A Composite-Curve-Based Biomass Procurement Planning Approach

  • WenZhao Wu
  • Daniel Kurniawan
  • WenBo Zhu
  • Christos T. MaraveliasEmail author


The production of transportation fuels from biomass is a promising renewable alternative to traditional fossil fuels. To achieve low carbon footprint of the overall biofuel supply chain however an efficient biomass procurement plan is essential. To this end, we discuss a novel approach to biomass procurement planning. In terms of transportation, we propose a region-to-point modeling approach based on mathematical integration over the sourcing region that has unique characteristics such as shape, location, and productivity. Both algebraic and numerical solution methods are discussed. In terms of system-level procurement planning, we develop a composite-curve-based approach that incorporates the regional transportation modeling method, and aims at identifying the biomass procurement plan that minimizes the total procurement cost (including biomass purchasing, harvesting and transportation). The specific steps for the generation of the composite curve, as well as insights into the procurement planning problem are discussed. We complete the chapter with a case study illustrating the applicability of the proposed methods.


Corn Stover Polar Coordinate System Procurement Cost Procurement Plan Transportation Distance 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.



This work was funded by the DOE Great Lakes Bioenergy Research Center (DOE Office of Science BER DE-FC02-07ER64494).


  1. Akgul, O., Shah, N., & Papageorgiou, L. G. (2012). Economic optimisation of a UK advanced biofuel supply chain. Biomass and Bioenergy, 41, 57–72.CrossRefGoogle Scholar
  2. Andersen, F. E., Diaz, M. S., & Grossmann, I. E. (2013). Multiscale strategic planning model for the design of integrated ethanol and gasoline supply chain. AIChE Journal, 59(12), 4655–4672.CrossRefGoogle Scholar
  3. An, H., Wilhelm, W. E., & Searcy, S. W. (2011). A mathematical model to design a lignocellulosic biofuel supply chain system with a case study based on a region in Central Texas. Bioresource technology, 102(17), 7860–7870.CrossRefGoogle Scholar
  4. Brown, R. C., & Brown, T. R. (2012). Why are we producing biofuels. Ames, Iowa: Brownia LLC.Google Scholar
  5. DOE Bioenergy Technologies Office. (2014). Multi-year program plan. Washington DC, USA: DOE.Google Scholar
  6. DOE/EERE (2013). Feedstock supply and logistics: biomass as a commodity, US: Department of Energy, Office of Energy Efficiency and Renewable EnergyGoogle Scholar
  7. Elia, J. A., Baliban, R. C., Xiao, X., & Floudas, C. A. (2011). Optimal energy supply network determination and life cycle analysis for hybrid coal, biomass, and natural gas to liquid (CBGTL) plants using carbon-based hydrogen production. Computer Chemical Engineering, 35(8), 1399–1430.CrossRefGoogle Scholar
  8. Garcia, D. J., & You, F. (2015). Supply chain design and optimization: challenges and opportunities. Computers and Chemical Engineering, 81, 153–170.CrossRefGoogle Scholar
  9. Hamelinck, C. N., Suurs, R. A., & Faaij, A. P. (2005). International bioenergy transport costs and energy balance. Biomass and Bioenergy, 29(2), 114–134.CrossRefGoogle Scholar
  10. Josephine, E. A., & Floudas, C. A. (2014). Energy supply chain optimization of hybrid feedstock processes: a review. Chemical and Biomolecular Engineering, 5, 147–179.CrossRefGoogle Scholar
  11. Linnhoff, B., & Hindmarsh, E. (1983). The pinch design method for heat exchanger networks. Chemical Engineering Science, 38(5), 745–763.CrossRefGoogle Scholar
  12. Liu, P., Georgiadis, M. C., & Pistikopoulos, E. N. (2010). Advances in energy systems engineering. Industrial and Engineering Chemistry Research, 50(9), 4915–4926.CrossRefGoogle Scholar
  13. Marvin, W. A., et al. (2012a). Economic optimization of a lignocellulosic biomass-to-ethanol supply chain. Chemical Engineering Science, 67(1), 68–79.CrossRefGoogle Scholar
  14. Marvin, W. A., Schmidt, L. D., & Daoutidis, P. (2012b). Biorefinery location and technology selection through supply chain optimization. Industiral and Engineering Chemistry Research, 52(9), 3192–3208.CrossRefGoogle Scholar
  15. Miao, Z., et al. (2012). Lignocellulosic biomass feedstock transportation alternatives, logistics, equipment configurations, and modeling. Biofuels, Bioproducts and Biorefining, 6(3), 351–362.CrossRefGoogle Scholar
  16. Mosier, N., et al. (2005). Features of promising technologies for pretreatment of lignocellulosic biomass. Bioresource Technology, 96, 673–686.CrossRefGoogle Scholar
  17. Osmani, A., & Zhang, J. (2013). Stochastic optimization of a multi-feedstock lignocellulosic-based bioethanol supply chain under multiple uncertainties. Energy, 59, 172–457.CrossRefGoogle Scholar
  18. Overend, R. P. (1982). The average haul distance and transportation work factors for biomass delivered to a central plant. Biomass, 2, 75–79.CrossRefGoogle Scholar
  19. Santibanez-Aguilar, J. E., et al. (2011). Optimal planning of a biomass conversion system considering economic and environmental aspects. Industrial and Engineering Chemistry Research, 50(14), 8558–8570.CrossRefGoogle Scholar
  20. Sharma, B., Ingalls, R. G., Jones, C. L., & Khanchi, A. (2013). Biomass supply chain design and analysis: basis, overview, modeling, challenges, and future. Renewable and Sustainable Energy Reviews, 24, 608–627.CrossRefGoogle Scholar
  21. Sultana, A., & Kumar, A. (2014). Development of tortuosity factor for assessment of lignocellulosic biomass delivery cost to a biorefinery. Applied Energy, 119, 288–295.CrossRefGoogle Scholar
  22. Tong, K., Gong, J., Yue, D., & You, F. (2013). Stochastic programming approach to optimal design and operations of integrated hydrocarbon biofuel and petroleum supply chains. ACS Sustainable Chemistry and Engineering, 2(1), 49–61.CrossRefGoogle Scholar
  23. Uslu, A., Faaij, A. P., & Bergman, P. C. (2008). Pre-treatment technologies, and their effect on international bioenergy supply chain logistics. Techno-economic evaluation of torrefaction, fast pyrolysis and pelletisation. Energy, 33, 1206–1223.CrossRefGoogle Scholar
  24. You, F., Tao, L., Graziano, D., & Snyder, S. W. (2012). Optimal design of sustainable cellulosic biofuel supply chains: multiobjective optimization coupled with life cycle assessment and input-output analysis. AIChE Journal, 58, 1157–1180.CrossRefGoogle Scholar
  25. Yue, D., You, F., & Snyder, S. W. (2014). Biomass-to-bioenergy and biofuel supply chain optimization: Overview, key issues and challenges. Computer Chemical Engineering, 66, 36–56.CrossRefGoogle Scholar
  26. Zhang, F., Johnson, D. M., & Sutherland, J. W. (2011). A GIS-based method for identifying the optimal location for a facility to convert forest biomass to biofuel. Biomass and Bioenergy, 35, 3951–3961.Google Scholar

Copyright information

© Springer International Publishing Switzerland 2017

Authors and Affiliations

  • WenZhao Wu
    • 1
  • Daniel Kurniawan
    • 1
  • WenBo Zhu
    • 1
  • Christos T. Maravelias
    • 1
    Email author
  1. 1.Department of Chemical and Biological EngineeringUniversity of Wisconsin-MadisonMadisonUSA

Personalised recommendations