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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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Keywords Biomass logistics; switchgrass; sorghum; biomass harvest system; storage analysis

Citation: Kevin Caffrey, Mari Chinn, Matthew Veal, Michael Kay. Biomass supply chain management in North Carolina (part 2): biomass feedstock logistical optimization. AIMS Energy, 2016, 4(2): 280-299. doi: 10.3934/energy.2016.2.280

References

  • 1. US EPA (2011) Biofuels and the Environment: First Triennial Report to Congress. United States Environmental Protection Agency, National Center for Environmental Assessment, Office of Research and Development. EPA/600/R-10/183F.
  • 2. US Cong (2007) Energy Independence and Security Act of 2007. 100th Congress, 1st session HR 6.4.
  • 3. US DOE (2011) US Billion Ton Update: Biomass Supply for a Bioenergy and Bioproducts Industry. Leads: RD Perlack and BJ Stokes. United States Department of Energy, Oak Ridge National Laboratory. ORNL/TM-2011/224.
  • 4. US DOE (2005) Biomass as Feedstock for a Bioenergy and Bioproducts Industry: The Technical Feasibility of a Billion Ton Annual Supply. Leads: RD Perlack, BJ Stokes, and DC Erbach. United States Department of Energy, Oak Ridge National Laboratory ORNL/TM-2005/66.
  • 5. BR & DB (2008) National Biofuels Action Plan October 2008. Biomass Research and Development Board, Biomass Research and Development Initiative.
  • 6. Haldi J, David W (1967) Economies of Scale in Industrial Plants. J polit econ 75: 373-385.    
  • 7. US DOE (2002) Lignocellulosic Biomass to Ethanol Process Design and Economics Utilizing Co-Current Dilute Acid Prehydrolysis and Enzymatic Hydrolysis for Corn Stover. A Aden, M Ruth, K Ibsen, et al., United States Department of Energy, National Renewable Energy Laboratory, NREL/TP-510-32438.
  • 8. Resop JP, Cundiff JS, Heatwole CD (2011) Spatial analysis of site satellite storage locations for herbaceous biomass in the piedmont of the southeast. Appl eng agric 27: 25-32.    
  • 9. Judd JD, Sarin SC, Cundiff JS (2012) Design, Modeling and Analysis of a Feedstock Logistics System. Bioresource Technol 103: 209-218.
  • 10. Kumar A, Sokhansanj S (2007) Switchgrass (Panicum virgatum, L.) delivery to a biorefinery using integrated biomass supply analysis and logistics (IBSAL) model. Bioresource Technol 98: 1033-1044.
  • 11. Worley JS, Cundiff JS (1991) System analysis of sweet sorghum harvest for ethanol production in the piedmont. Transactions of the ASAE 34: 539-547.    
  • 12. US DOE (2009) Commodity-Scale Production of an Infrastructure-Compatible Bulk Solid from Herbaceous Lignocellulosic Biomass: Uniform-Format Bioenergy Feedstock Supply System Design Report Series. Hess JR, Kenney KL, Ovard LP, et al. United States Department of Energy, National Renewable Energy Laboratory. INL/EXT-09-17527.
  • 13. Shastri Y, Hansen A, Rodriguez L, et al. (2011) Development and application of BioFeed model for optimization of herbaceous feedstock production. Biomass Bioenerg 35: 2961-2974.    
  • 14. Shastri YN, Rodriguez LF, Hansen AC, et al. (2012) Impact of distributed storage and pre-processing on Miscanthus production and provision systems. Biofuel Bioprod Bior 6: 21-31.
  • 15. Ravula PP, Grisso RD, Cundiff JS (2008) Cotton logistics as a model for a biomass transportation system. Biomass Bioenerg 32: 314-325.    
  • 16. Cundiff JS, Grisso RD, Ravula PP (2004) Management for Biomass Delivery at a Conversion Plant. 2004 American Society of Agricultural Engineers and Canadian Society of Agricultural Engineers Annual International Meeting August 1-4, 2004 Ottawa, Ontario.
  • 17. Cundiff JS, Grisso RD (2008) Containerized Handling to Minimize Hauling Cost of Herbaceous Biomass. Biomass Bioenerg 32: 308-313.
  • 18. An H, Willhelm WE, Searcy SW (2011) A mathematical model to design a lignocellulosic biofuel supply chain system with a case study based on a region in Central Texas. Bioresource Technol 102: 7860-7870.
  • 19. Kay, MG (2014) Matlog: Logistics engineering Matlab toolbox. North Carolina State University, Fitts Department of Industrial and Systems Engineering. Available from: http://www.ise.ncsu.edu/kay/matlog/
  • 20. Caffrey KR, Chinn MS, Veal MW (2015). Biomass supply chain management in North Carolina (part 1): predictive model for cropland conversion to biomass feedstocks. AIMS Energy 4: 256-279.
  • 21. Hughes SR, Gibbons WR, Moser BR, et al., (2013) Sustainable Multipurpose Biorefineries for Third-Generation Biofuels and Value-Added Co-Products. InTech. Available from: www.intechopen.com
  • 22. Saxe C (2004) Big Bale Storage Losses. University of Wisconsin Extension, November 2004.
  • 23. Lazarus WF (2009) Machinery Cost Estimates. University of Minnesota Extension. September 2014.
  • 24. Lazarus WF (2014) Machinery Cost Estimates. University of Minnesota Extension. June 2014.
  • 25. USDA (2015) Cash Rents by County. United States Department of Agriculture, National Agricultural Statistics Service. August 2014. Available at: www.nass.usda.gov
  • 26. Atkinson AD, Rich BA, Tungate KD, et al. (2006) North Carolina Canola Production. North Carolina State University, North Carolina Solar Center & College of Agricultural and Life Sciences. SJS/KEL-9/06-W07.
  • 27 Xiao R, Cai Z, Zhang X (2012) A production optimization model of supply-driven chain quality uncertainty. J Syst Sci Systems Eng 21: 144-160.    
  • 28. Benson GA, Green JT (2013) Corn Silage 2013. North Carolina State University, Department of Agricultural and Resource Economics. Available from:
    http://ag-econ.ncsu.edu/extension/budgets.
  • 29. Hwang S, Epplin FM, Lee BH, et al. (2009) A probabilistic estimate of the frequency of mowing and baling days available in Oklahoma USA for the harvest of switchgrass for use in biorefineries. Biomass Bioenerg 33: 1037-1045.    
  • 30. Crouse D (2003) Realistic yields and nitrogen application factors for North Carolina crops. http://nutrients.soil.ncsu.edu/yields/ North Carolina State University, North Carolina Department of Agriculture and Consumer Services, North Carolina Department of Environment and Natural Resources, Natural Resources Conservation Service. Raleigh NC
  • 31. USDA (2014) Cropland Data Layer 2013. Published crop-specific data layer [online]. United States Department of Agriculture, National Agricultural Statistics Service. Available from: http://nassgeodata.gmu.edu/CropScape. Accessed: July 2014.
  • 32. Bullen G, Weddington E (2012) Enterprise Budgets: Corn- Conventional Till-NC, Coastal Plain 2012. North Carolina State University, Department of Agricultural and Resource Economics. Available from: http://ag-econ.ncsu.edu/extension/budgets.
  • 33. Bullen G, Weddington E (2012). Enterprise Budgets: Corn- No Till-NC, Coastal Plain 2012. North Carolina State University, Department of Agricultural and Resource Economics. Available from: http://ag-econ.ncsu.edu/extension/budgets.
  • 34. NCSU (2014) Enterprise Budget: No-Till Grain Sorghum for the Coastal Plain Region of NC. North Carolina State University, Department of Agricultural and Resource Economics. Available from: http://ag-econ.ncsu.edu/extension/budgets.
  • 35. Bullen G, Dunphy J (2012). Enterprise Budgets: Soybeans- Full Season, Conventional Tillage 2012. North Carolina State University, Department of Agricultural and Resource Economics. Available from: http://ag-econ.ncsu.edu/extension/budgets.
  • 36. Bullen G, Dunphy J (2012) Enterprise Budgets: Soybeans- Full Season, No Till 2012. North Carolina State University, Department of Agricultural and Resource Economics. Available from: http://ag-econ.ncsu.edu/extension/budgets.
  • 37. Bullen G, Weddington E (2012) Enterprise Budgets: Wheat for Grain Conventional 2012. North Carolina State University, Department of Agricultural and Resource Economics. Available from: http://ag-econ.ncsu.edu/extension/budgets.
  • 38. Bullen G, Weddington E (2012) Enterprise Budgets: Wheat for Grain No-Till 2012. North Carolina State University, Department of Agricultural and Resource Economics. Available from: http://ag-econ.ncsu.edu/extension/budgets.
  • 39. Green JT, Benson GA (2013) Bermuda Grass for Pasture. North Carolina State University, Department of Agricultural and Resource Economics. Available from:
    http://ag-econ.ncsu.edu/extension/budgets.
  • 40. Green JT, Benson GA (2013) Bermuda Grass for Hay. North Carolina State University, Department of Agricultural and Resource Economics. Available from:
    http://ag-econ.ncsu.edu/extension/budgets.
  • 41. Green JT, Benson GA (2013) Cool Season Perennial Grass for Pasture. North Carolina State University, Department of Agricultural and Resource Economics. Available from:
    http://ag-econ.ncsu.edu/extension/budgets.
  • 42. Green JT, Benson GA (2013) Hay Harvest Cost, Large Round Baler. North Carolina State University, Department of Agricultural and Resource Economics. Available from:
    http://ag-econ.ncsu.edu/extension/budgets.
  • 43. Cooper L (1964) Heuristic Method for Location-Allocation Problems. SIAM Review 6: 36-53.
  • 44. US BOC (2014) Topologically Integrated Geographic Encoding and Referencing 2010. United States Bureau of the Census. Available from:
    https://www.census.gov/geo/maps-data/data/tiger.html
  • 45. US DOI (2014) National Land Cover Database 2011. United States Department of the Interior, Multi-Resolution Land Characteristics Consortium (MRLC). Available from:
    http://www.mrlc.gov/nlcd2011.php
  • 46 Love R, Morris J, Wesolowsky G (1988) Facilities Location: Models and Methods, New York: North-Holland.

 

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Copyright Info: 2016, Mari Chinn, et al., licensee AIMS Press. This is an open access article distributed under the terms of the Creative Commons Attribution Licese (http://creativecommons.org/licenses/by/4.0)

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