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Assessment of the feedstock supply for siting single‐ and multiple‐feedstock biorefineries in the USA and identification ...

by Bhavna Sharma, Craig C Brandt, Devita D Amal, Matthew H Langholtz, Erin G Webb
Publication Type
Journal Name
Biofuels, Bioproducts and Biorefining
Publication Date
Page Numbers
578 to 593

An integrated multi‐feedstock bioenergy (i.e., biofuel, biopower, or bioproduct) supply system has potential to reduce biomass supply system uncertainties and costs. This study identifies optimal configurations of multi‐feedstock biomass‐to‐biorefinery supply chains and pertinent feedstock combinations based on spatial distribution of feedstock and lowest delivered cost to the biorefinery. We used the Supply Characterization Model (SCM) to allocate feedstock supplies to candidate biorefinery facilities. Model runs were performed for herbaceous energy crops, agriculture residue, and woody biomass available in 2017, 2022, 2025, and 2030 as estimated by the Policy Analysis System (POLYSYS) and Forest Sustainable and Economic Analysis Model (ForSEAM) models. Three feedstock supply scenarios were compared: (a) an herbaceous scenario: switchgrass, miscanthus, biosorghum, and corn stover; (b) a woody scenario: coppice wood, noncoppice wood, whole trees, and forestry residues, and (c) a mixed scenario: a combination of all feedstocks in herbaceous and woody scenarios. By 2030 the analyses predicted that 323, 168, and 473 biorefineries were sited in the herbaceous, woody, and mixed scenario, respectively, in the conterminous USA. Feedstock mixes supplied to the biorefineries were mostly dominated by a single feedstock. The most prominent feedstock mixes identified were: (1) switchgrass and miscanthus; (2) coppice and noncoppice wood; and (3) coppice wood, noncoppice wood, switchgrass and miscanthus. Biorefineries using multi‐feedstock would be beneficial for growth of bioeconomy, however flexible and cost‐effective conversion platforms should be developed to efficiently utilize multiple feedstocks. This analysis identifies biorefinery locations and feedstock supply mixes while minimizing delivered feedstock costs based on spatial and temporal feedstock availability.