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

Abstract

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.

Bioproducts from agro-industrial plant residues: opportunities for sustainable reuse / Bioprodutos de resíduos agroindustriais vegetais: oportunidades ao reaproveitamento sustentável

“…Barnabé et al 2019;Cervi et al 2021;Fini et al 2016;Gu et al 2018;Heinz et al 2017;Maciel et al 2020;Martín et al 2017;Ndukwe et al 2020;Nunes et al 2019;Oleson and Schwartz 2016;Ponte et al 2019;Rambo et al 2015;Sharma et al 2020;Srinivas et al 2016;Tavares et al 2020;Zhao et al 2019 …”

Economic viability and carbon footprint of switchgrass for cellulosic biofuels: Insights from a spatial multi‐feedstock procurement landscape analysis

“…Data from Jasper and White Counties in Indiana are the basis for calculation of most of the exogenous parameters. These counties have the highest corn planting densities and yields in Indiana, and are, as such, a promising location for an ethanol plant (Sesmero, Balagtas, et al, 2015; Sharma et al, 2020). In addition to access to feedstocks, infrastructure (roads, rail, etc.)…”

“…Second, given the lack of observational data, we combine historical data on land use from the National Agricultural Statistics Service, crop simulation models, and farm‐level data to calibrate the model to growing conditions in two counties in Indiana. This area has high corn planting density and was also identified as promising in Sharma et al (2020).…”

The Economic Accessibility of CO₂ Sequestration Through Bioenergy with Carbon Capture and Sequestration (BECCS) in the US

“…A near-term pulverized generation scenario is excluded under the assumption that widescale pelletization is not immediately available to produce pellets needed by pulverizing systems, but rather can be developed in the long-term scenario. All power facilities are assumed capable of using all feedstock types, though delivered feedstocks are likely to be geographically concentrated by feedstock type [49]. Following are descriptions of modeling and key data inputs presented in sequence of application.…”