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Biomass supply chain management in North Carolina (part 2): biomass feedstock logistical optimization

1 BASF Corp., 26 Davis Dr., Research Triangle Park, NC, 27709 USA
2 NC State University, Biological and Agricultural Engineering, Box 7625, Raleigh, NC 27695 USA
3 Bayer Crop Science LP, 2 T.W. Alexander Dr., Research Triangle Park, NC 27709 USA
4 NC State University, Industrial and Systems Engineering, Box 7906, Raleigh, NC 27695 USA

Special Issues: Renewable energy systems and agro-residue management

Biomass logistics operations account for a major portion of the feedstock cost of running a biorefinery, and make up a significant portion of total system operational costs. Biomass is a bulky perishable commodity that is required in large quantities year round for optimal biorefinery operations. As a proof of concept for a decision making tool for biomass production and delivery, a heuristic was developed to determine biorefinery location, considering city size, agricultural density, and regional demographics. Switchgrass and sorghum (with winter canola) were selected to examine as viable biomass feedstocks based on positive economic results determined using a predictive model for cropland conversion potential. Biomass harvest systems were evaluated to examine interrelationships of biomass logistical networks and the least cost production system, with results demonstrating a need to shift to maximize supply-driven production harvest operations and limit storage requirements. For this supply-driven production harvest operations approach a harvest window from September until March was selected for producing big square bales of switchgrass for storage until use, forage chopped sorghum from September to December, and forage chopped switchgrass from December to March. A case study of the three major regions of North Carolina (Mountains, Piedmont, and Coastal Plain) was used to assess logistical optimization of the proposed supply-driven production harvest system. Potential biomass production fields were determined within a hundred mile radius of the proposed biorefinery location, with individual fields designated for crop and harvest system by lowest transportation cost. From these selected fields, crops and harvest system regional storage locations were determined using an alternate location-allocation heuristic with set storage capacity per site. Model results showed that the supply-driven production harvest system greatly reduced system complexity, maximized annual usage of high cost specialized equipment, and reduced logistical operations cost. The siting method and developed model shows promise and can be used for computational analysis of potential biorefinery site biomass production systems before costly on the ground logistical analysis.
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