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Decision Support Models for Integrated Design of Bioenergy Supply Chains

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Handbook of Bioenergy

Part of the book series: Energy Systems ((ENERGY))

Abstract

The scarcity of fossil fuels and the environmental implications of their use has drawn increasing attention to the production of bioenergy from nonfood sources. To validate the progressive experimental research in this field, we require a credible tool that can quantify various impacts of potential biorefining processes. This chapter will demonstrate a novel decision support model that can provide comprehensive techno-economic results to various stakeholders. The framework integrates process optimization , supply chain optimization and discrete event simulation (DES) capabilities to provide a comprehensive and multi-disciplinary tool for bioenergy supply chain design following an iterative process. The tool is further enhanced by the incorporation of supply chain risk modeling to capture various uncertainties. A proof of concept case study is presented to illustrate the applicability of this framework to any given geographic region.

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References

  • Aden A, Ruth M, Ibsen K, Jechura J, Neeves K, Sheehan J, Wallace B (2002) Lignocellulosic biomass to ethanol process design and economics utilizing co-current dilute acid prehydrolysis and enzymatic hydrolysis for corn stover. National Renewable Energy Laboratory, Golden, Colorado, USA

    Book  Google Scholar 

  • Akgul O, Zamboni A, Bezzo F, Shah N, Papageorgiou LG (2011) Optimization-based approaches for bioethanol supply chains. Ind Eng Chem Res 50(9):4927–4938. doi:10.1021/ie101392y

    Article  Google Scholar 

  • Aksoy B, Cullinan H, Webster D, Gue K, Sukumaran S, Eden M, Sammons N (2011) Woody biomass and mill waste utilization opportunities in Alabama: transportation cost minimization, optimum facility location, economic feasibility, and impact. Environ Prog Sustain Energ 30(4):720–732. doi:10.1002/ep.10501

    Article  Google Scholar 

  • Amundson J, Faulkner W, Sukumara S, Seay J, Badurdeen F (2012) A Bayesian Network based approach for risk modeling to aid in development of sustainable biomass supply chains. In: 22nd european symposium on computer aided process engineering ,vol 30, pp 152–156. doi:10.1016/B978-0-444-59519-5.50031-9

  • Amundson J (2013) Modeling of biorefinery supply chain economic performance with discrete event simulation. Lexington, Manufacturing Systems Engineering, University of Kentucky

    Google Scholar 

  • An H, Wilhelm WE, Searcy SW (2011) Biofuel and petroleum-based fuel supply chain research: a literature review. Biomass Bioenergy 35(9):3763–3774. doi:10.1016/j.biombioe.2011.06.021

    Google Scholar 

  • Azapagic A (2014) Sustainability considerations for integrated biorefineries. Trends Biotechnol 32(1):1–4. doi:10.1016/j.tibtech.2013.10.009

    Article  Google Scholar 

  • Badurdeen F, Shuaib M, Wijekoon K, Brown A, Faulkner W, Amundson J, Jawahir IS, Goldsby T, Iyengar D, Boden B (2014) Quantitative modeling and analysis of supply chain risks using Bayesian Theory. J Manufact Technol Manage (Accepted for Publication)

    Google Scholar 

  • Batidzirai B, Smeets EMW, Faaij André PC (2012) New conversion technologies for liquid biofuels production in Africa. In: Janssen Rainer, Rutz Dominik (eds) Bioenergy for sustainable development in Africa. Springer, Netherlands, pp 117–130

    Google Scholar 

  • Bernardi A, Sara G, Bezzo F (2013) Spatially explicit multiobjective optimization for the strategic design of first and second generation biorefineries including carbon and water footprints. Ind Eng Chem Res 52(22):7170–7180. doi:10.1021/ie302442j

    Article  Google Scholar 

  • Brown A, Amundson J, Badurdeen F (2012) Bayesian informed simulation for supply chain risk probability and impact assessment. In: 22nd international conference on production research

    Google Scholar 

  • Corsano G, Vecchietti AR, Montagna JM (2011) Optimal design for sustainable bioethanol supply chain considering detailed plant performance model. Comput Chem Eng 35(8):1384–1398. doi:10.1016/j.compchemeng.2011.01.008

    Article  Google Scholar 

  • Coyle WT (2010) Next-generation biofuels: near-term challenges and implications for agriculture. Washington

    Google Scholar 

  • Dal-Mas M, Giarola S, Zamboni A, Bezzo F (2011) Strategic design and investment capacity planning of the ethanol supply chain under price uncertainty. Biomass Bioenergy 35(5):2059–2071. doi:10.1016/j.biombioe.2011.01.060

    Article  Google Scholar 

  • De Mol RM, Jogems MAH, Beek PV, Gigler JK (1997) Simulation and optimization of the logistics of biomass fuel collection. Neth J Agric Sci 45:219–228

    Google Scholar 

  • Dunnett A, Adjiman C, Shah N (2007) Biomass to heat supply chains: application of process optimization. Process Saf Environ Prot 85(5):419–429. doi:10.1205/psep07022

    Article  Google Scholar 

  • Dunnett AJ, Adjiman CS, Shah N (2008) A spatially explicit whole-system model of the lignocellulosic bioethanol supply chain: an assessment of decentralised processing potential. Biotechnol Biofuels 1(1):1–17. doi: 10.1186/1754-6834-1-13

  • EIA (2012) Refiners’/Gas Plant Operators’ Monthly Petroleum Product Sales Report. Energy Information Administration, Washington

    Google Scholar 

  • EIA (2013a) Annual Energy Outlook 2013. U.S. Energy Information Administration, Washington

    Google Scholar 

  • EIA (2013b) U.S. Imports by Country of Origin. U.S. Energy Information Administration, Washington

    Google Scholar 

  • Ekşioğlu SD, Acharya A, Leightley LE, Arora S (2009) Analyzing the design and management of biomass-to-biorefinery supply chain. Comput Ind Eng 57(4):1342–1352. doi:10.1016/j.cie.2009.07.003

    Article  Google Scholar 

  • EPA (2014) Renewable fuels: regulations & standards. Office of transportation and air quality (OTAQ) 2014 [cited 02/24/2014 2014]. http://www.epa.gov/otaq/fuels/renewablefuels/regulations.htm

  • Faulkner WH (2012) Economic modeling & optimization of a region specific multi-feedstock biorefinery supply chain. Manufacturing Systems Engineering, University of Kentucky, Lexington

    Google Scholar 

  • Fenton N, Neil M (2007) Managing risk in the modern world: application of Bayesian Networks

    Google Scholar 

  • Fernández E, Salomone E, Chiotti O (2010) Model based on Bayesian Networks for monitoring events in a supply chain. In: Vallespir B, Alix T (ed) Advances in production management systems. New Challenges, New Approaches, pp 358–365. Springer, Berlin Heidelberg

    Google Scholar 

  • Gebreslassie BH, Yao Y, You F (2012) Design under uncertainty of hydrocarbon biorefinery supply chains: Multiobjective stochastic programming models, decomposition algorithm, and a Comparison between CVaR and downside risk. AIChE J 58(7):2155–2179. doi:10.1002/aic.13844

    Article  Google Scholar 

  • Gold S, Seuring S (2011) Supply chain and logistics issues of bio-energy production. J Clean Prod 19(1):32–42. doi:10.1016/j.jclepro.2010.08.009

    Article  Google Scholar 

  • Humbird D, Davis R, Tao L, Kinchin C, Hsu D, Aden A (2011) Process design and economics for biochemical conversion of lignocellulosic biomass to ethanol

    Google Scholar 

  • Hytönen E, Stuart PR (2010) Biofuel production in an integrated forest biorefinery & technology identification under uncertainty. J Biobased Mater Bioenergy 4(1):58–67. doi:10.1166/jbmb.2010.1066

    Article  Google Scholar 

  • Kelepouris T, Harrison M, McFarlane D (2011) Bayesian supply chain tracking using serial-level information. IEEE Trans Syst Man Cybern Part C Appl Rev 41(5):733–742. doi:10.1109/TSMCC.2010.2093599

    Article  Google Scholar 

  • Kim J, Realff MJ, Lee JH (2011a) Optimal design and global sensitivity analysis of biomass supply chain networks for biofuels under uncertainty. Comput Chem Eng 35(9):1738–1751

    Article  Google Scholar 

  • Kim J, Realff MJ, Lee JH (2011b) Optimal design and global sensitivity analysis of biomass supply chain networks for biofuels under uncertainty. Comput Chem Eng 35(9):1738–1751. doi:10.1016/j.compchemeng.2011.02.008

    Article  Google Scholar 

  • Kumar A, Sokhansanj S (2007) Switchgrass (Panicum vigratum, L.) delivery to a biorefinery using integrated biomass supply analysis and logistics (IBSAL) model. Bioresour Technol 98(5):1033–1044

    Article  Google Scholar 

  • Lockamy A III (2011) Benchmarking supplier risks using Bayesian Networks. Int J Benchmarking 18(3):409–427

    Article  Google Scholar 

  • Mantovani B, Gibson H, Peart RM, Brook RC (1992) A simulation model for analysis of harvesting and transport costs for biomass based on geography, density and plant location. Anal Agric Energy Syst 253–280

    Google Scholar 

  • Marvin WA, Schmidt LD, Daoutidis P (2012) Biorefinery location and technology selection through supply chain optimization. Ind Eng Chem Res 52(9):3192–3208. doi:10.1021/ie3010463

    Article  Google Scholar 

  • Medina-Oliva G, Weber P, Simon C, Iung B (2009) Bayesian Networks applications on dependability, risk analysis and maintenance. Paper read at 2nd IFAC workshop on dependable control of discrete system, DCDS’09

    Google Scholar 

  • Mele FD, Kostin AM, Guillén-Gosálbez G, Jiménez L (2011) Multiobjective model for more sustainable fuel supply chains: a case study of the sugar cane industry in Argentina. Ind Eng Chem Res 50(9):4939–4958. doi:10.1021/ie101400g

    Article  Google Scholar 

  • Min H, Zhou G (2002) Supply chain modeling: past, present and future. Comput Ind Eng 43(1–2):231–249. doi:10.1016/S0360-8352(02)00066-9

    Article  Google Scholar 

  • Mittal A, A Kassim (2007). Bayesian network technologies: applications and graphical models: IGI Global

    Google Scholar 

  • Naik SN, Goud VV, Rout PK, Dalai AK (2010) Production of first and second generation biofuels: a comprehensive review. Renew Sustain Energy Rev 14(2):578–597. doi:10.1016/j.rser.2009.10.003

    Article  Google Scholar 

  • Nikolopoulou A, Ierapetritou MG (2012) Optimal design of sustainable chemical processes and supply chains: A review. Comput Chem Eng 44:94–103. doi:10.1016/j.compchemeng.2012.05.006

    Article  Google Scholar 

  • Sammons NE Jr, Yuan W, Eden MR, Aksoy B, Cullinan HT (2008) Optimal biorefinery product allocation by combining process and economic modeling. Chem Eng Res Des 86(7):800–808. doi:10.1016/j.cherd.2008.03.004

    Article  Google Scholar 

  • Sammons N, Eden M, Yuan W, Cullinan H, Aksoy B (2007) A flexible framework for optimal biorefinery product allocation. Environ Prog 26(4):349–354. doi:10.1002/ep.10227

    Article  Google Scholar 

  • Santibañez-Aguilar, JE, González-Campos JB, Ponce-Ortega JM, Serna-González M, El-Halwagi MM (2013) Optimal planning and site selection for distributed multiproduct biorefineries involving economic, environmental and social objectives. J Clean Prod (0). doi: 10.1016/j.jclepro.2013.08.004

  • Sharma P, Sarker BR, Romagnoli JA (2011) A decision support tool for strategic planning of sustainable biorefineries. Comput Chem Eng 35(9):1767–1781

    Article  Google Scholar 

  • Sokhansanj S, Kumar A, Turhollow AF (2006) Development and implementation of integrated biomass supply analysis and logistics model (IBSAL). Biomass Bioenergy 30(10):838–847. doi:10.1016/j.biombioe.2006.04.004

    Article  Google Scholar 

  • Sukumara S, Amundson J, Faulkner W, Badurdeen F, Seay J (2012) Multidisciplinary approach in developing region specific optimization tool for sustainable biorefining. In: 22nd European symposium on computer aided process engineering, vol 30, pp 157–161. doi: 10.1016/B978-0-444-59519-5.50032-0

  • Sukumara S, Faulkner W, Amundson J, Badurdeen F, Seay J (2013) A multidisciplinary decision support tool for evaluating multiple biorefinery conversion technologies and supply chain performance. Clean Technol Environ Policy 1–18. doi: 10.1007/s10098-013-0703-6

  • Tako AA, Robinson S (2012) The application of discrete event simulation and system dynamics in the logistics and supply chain context. Decis Support Syst 52(4):802–815. doi:10.1016/j.dss.2011.11.015

    Article  Google Scholar 

  • Tang O, Musa SN (2011) Identifying risk issues and research advancements in supply chain risk management. Int J Prod Econ 133(1):25–34. doi:10.1016/j.ijpe.2010.06.013

    Article  Google Scholar 

  • US Census Bureau (2010) Intercensal Population Estimates 1970-2010. http://www.census.gov/popest/data/counties/totals/2011/CO-EST2011-01.html. Accessed 12 Jan 2011

  • Vanany I, Zailani S, Pujawan N (2009) Supply chain risk management: literature review and future research. Int J Inf Syst Supply Chain Manage (IJISSCM) 2(1):16–33. doi: 10.4018/jisscm.2009010102

  • Yen BPC, Zeng B (2010) A hierarchical assessment method using Bayesian network for material risk detection on green supply chain. In: 2010 IEEE international conference 2010 paper read at industrial engineering and engineering management (IEEM)

    Google Scholar 

  • You F, Tao L, Graziano DJ, Snyder SW (2012) Optimal design of sustainable cellulosic biofuel supply chains: Multiobjective optimization coupled with life cycle assessment and input–output analysis. AIChE J 58(4):1157–1180. doi:10.1002/aic.12637

    Article  Google Scholar 

  • Young D, Scharp R, Cabezas H (2000) The waste reduction (WAR) algorithm: environmental impacts, energy consumption, and engineering economics. Waste Manage 20(8):605–615. doi:10.1016/s0956-053x(00)00047-7

    Article  Google Scholar 

  • Yue D, You F, Snyder SW (2014) Biomass-to-bioenergy and biofuel supply chain optimization: overview, key issues and challenges. Comput Chem Eng (0). doi: http://dx.doi.org/10.1016/j.compchemeng.2013.11.016

  • Zamboni A, Bezzo F, Shah N (2009a) Spatially explicit static model for the strategic design of future bioethanol production systems. 2. Multi-Objective Environmental Optimization. Energy Fuels 23(10):5134–5143. doi:10.1021/ef9004779

    Article  Google Scholar 

  • Zamboni A, Shah N, Bezzo F (2009b) Spatially explicit static model for the strategic design of future bioethanol production systems. 1. Cost minimization. Energy Fuels 23(10):5121–5133. doi:10.1021/ef900456w

    Article  Google Scholar 

  • Zhang F, Johnson DM, Johnson MA (2012) Development of a simulation model of biomass supply chain for biofuel production. Renew Energy 44(0):380–391. doi:10.1016/j.renene.2012.02.006

  • Zhao B, Li J, Zhang Y (2010) Research on the reward and punishment feedback contract based on asymmetric information under the “Company + Farmer” model. In ICLEM 2010:871–879

    Google Scholar 

  • Zwart RWR, Boerrigter H, van der Drift A (2006) The impact of biomass pretreatment on the feasibility of overseas biomass conversion to Fischer-Tropsch products. Energy Fuels 20(5):2192–2197. doi:10.1021/ef060089f

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Authors and Affiliations

  1. Department of Mechanical Engineering, University of Kentucky, Lexington, KY, USA

    Joseph Amundson & Fazleena Badurdeen

  2. Department of Chemical and Materials Engineering, University of Kentucky, Lexington, KY, USA

    Sumesh Sukumara & Jeffrey Seay

  3. Institute of Sustainable Manufacturing, University of Kentucky, Lexington, KY, USA

    Fazleena Badurdeen

Authors
  1. Joseph Amundson
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  2. Sumesh Sukumara
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  3. Jeffrey Seay
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  4. Fazleena Badurdeen
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Corresponding author

Correspondence to Joseph Amundson .

Editor information

Editors and Affiliations

  1. Department of Industrial Engineering, Clemson University, Clemson, South Carolina, USA

    Sandra D. Eksioglu

  2. Division of Economics and Business Engineering Hall, Colorado School of Mines, Golden, Colorado, USA

    Steffen Rebennack

  3. Industrial & Systems Engineering, University of Florida, Gainesville, Florida, USA

    Panos M. Pardalos

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Amundson, J., Sukumara, S., Seay, J., Badurdeen, F. (2015). Decision Support Models for Integrated Design of Bioenergy Supply Chains. In: Eksioglu, S., Rebennack, S., Pardalos, P. (eds) Handbook of Bioenergy. Energy Systems. Springer, Cham. https://doi.org/10.1007/978-3-319-20092-7_7

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  • DOI: https://doi.org/10.1007/978-3-319-20092-7_7

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