transportation of switchgrass requires less energy than that of the other feedstocks because of its lower moisture content. In addition, because pulpwood requires a larger draw radius than the other feedstocks, it requires more energy to transport to the biorefinery. Emissions factors for diesel, electricity, and natural gas consumption are adapted from the GREET model (92.2 g CO2-e/MJ diesel LHV; 161.1 g CO2e/MJ electricity; 69.4 g CO2-e/MJ natural gas (LHV)) 22 3.3 Biomass Conversion to Fuel


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This stage of the fuel life cycle begins at the throat of the pyrolysis reactor in the integrated biorefinery and ends with finished fuel at the plant gate. Listed in Table 8 are the key conversion material and energy flows from the biorefinery process models that are used in the GHG analysis. Also listed are the references used for calculating the GHG contribution of each process. Zeolite from EcoInvent23 is used as a proxy to estimate emissions associated with all catalysts consumed in the conversion process. 3.4 Fuel Distribution and Consumption Emissions associated with fuel distribution to the end user, including fuel transportation and operation of storage tanks and fueling stations, are modeled using an EcoInvent database23 process (“petrol, unleaded, at regional storage/ RER WITH US ELECTRICITY U”). Emissions of biogenic methane and N2O from combustion of gasoline and diesel fuels in a motor vehicle are adapted from GREET.22 The breakout of GHGs is not presented for fuel distribution and consumption, as they are a relatively minor contributor to overall fuel life cycle GHGs compared to other stages. 4. RESULTS AND DISCUSSION 4.1 Economic Analysis Results The purpose of the study by Howe et al.11 was to investigate the impact of feedstock characteristics on thermochemical processing of renewable cellulosic feedstocks to liquid hydrocarbon fuels. The Howe et al.11 experiments were performed at conditions that fit within the capabilities of the experimental equipment, with the focus on elucidating information regarding feedstock effects on conversion efficiency and yield. The experiments were not performed at conditions that yielded the most economically favorable results, nor were they intended to. For this reason, the results of this TEA will be compared on a relative basis using pine as base case for analysis. Pine was chosen for the base case because the pine process produced the highest hydrocarbon fuel product among the seven feedstocks (Table 5).11 The TEA results of other feedstocks are presented in percent variation from the pine base case, and possible ways to improve process economics are also discussed. Annual fuel blendstock production, total installed equipment cost (TIC) and fixed capital investment (FCI) are presented in percent variation from the pine base case as listed in Table 9. Lower liquid product yield is expected from herbaceous feedstocks which contain less lignin and more inorganic and protein contents versus woody feedstocks.11,31 This is true for pure feedstocks such as SG and CS. However, the study by Howe et al.11 showed comparable yield for Blend 1 (with a herbaceous component) and Blend 2 (woody components only), indicating there are possible synergies that occur with blended materials that may allow for increasing amounts of herbaceous fractions without sacrificing yield. . Processing pine results in the most expensive installed equipment cost (TIC in Table 9) because it has the highest blendstock yield that requires higher-capacity upgrading equipment. The lowest capital costs are associated with corn stover processing (-11% variation from the base case) even though the product yield is higher than that from switchgrass. This is because switchgrass consumes more hydrogen during upgrading which results in a more expensive hydrogen plant.


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Installed equipment costs distribution by process areas are presented in Table 10 in three feedstock groupings: woody biomass, herbaceous and blends. The pattern of installed cost distribution looks similar for every process scenario. The majority of the installed equipment costs are related to pyrolysis oil production and upgrading, with 38-43% of TIC in the fast pyrolysis process and 34%-36% of TIC in the hydrotreating process. In fast pyrolysis, over 90% of the installed equipment cost belongs to the fast pyrolysis reactor (installed). Similarly, the hydrotreating reactors (installed) are approximately 80% of the installed equipment cost in the hydrotreating process. These reactor costs could be decreased through technical research. For example, lower hydrogen partial pressure and lower total pressure and higher space velocity and longer catalyst life could result in smaller and less expensive reactors and peripheral equipment. Sensitivities to reactor costs, catalyst life, and space velocity are presented later to determine their effects on the overall process economics. The minimum fuel selling price (MFSP) is the selling price of the product that makes the net present value of the process equal to zero for a given discount rate (10% in this case). MFSPs and conversion costs (manufacturing cost excluding feedstock cost) from the six feedstocks are shown in Figure 4 in percent variation of the cost from the base case (Pine process). A selection of the MFSP cost contributors (in percentage of MFSP) are presented in Table 11. Process economics are generally strongly dependent on product yield. Among the seven feedstocks, processing pine results in the highest yield of hydrocarbon product, and thus it has the most favorable process economics. In contrast, processing switchgrass has the lowest product yield resulting in the least favorable process economics. As shown in Table 11, feedstock and hydrotreating catalysts are significant contributors to the MFSP. Fixed costs (e.g., labor) and average return on investment are also significant contributors to the MFSP, but are solely based on financial assumptions and not objectively improved by current technical research. However, due to their potential significance, the sensitivities of some financial assumptions are considered and discussed later. The feedstock cost contribution is high (over 35% of MFSP) for herbaceous feedstocks because of their relative expense (based on the assumptions noted in section 2.1.1) and low product yields compared to woody biomass and blends. The hydrotreating catalyst cost (catalysts for stabilizer, 1st and 2nd hydrotreaters) accounts for at least 13% of the product MFSP. Various active research and development activities supported by the US DOE are currently underway toward the goals of reducing the feedstock costs and hydrotreating catalyst costs as presented in biannual MYPP reports1,17 and the PNNL design case.7 4.2 FINANCIAL AND TECHNICAL SENSITIVITIES Sensitivity results are presented for pine only, with the general trend expected to follow similarly for other feedstocks. Sensitivity scenarios are shown in the form of a tornado chart in Figure 5 in which the costs are arranged by the relative magnitude of deviation value from the base case MFSP for pine. The most significant market and financial factors affecting MFSP include plant size, capital investment, and internal rate of return (IRR); the most significant technical performance factors are cost of hydrotreating catalysts and product yield. Sensitivity values are intentionally reported as relative percentage


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deviations rather than absolute values due to the magnitude of error bars surrounding the MFSP and the importance of emphasizing relative effect. Sensitivities to various assumptions in Figure 5 show that plant size has potentially the most significant effect on MFSP. Only the detrimental effect of scale less than the design case of 2000 metric tons per day (mtpd) is shown. Similar benefit can be achieved by increased scale of the pyrolysis system. Whereas current assumptions have the pyrolysis system designed at 1000 mtpd with two (2) installed units supporting a single 2000 mtpd plant, a single 2000 mtpd pyrolysis system would facilitate ~3% reduced MFSP. The internal rate of return (IRR) has significant impact on MFSP. Reducing the IRR from 10% to 5% effects a 13% reduction in the MFSP, and vice versa at similar magnitude. Project contingency is shown to have modest impact (3%) at variations from the assumed 10%. The total capital investment is the sum of the fixed capital investment, working capital, and land. Thus, with regards to plant cost, the critical component is the fixed capital investment (FCI), which is shown to have large impact on MFSP. Decreasing the FCI by 10% achieves a 5% reduced MFSP, whereas increasing the FCI by 40% increases MFSP by 20%. The FCI is primarily made up of the cost of the three (3) main processing areas: pyrolysis, hydrotreating, and H2 plant. Thus, there is value at looking into more detail surrounding their individual sensitivities. Varying the installed cost of pyrolysis ±40% results in a 7% variation in the MFSP. Similarly but less significant, varying the installed cost of the hydrogen plant ±25% results in 2% variation in the MFSP. For hydrotreating reactors, the primary capital cost contributors are the stabilizer reactor, 1st and 2nd stage hydrotreaters (i.e., HDO reactors), and various product gas and hydrogen compressors. Cost of the various compressors has minimal effect on economics, with ±40% required cost (from baseline) incurring ~0.3% variation in the MFSP. The stabilizer reactor has more impact, with ±40% required cost (from baseline) incurring 1% variation in the MFSP. Interestingly, eliminating the need for a stabilizer reactor altogether would have even more impact, decreasing the MFSP by 5%. The 1st and 2nd stage hydrotreater costs can have modest impact on MFSP, with ±40% of their cost (from baseline) incurring a 5% variation in the MFSP. Cost of these reactors is driven by required volume (i.e., LHSV, discussed further below) and wall thickness (to assist high pressure process condition), with wall thickness dictated by operating temperature and pressure. Achieving the necessary conversion performance at lower pressure is the subject of extensive on-going research and typically limited by catalyst reactivity. As hydrotreating pressure increases, reactors get thicker and more expensive: increasing hydrotreating pressure to 13.8 MPa (from the base case of 10.8 MPa) results in 4% increase in the MFSP. Regarding the hydrocracker, there is uncertainty surrounding its necessary scale, and the value it will provide. ~4% reduction in MFSP is achieved by eliminating the hydrocracker altogether, whereas a similar magnitude increase in MFSP is incurred by doubling its size.


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Hydrotreating catalyst costs are shown as one of the most significant technical and operating factors contributing to MFSP. Total incurred catalyst cost is a function of price per mass of the catalyst, the total required volume of each catalyst, and the frequency of replacement.19 Baseline costs for the catalysts are estimated at $142.5/kg ($64.65/lb) for the ruthenium-based catalyst (in the stabilizer and 1st stage hydrotreater) and $41.9/kg ($19.01/lb) for the sulfided molybdenum based catalyst (in the 2nd stage hydrotreater), presented in 2011 dollars. However, these catalyst costs are, in part, a function of catalyst component costs that can change significantly over short periods of time. For instance, Ru cost has varied from $6.3/g ($180/oz) at start of 2011 to $1.5/g ($42/oz) at the start of 2016.32 For the process modeled here, varying the ruthenium-based catalyst price from $88.2/kg ($40/lb) to $220.5/kg ($100/lb) results in a 4% decrease and 6% increase in the MFSP, respectively. Similarly but less impactful, varying the sulfided molybdenum based catalyst price from $10/lb to $30/lb results in a 2% variance in the MFSP. There is large impact on the MFSP if the hydrotreating catalysts deactivate more quickly than predicted, as it increases operating costs by necessitating increased frequency of catalyst replacement. The process modeled here assumes a 1-year catalyst life for both catalysts. Reducing the catalyst life by half causes the MFSP to increase by 15%. Conversely, if catalyst life is extended to 2 years, a 7% reduction in MFSP is expected. Liquid hourly space velocity (LHSV) is examined because it has the potential to impact both catalyst and reactor costs. A larger space velocity is more economical as it allows a smaller reactor and less catalyst. As modeled here, the LHSV for the hydrotreating is 0.5 hr-1 in the stabilizer and 0.15 hr-1 in both HDO reactors. There is the potential to significantly reduce the size of the stabilizer and/or 1st stage hydrotreater. Doubling the stabilizer LHSV decreases the MFSP by 2%, and increasing the LHSV to 0.5 hr1 for the 1st stage hydrotreater decreases the MFSP by 4%. The 2nd stage hydrotreater is not expected to be significantly smaller, but increasing its LHSV to 0.22 hr-1 decreases the MFSP by a modest 1%. Regarding conversion yield, the primary factor affecting MFSP is volume of product yield. Varying the annual gge blendstock volume ±5% results in 5% variation in the MFSP. The blendstock density affects yield indirectly, since lower density results in higher volumetric yields. Varying the density by ±0.02 from the base case results in a 3% variation in the MFSP. Feedstock cost is also a significant contributor to MFSP, as shown in Figure 5. Decreasing the feedstock cost to $77.2/metric ton ($70/dry U.S. ton) achieves 9% reduction in MFSP compared to the current estimate of $99.49, with similar magnitude of effect by increasing feedstock cost to $143.3/metric ton ($130/dry U.S. ton). Current efforts at INL and elsewhere are focused on reducing feedstock costs, however this must be a concerted effort with conversion efficiency. Feedstock availability and cost is driving the research to accurately predict the effects of feedstock blending on conversion performance and efficiency. Feedstock availability will vary from region to region, and economics may drive towards regionally-specific feedstock blends. Finally, H2 consumption is a key cost and sustainability driver. Blend 2 was selected to analyze the impact of H2 consumption, which was measured and modeled at 5.49%. The effect of varying H2 10 ACS Paragon Plus Environment

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Energy & Fuels

consumption for Blend 2 from 5% to 7% was examined and found to be somewhat insignificant. The effect on MFSP by decreasing H2 consumption to 5% was negligible, and increasing H2 consumption to 7% incurred only a 2% increase in MFSP. 4.3 Greenhouse Gas Analysis Results Results for the GHG life cycle analysis are presented for the feedstock establishment and growth stage, the feedstock harvesting and preprocessing stage, the conversion stage, and the complete fuel life cycle. Figure 6 gives the estimated GHG emissions associated with establishment and growth operations for each of the feedstocks examined. Switchgrass has significantly higher nitrogen fertilizer-related emissions than other feedstocks22 because it has comparatively higher fertilizer requirements, leading to higher N2O emissions. To explore the impact of N fertilizer rate on switchgrass production GHGs, low and high fertilizer rates were analyzed, as shown in Figure 7. The range of 6,945 g N/dry tonne to 8,818 g N/dry tonne (6,300 to 8,000 g N/dry ton) is cited in Wang et al.25 for the basis of the GREET assumption (8,057 g N/tonne or 7,300 g N/dry ton) and results in production GHGs that are 13% less and 8% greater than the base case, respectively. This translates to a significant impact on total fuel cycle GHGs of 6% lower and 4% higher than the base case for the low and high fertilizer rates, respectively. Although the analysis does not allocate any of the corn growing operations to stover (i.e., it is considered a residue from corn harvest), replenishment of nutrients to the soil is required due to corn stover removal. The result is similar emissions from nitrogen fertilizer as pine and poplar, and higher emissions from potash (K) fertilizer compared to all the other feedstocks. Figure 8 gives the estimated GHGs for the feedstock harvesting, preprocessing and transportation stage of the fuel life cycle. Woody feedstocks require more energy (and thus produce more GHGs) to harvest, transport, and grind in comparison herbaceous feedstocks (switchgrass and corn stover). Pulpwood (pine and poplar) requires delimbing. Corn stover requires shredding/chopping that is somewhat more energy intensive than cutting and windrowing of switchgrass, resulting in increased GHG emissions. Figure 9 shows the conversion stage GHGs for each of the seven feedstocks tested. Results for the various feedstocks range from 8 to 19.4 grams CO2-e/MJ fuel produced. The most significant contributors to conversion GHGs are natural gas and electricity consumption at the plant. The differences in natural gas consumption stem in part from the fact that different feedstocks yield different ratios of oil/gas/char in fast pyrolysis. The pyrolysis gas is used as feed to the steam reformer to offset natural gas usage for hydrogen production. Thus, in general, lower pyrolysis oil yields result in greater offgas production. This in turn, translates to lower natural gas demand. However, natural gas consumption also depends on other variables such as hydrogen consumption during upgrading and the quality of the offgas sent to the hydrogen plant (e.g., higher CO gas is preferable for hydrogen production). For example, the tulip poplar case has the highest pyrolysis oil yield and the lowest amount of pyrolysis offgas. However, its low hydrogen consumption during upgrading offsets the need for additional natural gas and results in comparatively small natural gas demand. Because hydrogen consumption can vary even for one feedstock, a sensitivity analysis of this parameter was performed for Blend 2 specifically. As shown in Figure 10, the variability in hydrogen consumption assumed has a


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significant impact on conversion GHGs, resulting in estimates 7% lower and 21% higher than the base case, respectively. Figure 11 shows the GHG results for the complete fuel life cycle, from establishment and growth of the biomass through combustion of gasoline in an automobile. Results for the various feedstocks range from 27.8 to 38.7 grams CO2-e/MJ fuel produced. In large part, the differences among various feedstocks reflect the differences seen for the conversion stage. The exception is the switchgrass case, where both nitrogen emissions associated with feedstock production and low overall fuel yields cause life cycle GHGs to be similar to the pine case. The estimated GHG reduction compared to petroleum fuel is 60% or greater in all cases except Blend 1. 5. Conclusions This study shows results from techno-economic analysis (TEA) and life-cycle greenhouse gas analysis (LCA) of seven biomass feedstocks to produce liquid transportation fuels via fast pyrolysis and upgrading, and leverages the uniquely complete experimental data set reported by Howe et al.11 The process economics are strongly dependent on product yield, feedstock cost and hydrotreating catalyst life and cost. Low product yields from herbaceous feedstocks (switchgrass and corn stover) result in poor process economics, whereas the pine process has the most favorable process economics because of its high yield. Ash and volatile carbon content are known to have adverse effects on pyrolysis oil yield, and likely affect (along with other factors such as protein content) pyrolysis oil quality and hydrotreating efficacy and yield. Additionally, using blends of multiple feedstocks (including herbaceous materials) may further improve process economics compared to pure feedstocks because the greater diversity in feedstock choices, the lower risk and transportation costs. Process parameters that most significantly affect the life-cycle GHG emissions (from feedstock production to vehicle operation) are: 1. Natural gas (used for hydrogen production) and electricity usages in the conversion-stage (fast pyrolysis and upgrading), 2. Energy usage in the feedstock supply system, and 3. Nitrogen fertilizer usage for growing biomass. Among the seven feedstocks, fuel from the tulip poplar process achieves the highest GHG emission reduction from petroleum (~70%) due to its comparatively low natural gas requirements for hydrogen production that stems from low hydrogen consumption during upgrading (Table 5). Conversely, fuel from the Blend 1process has the lowest GHG emission reduction from petroleum (~58%) because of high natural gas requirements in hydrogen production. It has been a concern that the target of renewable fuel production per year (136 million m3/year by 2022) set by the RFS program is not going to be achieved by using a single type of biomass. These preliminary results show that using blended materials does not necessarily result in inferior process performance compared to pure feedstocks. This work serves as an initial evaluation of the impact of feedstock type on economics and GHGs and can be used as a baseline for current and future work.


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AUTHOR INFORMATION Corresponding Author Pacific Northwest National Laboratory, 902 Battelle Blvd., Richland, WA 99352. Phone: 1-509-375-2485. E-mail: [email protected] Notes The authors declare no competing financial interest. ACKNOWLEDGMENTS The manuscript preparation work at PNNL was supported by the U.S. Department of Energy under Contract No. DE-AC05-76RL01830 at the Pacific Northwest National Laboratory. The PNNL authors gratefully acknowledge the support of the Department of Energy Bioenergy Technologies Office. We also thank Daniel Howe (at Pacific Northwest National Laboratory) and Daniel Carpenter (at National Renewable Energy Laboratory) for providing additional information describing the fast pyrolysis and hydrotreating experiment. REFERENCES 1. U.S. Department of Energy, Energy & Renewable Energy. Bioenergy Technology Office. Multi-Year Program Plan, March 2015. 2. Zacher, A. H.; Olarte, M. V.; Santosa, D. M.; Elliott, D. C.; Jones, S. B. A review and perspective of recent bio-oil hydrotreating research. Green Chem. 2014, 16, 491-515. 3. Anex, R. P.; Aden, A.; Kazi, F. K.; Fortman, J., Swanson, R. M.; Wright, M. M.; Satrio, J. A.; Brown, R. C.; Daugaard, D. E.; Platon, A.; Kothandaraman, G.; Hsu, D. D.; Dutta, A. Techno-economic comparison of biomass-to-transportation fuels via pyrolysis, gasification, and biochemical pathways. Fuel. 2010, 89, S29-S35. 4. Do, T. X.; Lim, Y. Techno-economic comparison of three energy conversion pathways from empty fruit bunches. Renew Energ. 2016, 90, 307–318. 5. Elliott, D. C.; Wang, H.; French, R.; Deutch, S.; Lisa, K. Hydrocarbon liquid production from biomass via hot-vapor-filtered fast pyrolysis and catalytic hydroprocessing of the bio-oil. Energy Fuels. 2014, 28, 5909-5917. 6. Bridgwater, A. V.; Peacocke, G. V. C. Fast pyrolysis processes for biomass. Renew. Sust. Energy Rev. 2000, 4, 1-73. 7. Jones, S. B.; Meyer, P. A.; Snowden-Swan, L. J.; Padmaperuma, A.; Tan, E. C. D.; Dutta, A.; Jacobson, J. J.; Cafferty, K. G. Economics for the conversion of lignocellulosic biomass to hydrocarbon fuels. Fast pyrolysis and hydrotreating bio-oil pathway. Pacific Northwest National Laboratory. PNNL-23053. November 2013. 8. Patel, M.; Zhang, X.; Kumar, A. Techno-economic and life cycle assessment on lignocellulosic biomass thermochemical conversion technologies: A review. Renew Sust Energ Rev. 2016, 53, 14861499.


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9. Brown, R. T.; A techno-economic review of thermochemical cellulosic biofuel Pathways. Bioresource Technol. 2015, 178, 166–176. 10. Wright, M. M.; Satrio, J. A.; Brown, R. C.; Daugaard, D. E.; Hsu, D. D. Techno-economic analysis of biomass fast pyrolysis to transportation fuels. National Renewable Energy Laboratory. NREL/TP6A20-46586. November 2010. 11. Howe, D.; Westover, T.; Carpenter, D.; Santosa, D.; Emerson, R.; Deutch, S.; Starace, A.; Kutnyakov, I.; Lukins, C. Field-to-fuel performance testing of lignocellulosic feedstocks: An integrated study of the fast pyrolysis−hydrotreaXng pathway. Energ Fuel. 2015, 29, 3188-3197. 12. Chemstations, 2015. CHEMCAD 6.5.5.7261. 13. Peters, M.S.; K.D. Timmerhaus; R. E. West. Plant Design and Economics for Chemical Engineers, 5th Edition; McGraw Hill: New York, 2003; pp. 242-243. 14. Aspentech, 2013. Capital Cost Estimator V7.3.2. 15. Jacobson, J.J.; Roni, M. S.; Lamers, P., Cafferty, K. G. Biomass feedstock supply system design and analysis. Idaho National Laboratory. INL/EXT 14-32377. September 2014. 16. Fales, S. L.; Hess, J. R.; Wilhelm, W.W. Convergence of agriculture and energy II: Producing cellulosic biomass for biofuels. CAST Commentary. 2007, The Council for Agricultural Science and Technology. 17. U.S. Department of Energy, Energy & Renewable Energy. Bioenergy Technology Office. Multi-Year Program Plan, November 2014. 18. Cafferty, K. G.; Muth, D. J.; Jacobson, J. J.; Bryden, K. M. Model based biomass system design of feedstock supply systems for bioenergy production. ASME Proceedings. ASME 2013 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. 2013, 2B, 1-9. 19. Jones, S. B.; Snowden-Swan, L. J.; Meyer, P. A.; Zacher, A. H.; Olarte, M. V., Drennan, C. Fast Pyrolysis and Hydrotreating: 2014 State of Technology R&D and Projections to 2017. Pacific Northwest National Laboratory. PNNL-24176. March 2015. 20. SimaPro, 2015. SimaPro Version 8. 21. Solomon, S.; Qin, D.; Manning, M.; Chen, Z.; Marquis, M.; Averyt K. B.; Miller, H.L. Contribution of Working Groups I to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change: Physical Science Basis. New York, USA; Cambridge University Press; 2007. 22. The greenhouse gases, regulated emission, and energy use in transportation model. 2014. GREET™ 2014. 23. Ecoinvent, v.2.2. Duebendorf, Switzerland: Swiss Center for Life Cycle Inventories, 2010. 24. Perlack, R. D.; Stokes, B. J. U.S. Billion-Ton Update: Biomass Supply for a Bioenergy and Bioproducts Industry. Oak Ridge National Laboratory. ORNL/TM-2011/224. August 2011. 25. Wang, Z; Dunn, J.; Han, J.; Wang, M. Q. Material and Energy Flows in the Production of Cellulosic Feedstocks for Biofuels for the GREET Model. Argonne National Laboratory. ANL/ESD-13/9. October 2013. 26. Adler, P. R.; Del Grosso, S. J.; Parton, W. J. Life cycle assessment of net greenhouse-gas flux for bioenergy cropping systems. Ecol Appl. 2007, 17, 675-691. 27. Bacenetti, J.; Gonzalez-Garcia, S.; Mena, A.; Fiala, M. Life cycle assessment: an application to poplar for energy cultivated in Italy. J Agr Eng. 2012, 43, 72-78.


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28. Huisman, W.; P. Venturi, P.; Molenaar, J. Costs of supply chains of miscanthus giganteus. Ind Crop Prod. 1997, 6, 353-366. 29. Smeets, E. M. W.; Lewandowski, I. M.; Faaij, A. P. C. The economical and environmental performance of miscanthus and switchgrass production and supply chains in a European setting. Renew Sust Energ Rev. 2009, 13, 1230-1245. 30. Hsu, D. D. Life Cycle Assessment of Gasoline and Diesel Produced via Fast Pyrolysis and Hydroprocessing. National Renewable Energy Laboratory. NREL/TP-6A20-49341. March 2011. 31. Oasmaa, A.; Solantausta, Y.; Arpiainen, V.; Kuoppala, E.; Sipila, K. Fast pyrolysis bio-oils from wood and agricultural residues. Energ Fuel. 2010, 24, 1380–1388. 32. United States Geological Survey, Minerals Information, Platinum-Group Metals Statistics and Information. Annual Publications: Minerals Yearbook. http://minerals.usgs.gov/minerals/pubs/commodity/platinum/. Accessed March 11, 2016. 33. EPA. Fuel-Specific Lifecycle Greenhouse Gas Emissions Results. Docket # EPAHQ- OAR-2005-01613173. Washington, DC: U.S. Environmental Protection Agency, 2010.


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Table 1 – Costs of the reactor-throat-ready feedstocks used for the base case study17 Feedstock

Cost, per metric ton (per dry U.S. ton)

Woody feedstocks (PN, HP and TP)

$109.67 ($99.49)

Corn Stover (CS)

$86.31 ($78.30)

Switchgrass (SG)

$87.74 ($79.60)

Blend 1 (1-PN:1-TP:1-SG)*

$102.36 ($92.86)

Blend 2 (2-PN:1-HP)*

$109.67 ($99.49)

* Calculated based on individual component contributions

Table 2 – Financial assumptions Assumption Description Internal rate of return Plant financing debt/equity Plant life Income tax rate Interest rate for debt financing Term for debt financing Working capital cost Depreciation schedule Construction period Plant salvage value Startup time Revenue and costs during startup

On-stream factor a

Assumed Value 10% 60% / 40% of total capital investment 30 years 35% 8.0% annually 10 years 5.0% of fixed capital investment (excluding land) a 7-years MACRS schedule 3 years (8% 1st yr, 60% 2nd yr, 32% 3rd yr) No value 6 months Revenue = 50% of normal Variable costs = 75% of normal Fixed costs = 100% of normal 90% (7884 operating hours per year)

Modified Accelerated Cost Recovery System


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Energy & Fuels

Table 3 – Summary of assumptions for operating costs in 2011 US dollars Variable

Value

Source

Material Costs st

Stabilizer & 1 Bed Catalyst*

$142.5/kg ($64.65/lb)

15

2 Bed Hydrotreating Catalyst (2007)

$41.9/kg ($19.01/lb)

5

Hydrocracking Catalyst (2007)

$41.9/kg ($19.01/lb)

5

16 cents/m -H ($4.41/1000 scf-H2)

2

5

$2.2/kg ($0.98/lb)

5

3

nd

3

Hydrogen Plant Catalysts (2007) Sulfiding Agent (2007) Utilities Natural gas cost

18 cents/m ($5.10/1000 scf)

5

Composition

94% CH4, 3.3% C2H6, 1.0% N2, 1.0% C3H8, 0.35% C4H10, 0.3% CO2, 0.04% C5H12, 0.01% C6H14

5

48.5 MJ/kg (20,854 BTU/lb)

5

6.89 cents/kwh

5

32.2 °C (90 °F) service 43.3 °C (110 °F) return

5

Lower heating value (LHV) Electricity cost Cooling tower water makeup (20 °F rise) Chemical Costs

3

5

3

53 cents/m ($2.02/1000 gal)

5

Cooling tower chemicals

$7.9/kg ($3.59/lb)

5

Boiler feed water chemicals

$4.7/kg ($2.15/lb)

5

$38.6/metric ton ($35.00/U.S. ton)

5

Boiler feed water makeup

53 cents/m ($2.02/1000 gal)

Cooling tower makeup

Ash and solid disposal

3

Wastewater disposal

3

90 cents/m ($2.54/100 ft )

5

*The price is calculated based on current market ruthenium metal and support price.

Table 4 – Fixed Operating Cost Assumptions Salaries Annual salaries Other Fixed Costs Benefits and general overhead Maintenance Insurance and taxes

$4,055,000 Factor 90% of total salaries 3% of fixed capital investment 0.7% of fixed capital investment


Energy & Fuels

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Table 5 – Fast pyrolysis oil yield, hydrotreating yield and hydrogen consumption11 Pine

Feedstock Designation Fast pyrolysis oil yield

a b

Hydrotreating oil yield c Hydrogen consumption

Hybrid Poplar HP

Corn Stover CS

Switch grass SG

Blend 1

Blend 2

PN

Tulip Poplar TP

1-PN:1-TP:1-SG

2-PN:1-HP

53.1%

60.3%

54.0%

35.4%

40.8%

49.6%

46.7%

27.4%

24.2%

24.9%

20.1%

17.0%

24.5%

24.3%

7.0%

5.1%

6.0%

5.3%

6.5%

5.4%

5.5%

a. Weight % dry bio-oil / dry biomass b. Weight % dry hydrotreated oil / dry biomass – for the hydrodeoxygenation steps only c. Weight % hydrogen / dry bio-oil

Table 6 – Feedstock parameters for establishment and growth stage of life cycle a

Poplar Pine Yield , kg/m /yr 1.01 1.18 b c Rotation , yr 25 8 d Chemical Application Rates, g/dry tonne Nitrogen (N) fertilizer 3024 3214 Phosphorus (P2O5) fertilizer 1008 2183 Potash (K2O) fertilizer 2015 573 Limestone (CaCO3) 2380 2380 Herbicide 150 55 3 2 e,f Machinery Use, mm diesel/m (frequency per rotation) Plow 2002 (1) 2002 (1) Disk or harrow 1066 (2) 1066 (1) Cultivator 514 (6) 514 (5) Planter 2423 (1) 2423 (1) Roller Fertilizer 159 (13) 159 (7) Herbicide 262 (9) 262 (3) Stump remover at end 7174 (1) (1) of rotation Residue remover b

2

Switchgrass 1.1 10

Corn Stover 0.22 -

8047 110 220 31

7716 2205 13228 -

2526 (1) 664 (2) 393 (1) 2198 (1) 393 (1) 664 (10) 795 (1) -

-

1759(2)

-

a. In the absence of tulip poplar data, it is assumed the parameters are equal to those of hybrid poplar. 22 25 24 b. Yield and rotation are from GREET 2014 and Wang et al. except for pine, which is from Perlack and Stokes. c. With three (3) harvest years. 22 25 24 d. Application rates from GREET 2014 and Wang et al. except for PN which are from Perlack and Stokes. 26 27 e. Machinery fuel use for poplar and pine are from Adler et al. except for stump removal which is from Bacenetti et al. 25 24 Machinery use frequency taken from Wang et al. for poplar and Perlack and Stokes for pine. 25 28 29 f. Machinery use and frequency for switchgrass taken from Wang 2013 , Huisman et al. and Smeets et al.


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Energy & Fuels

Table 7 – Energy consumption for feedstock harvesting, preprocessing and transport steps Pulpwood

Corn Stover

Switchgrass Fuel Type

Machine Feller Buncher Grapple Skidder Flail Shredder/ Delimber Chipper

MJ/tonne

130117

Machine Flail Shredder/ Stalk Chopper

Densifier Dryer Surge Bin Conveyors Handling Loader Pellet Crumbler Total

104717

MJ/tonne

Windrower

63291

Diesel

86842

Baler

36751

Diesel

141038

Bale Stacker

50184

Bale Stacker

50184

Diesel

128221

Fieldside Storage

318349

Semi Truck and Trailer Transport Unloader

Storage Loader Grinder

Machine

Baler

82457

Transport Loader Semi Truck and Trailer

MJ/tonne

0

Fieldside Storage

0

Diesel

14421 107543

Transport Loader

14421 57836

Diesel

14421 12700

Diesel

Semi Truck and Trailer

14421 12700

Transport Unloader

10781

Storage Loader

248313

Grinder and Trommel screen

91273

Grinder and Trommel Screen

91273 Electricity

Grinder (for stage II size reduction)

47613

47613 Electricity

Densifier

99204 95238 361 33599

Grinder 2 (for stage II size reduction in Fig. 3) Densifier

99204 95238 361 942

Dryer Surge Bin

10141

Conveyors and Misc Equipment Handling loader

39449

Pellet Crumbler

1304612

Total

Storage Loader

Diesel

Dryer Surge Bin

10141

Conveyors and Misc Equipment Handling Loader

39449

Pellet Crumbler

807706

Total

99204 76188 361 33599 10141

Diesel

Electricity Natural Gas Electricity Electricity Diesel

39449 Electricity 647433


Energy & Fuels

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Table 8 – Key material and energy flows for conversion Pine

Hybrid Poplar

Tulip Poplar

Switch grass

Corn Stover

Gasoline Yield, kg/hr (MJ/hr)

10745 (468130)

10647 (468025)

9456 (411684)

7521 (329284)

9446 (412001)

Diesel Yield, kg/hr (MJ/hr)

12315 (516452)

10308 (435634)

10895 (459690)

6697 (282229)

Total Fuel Yield, kg/hr (MJ/hr)

23061 (984582)

20955 (903659)

20351 (871480)

Natural Gas, kg/hr

4089

1656

Electricity, MJ/hr Catalyst, g/MJ fuel

31622 0.06

Blend 2

Reference

10458 (458318)

10581 (462644)

This work

7394 (312720)

10219 (434052)

9872 (418437)

This work

14218 (611513)

16853 (724721)

20677 (892370)

20453 (881081)

This work

699

122

1633

4075

2751

37469 0.06

26572 0.07

24854 0.08

23648 0.07

22068 0.06

25978 0.06

23

23

23

23

23

23

23

6.3e-6; 3.0e-6

6.3e-6; 3.0e-6

6.3e-6; 3.2e-6

6.3e-6; 2.7e-6

6.3e-6; 2.6e-6

6.3e-6; 3.0e-6

6.3e-6; 2.8e-6

47887

41142

42519

45591

42095

45097

42060

558

758

383

3500

3558

1458

517

Blend 1

Products

Inputs

Catalyst sulfiding agent, kg/hr Infrastructure, piece/hr (pyrolysis; upgrading)

GREET: 69 g CO2-e/MJ, LHV22 GREET: 161 g CO2-e/MJ22 Zeolite, powder, at plant/RER WITH US ELECTRICITY U23 Dimethyl sulfoxide, at plant/RER WITH US ELECTRICITY U23 Thermochem Conversion Plant30; Refinery/RER/I WITH US ELECTRICITY U23

Wastes Wastewater to public treatment facility, m3/hr Ash disposal, kg/hr

Treatment, sewage, unpolluted, to wastewater treatment, class 3/CH WITH US ELECTRICITY U23 Disposal, wood ash mixture, pure, 0% water, to sanitary landfill/CH WITH US ELECTRICITY U23

Table 9 – Annual blendstock production from 2000 metric ton per day of biomass feedstock, total installed equipment cost (TIC) and fixed capital investment (FCI) in % variation of the base case (pine process) Base case Feedstock MMgge per year of blendstock Total Installed Cost (TIC) Fixed Capital Investment (FCI)

% Variation from the base case

Pine

Hybrid Poplar

Tulip Poplar

Corn Stover

Switch grass

Blend 1

Blend 2

63

-8%

-12%

-26%

-38%

-9%

-11%

$MM 446

-1%

-3%

-11%

-9%

-5%

-7%

$MM 766

-1%

-3%

-11%

-9%

-5%

-7%


Page 21 of 28

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

Energy & Fuels

Table 10 – Installed equipment cost contribution by area (% of total installed equipment cost) Woody biomass 38%-40%

Herbaceous 42%-43%

Blends 41%

Pyrolysis Oil Upgrading to Stable Oil

34%-36%

35%-36%

35%

Product Separation and Hydrocracking

8%-10%

7%-8%

8%

Hydrogen Plant

14%-15%

11%-13%

13%

2%

2%

2%

Pyrolysis Oil Production

Utilities and WWT

Table 11 – Cost distribution in % of MFSP Manufacturing cost

Woody biomass

Herbaceous

Blends

Feedstock + Handling

27%-28%

36%

26%-27%

Natural Gas

0.5%-3.0%

0.1%-1%

2%-3%

Hydrotreating Catalysts

15%-16%

13%

14%-15%

HCK catalyst, H2 plant catalyst, other chemicals and waste disposal

0.6%-0.7%

0.6%

0.6%-0.7%

Electricity and other utilities

2%

2%

1%-2%

Fixed Costs

14%

15%

14%

Capital Depreciation

10%

10%

10%

Average Income Tax

3%

3%

3%

Average ROI

27%

28%

26%-27%


Energy & Fuels

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

Page 22 of 28

Exhaust Biomass feedstock (10% moisture)

Offgas

FAST PYROLYSIS

Pyrolysis oil

Exhaust

Offgas

HYDRODEOXYGENATION

HYDROGEN PLANT

Hydrogen gas

Stable oil

Natural gas

Offgas

HYDROCRACKING AND PRODUCT SEPARATION

Gasoline blendstock Diesel blendstock

Waste water Figure 1 – Simplified Block Flow Diagram for the overall process

Feedstock Establishment and Growing Operations

Feedstock Harvesting, Preprocessing and Transport

Biomass Conversion to Fuels

Fuel Transport

Fuel Combustion

Figure 2 – Major stages of the life cycle for fuels from fast pyrolysis and upgrading


Page 23 of 28

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

Energy & Fuels

a)

b)

c) Figure 3 – Process steps included in feedstock harvesting and logistics to produce a pyrolysis reactor-ready feed for a) Pine and Poplar, b) Corn Stover, and c) Switchgrass


Energy & Fuels

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

Blend 2

+ [VALUE] + [VALUE]

Blend 1

+ [VALUE] + [VALUE]

Page 24 of 28

+ [VALUE] + [VALUE]

Switchgrass

+ [VALUE] + [VALUE]

Corn Stover

+ [VALUE] + [VALUE]

Tulip Poplar

Hybrid Poplar

Conversion Cost

+ [VALUE] + [VALUE]

0%

10%

Base case (Pine process)

MFSP 20%

30%

40%

50%

% Variant from the Pine base case (MSFP and Conversion cost)

Figure 4 – MFSP and conversion cost in % variation of the costs of the base case (Pine process)


Page 25 of 28

Figure 5 – Sensitivity analysis results based on pine process 140 kg CO2-e/tonne delivered

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

Energy & Fuels

120 100 80 60 40 20 0

Pine

Poplar

Switchgrass

Corn Stover

Tractor Operations

4.7

2.3

5.8

Lime

0.6

0.6

Herbicide

1.2

3.2

0.7

0

P2O5

3.5

1.6

0.2

3.6

K2O

0.4

1.4

0.2

9.3

Nitrogen

43.0

40.5

114.8

45.7

Total

53.3

49.6

121.6

58.5

Figure 6 – GHGs for feedstock establishment and growth operations 25 ACS Paragon Plus Environment

Energy & Fuels

140 131 GHGs [kg CO2-e/tonne biomass]

122

120 106 100

Tractor Operations Herbicide

80

P2O5 60

K2O Nitrogen

40

Total 20 0 6,945 g N/tonne

8,047 g N/tonne

8,818 g N/tonne

Figure 7 – Sensitivity analysis of nitrogen fertilizer rate for switchgrass establishment and growth GHGs

160 GHGs, kg CO2-e/tonne delivered

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

Page 26 of 28

140 120 100 80 60 40 20 0

Whole Pine

Pulpwood

Switchgrass

Corn Stover

In-Plant Handling

7.5

7.5

12.8

12.8

Drying

6.6

6.6

5.3

6.6

Densification

16.0

16.0

16.0

16.0

Grinding

40.0

40.0

22.4

22.4

Transport & Storage Loading/Unloading

1.0

1.0

3.8

3.8

Transportation

29.3

29.3

5.3

9.9

Harvest and In-Field Processing

31.4

44.4

13.8

22.3

Total

131.8

144.8

79.4

93.7

Figure 8 – GHGs for feedstock harvesting, transport, and pre-processing operations


Page 27 of 28

25.0 Conversion GHGs, g CO2-e/MJ Fuel

20.0 15.0 10.0 5.0 0.0 Pine

Hybrid Poplar

Tulip Poplar

Switchgrass

Corn Stover

Blend 1

Blend 2

Waste Disposal

0.02

0.03

0.02

0.1

0.1

0.05

0.02

Infrastructure

0.1

0.2

0.2

0.2

0.2

0.2

0.2

Catalyst

0.3

0.3

0.3

0.4

0.3

0.3

0.3

Electricity

5.2

6.7

4.9

6.5

5.3

4.0

4.7

Natural Gas

13.6

6.0

2.6

0.7

7.4

14.9

10.2

Total

19.2

13.1

8.0

8.0

13.2

19.4

15.4

Figure 9 – GHGs from conversion of different feedstocks, including fast pyrolysis and upgrading

20 Conversion GHGs, g CO2-e/MJ Fuel

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

Energy & Fuels

18.7

18 16

15.4 14.3

14

Waste Disposal

12

Infrastructure

10

Catalyst

8

Electricity

6

Natural Gas

4

Total

2 0 5% H2

5.5% H2 (Blend 2 case)

7% H2

Figure 10 – Sensitivity analysis of hydrogen consumption for conversion of blend 2 feedstock


Energy & Fuels

45.0 GHGs, g CO2-e/MJ gasoline

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

Page 28 of 28

40.0 35.0 30.0 25.0 20.0 15.0 10.0 5.0 0.0 Pine

Hybrid Poplar

Tulip Poplar

Switchg rass

Corn Stover

Blend 1

Blend 2

Fuel Use

0.5

0.5

0.5

0.5

0.5

0.5

0.5

Fuel Distribution

0.7

0.7

0.7

0.7

0.7

0.7

0.7

Conversion

19.2

13.2

8.0

8.0

13.2

19.4

15.4

Feedstock Transportation

2.5

2.7

2.8

0.7

1.1

2.0

2.8

Feedstock Harvest and Processing

9.8

10.7

11.0

10.1

9.6

9.1

10.5

Feedstock Establishment and Growing

4.5

4.6

4.7

16.6

6.7

7.0

4.9

Total

37.1

32.3

27.8

36.5

31.9

38.7

34.8

60

65

70

61

66

58

63

%GHG Reduction from Petroleum*

*GHG reduction from the 2005 petroleum gasoline baseline of 93.08 g CO2-e/MJ

33

Figure 11 – Life cycle GHGs for gasoline fuel blendstock, including feedstock growth, fast pyrolysis and upgrading, and combustion


Field-to-Fuel Performance Testing of ... - ACS Publications

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