GIS-enabled biomass-ethanol supply chain optimization: Model development and Miscanthus application

To ensure effective biomass feedstock provision for large-scale ethanol production, a three-stage supply chain was proposed to include biomass supply sites, centralized storage and pre-processing (CSP) sites, and biorefinery sites. A GIS-enabled biomass supply chain optimization model (BioScope) was developed to minimize annual biomass-ethanol production costs by selectingthe optimal numbers, locations, and capacities of farms, CSPs, and biorefineries as well as identifying the optimal biomass flow pattern from farms to biorefineries. The model was implemented to study the Miscanthus-ethanol supply chain in Illinois. The results of the baseline case, assuming 2% of cropland is allocated for Miscanthus production, showed that unit Miscanthus-ethanol production costs were $220.6 Mg^-1, or $0.74 L^-1. Biorefinery-related costs are the largest cost component, accounting for 48% of the total costs, followed by biomass procurement, transportation, and CSP related costs. The unit Miscanthus-ethanol production costs could be reduced to $198 Mg^-1 using 20% of cropland, primarily due to savings in transportation costs. Sensitivity analyses showed that the optimal supply chain configurations, including the numbers and locations of supply sites, CSP facilities, and biorefineries, changed significantly for different cropland usage rates, biomass demands, transportation means, and pre-processing technologies. A supply chain composed of large biorefineries with the support of distributed CSP facilities was recommended to reduce biofuels production costs. Rail outperformed truck transportation to ship pre-processed biomass. Ground biomass with tapping is the suggested biomass format for the case study in Illinois, while high-density biomass formats are suggested for long distance transportation.