U.S. Billion-Ton Update: Biomass Supply for a Bioenergy and Bioproducts Industry

Downing, Mark; Eaton, Laurence; Graham, R.L.; Langholtz, Matthew; Perlack, R.D.; Turhollow, Anthony; Stokes, Bryce J.; Brandt, Craig C.

doi:10.2172/1023318

Cited by 163 publications

(59 citation statements)

References 121 publications

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“…With the exception of a recent study [18], most prior LCA studies of bio-ethanol [10,11,13,14,37] have assumed a conventional harvest and biomass delivery in bale format, resulting in relatively low (approximately 10%) net life cycle GHG emissions [11]. Here, we focus on identifying and characterizing uncertainties in advanced agricultural residue harvest, collection, storage, transport, pre-processing, and delivery operations to bio-ethanol facilities in the U.S. Midwest that arise due to variability in: (1) the sustainable harvest yield, defined as the quantity of corn stover removal set to maintain erosion and soil carbon within tolerable levels [2]; (2) transportation of the agricultural residue to depots that process and densify the biomass; (3) depot facility size, which influences equipment and energy throughput per unit of biomass; and (4) long-distance transport of the densified biomass delivered to the bio-ethanol facility. While we note the significant variability in GHG emissions from feedstock production noted in literature, and in particular the possible risks to loss of soil organic carbon (SOC) with corn stover removal [38,39], here we focus exclusively on uncertainties that could arise due to the spatial variability of corn stover feedstocks available at different spatial densities in the U.S. Midwest.…”

Section: Methodsmentioning

confidence: 99%

“…The criteria for location selection consisted of the presence of transportation infrastructure (railroads and road systems) and annual biomass availability (i.e., sustainable harvest yield) in the state of Kansas [2]. Significant factors such as access to water, and availability of utilities and labor were assumed sufficient for the model scenarios constructed.…”

Section: Data Management and Analysismentioning

confidence: 99%

“…Biomass supply data were assumed to correspond to the marginal access (farm-to-gate) cost, which is proportionally related to biomass demand. The US Department of Energy's U.S. Billion-Ton Update Report (BT2) [2] estimates biomass marginal access costs in $5/ton increments. For the counties we consider in Kansas, biomass marginal access costs begin at $40/ton and can be as high as $80/ton depending upon the available supply within each county.…”

Section: Data Management and Analysismentioning

confidence: 99%

“…To meet this demand, an estimated 242 million Mg/year of biomass will need to be supplied to biorefineries that can process lignocellulose [1]. Sufficient biomass supply has been identified to meet these requirements through large-scale national assessments [2], and research is on-going concerning the logistics required to cultivate, harvest, transport, and process such large quantities of biomass into fuel. Our study focuses on the supply and logistics chain of the lignocellulosic ethanol produced from corn stover, an agricultural residue.…”

Section: Introductionmentioning

confidence: 99%

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Uncertainties in Life Cycle Greenhouse Gas Emissions from Advanced Biomass Feedstock Logistics Supply Chains in Kansas

et al. 2014

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To meet Energy Independence and Security Act (EISA) cellulosic biofuel mandates, the United States will require an annual domestic supply of about 242 million Mg of biomass by 2022. To improve the feedstock logistics of lignocellulosic biofuels in order to access available biomass resources from areas with varying yields, commodity systems have been proposed and designed to deliver quality-controlled biomass feedstocks at preprocessing "depots". Preprocessing depots densify and stabilize the biomass prior to long-distance transport and delivery to centralized biorefineries. The logistics of biomass commodity supply chains could introduce spatially variable environmental impacts into the biofuel life cycle due to needing to harvest, move, and preprocess biomass from multiple distances that have variable spatial density. This study examines the uncertainty in greenhouse gas (GHG) emissions of corn stover logistics within a bio-ethanol supply chain in the state of Kansas, where sustainable biomass supply varies spatially. Two scenarios were evaluated each having a different number of depots of varying capacity and location within Kansas relative to a central commodity-receiving biorefinery to test GHG emissions uncertainty. The first scenario sited four preprocessing depots evenly across the state of Kansas but within the vicinity of counties having high biomass supply density. OPEN ACCESSEnergies 2014, 7 7126The second scenario located five depots based on the shortest depot-to-biorefinery rail distance and biomass availability. The logistics supply chain consists of corn stover harvest, collection and storage, feedstock transport from field to biomass preprocessing depot, preprocessing depot operations, and commodity transport from the biomass preprocessing depot to the biorefinery. Monte Carlo simulation was used to estimate the spatial uncertainty in the feedstock logistics gate-to-gate sequence. Within the logistics supply chain GHG emissions are most sensitive to the transport of the densified biomass, which introduces the highest variability (0.2-13 g CO2e/MJ) to life cycle GHG emissions. Moreover, depending upon the biomass availability and its spatial density and surrounding transportation infrastructure (road and rail), logistics can increase the variability in life cycle environmental impacts for lignocellulosic biofuels. Within Kansas, life cycle GHG emissions could range from 24 g CO2e/MJ to 41 g CO2e/MJ depending upon the location, size and number of preprocessing depots constructed. However, this range can be minimized through optimizing the siting of preprocessing depots where ample rail infrastructure exists to supply biomass commodity to a regional biorefinery supply system.

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Section: Methodsmentioning

confidence: 99%

Section: Data Management and Analysismentioning

confidence: 99%

Section: Data Management and Analysismentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Uncertainties in Life Cycle Greenhouse Gas Emissions from Advanced Biomass Feedstock Logistics Supply Chains in Kansas

et al. 2014

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“…The weight of No. 2 fi corn at 15.5% moisture is applied to calculate the corn grain yields [24]. The county level corn production and yield data from 2003-2012 are collected from the National Agricultural Statistics Service (NASS), United States Department of Agriculture (USDA) [25].…”

Section: Data Sourcesmentioning

confidence: 99%

Supply chain design under uncertainty for advanced biofuel production based on bio-oil gasification

2014

Energy

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An advanced biofuels supply chain is proposed to reduce biomass transportation costs and take advantage of the economics of scale for a gasification facility. In this supply chain, biomass is converted to bio-oil at widely distributed small-scale fast pyrolysis plants, and after bio-oil gasification, the syngas is upgraded to transportation fuels at a centralized biorefinery. A two-stage stochastic programming is formulated to maximize biofuel producers' annual profit considering uncertainties in the supply chain for this pathway. The first stage makes the capital investment decisions including the locations and capacities of the decentralized fast pyrolysis plants as well as the centralized biorefinery, while the second stage determines the biomass and biofuels flows. A case study based on Iowa in the U.S. illustrates that it is economically feasible to meet desired demand using corn stover as the biomass feedstock. The results show that the locations of fast pyrolysis plants are sensitive to uncertainties while the capacity levels are insensitive. The stochastic model outperforms the deterministic model in the stochastic environment, especially when there is insufficient biomass. Also, farmers' participation can have a significant impact on the profitability and robustness of this supply chain. Keywords stochastic programming, bio-oil gasification, supply chain design Disciplines Industrial Engineering | Systems EngineeringComments NOTICE: This is the author's version of a work that was accepted for publication in Energy. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Energy, 74, September (2014) NOTICE: This is the author's version of a work that was accepted for publication in Energy. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Energy, 74, September (2014) AbstractAn advanced biofuels supply chain is proposed to reduce biomass transportation costs and take advantage of the economics of scales for gasification facility. In this supply chain, biomass is converted to bio-oil at widely distributed small-scale fast pyrolysis plants, and after bio-oil gasification, the syngas is upgraded to transportation fuels at centralized biorefi . A twostage stochastic programming is formulated to maximize biofuel producers' annual profi considering uncertainties in supply chain for this pathway. The fi makes the capital investment decisions including the locations and capacities of the decentralized fast pyrolysis plants as well as the centralized biorefi while the second-stag...

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Synthesis of augmented biofuel processes using solar energy

Mallapragada

Tawarmalani

2014

AIChE Journal

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A method for synthesizing augmented biofuel processes, which improve biomass carbon conversion to liquid fuel (g carbon ) using supplemental solar energy as heat, H 2 , and electricity is presented. For a target g carbon , our method identifies augmented processes requiring the least solar energy input. A nonconvex mixed integer nonlinear programming model allowing for simultaneous mass, heat, and power integration, is built over a process superstructure and solved using global optimization tools. As a case study, biomass thermochemical conversion via gasification/Fischer-Tropsch synthesis and fast-hydropyrolysis/hydrodeoxygenation (HDO) is considered. The optimal process configurations can be categorized either as standalone (g carbon 54%), augmented using solar heat (54% g carbon 74%), or augmented using solar heat and H 2 (74 g carbon 95%). Importantly, the process H 2 consumption is found to be close to the derived theoretical minimum values. To accommodate for the intermittency of solar heat/H 2 , we suggest processes that can operate at low and high g carbon .All the flow rates are normalized to 1 kmol/h of biomass carbon feed processed. Multiple values (separated by commas) considered for certain parameters.

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U.S. Billion-Ton Update: Biomass Supply for a Bioenergy and Bioproducts Industry

Cited by 163 publications

References 121 publications

Uncertainties in Life Cycle Greenhouse Gas Emissions from Advanced Biomass Feedstock Logistics Supply Chains in Kansas

Uncertainties in Life Cycle Greenhouse Gas Emissions from Advanced Biomass Feedstock Logistics Supply Chains in Kansas

Supply chain design under uncertainty for advanced biofuel production based on bio-oil gasification

Synthesis of augmented biofuel processes using solar energy

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