A Multi-Criteria Decision Analysis Approach to Facility Siting in a Wood-Based Depot-and-Biorefinery Supply Chain Model - Martinkus, Natalie; Latta, Greg; Brandt, Kristin; Wolcott, Michael (Frontiers in Energy Research, 2018)
As the lignocellulosic biofuels industry is still developing, reducing operational, and capital costs along the supply chain can increase the competitiveness of the final fuel price and investor willingness to commit funds. Capital cost savings may be realized through co-locating depots with active biomass processing plants, such as saw mills, and through repurposing existing industrial facilities, such as pulp mills, into a biorefinery. Operational cost savings may be gained through the selective siting of depots and biorefineries based on operational cost components that vary geospatially, such as energy rates and feedstock availability. Utilizing depots in a biofuel supply chain to procure and preprocess feedstock has additionally been found to mitigate supply risk in regions of low biomass availability, as well as reduce the biorefinery footprint. A multi-criteria decision support tool (DST) is utilized to assess existing industrial facilities for their potential role in a wood-based depot-and-biorefinery supply chain. Geospatial cost components are identified through techno-economic analyses for use as siting criteria for the depots and biorefinery. The “repurpose potential” of industrial facilities is assessed as a siting criterion for candidate biorefinery locations. A case study is presented in the Inland Northwest region of the United States to assess the usefulness of the tool in selecting industrial facilities for a configured depot-and-biorefinery supply chain. The results are compared against optimization runs of the candidate facilities to validate the depots selected by the DST. In two of the three supply chains, the DST selected the same or similar facilities as the optimization run for no net increase in annual cost. The third supply chain showed an ~1% increase in annual cost over the optimized facilities selected.