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Modeling and optimization of biomass quality variability for decision support systems in biomass supply chains. (English) Zbl 1496.90042

Summary: A feasible alternative to the production of fossil fuels is the production of biofuels. In order to minimize the costs of producing biofuels, we developed a stochastic programming formulation that optimizes the inbound delivery of biomass. The proposed model captures the variability in the moisture and ash content in the biomass, which define its quality and affect the cost of biofuel. We propose a novel hub-and-spoke network to take advantage of the economies of scale in transportation and to minimize the effect of poor quality. The first-stage variables are the potential locations of depots and biorefineries, and the necessary unit trains to transport the biomass. The second-stage variables are the flow of biomass between the network nodes and the third-party bioethanol supply. A case study from Texas is presented. The numerical results show that the biomass quality changes the selected depot/biorefinery locations and conversion technology in the optimal network design. The cost due to poor biomass quality accounts for approximately 8.31% of the investment and operational cost. Our proposed L-shaped with connectivity constraints approach outperforms the benchmark L-shaped method in terms of solution quality and computational effort by 0.6% and 91.63% on average, respectively.

MSC:

90C15 Stochastic programming
90C90 Applications of mathematical programming

Software:

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References:

[1] Aguayo, M. M., Sarin, S. C., & Cundiff, J. S. (2019). A branch-and-price approach for a biomass feedstock logistics supply chain design problem. In IISE transactions (pp. 1-17).
[2] Atashbar, NZ; Labadie, N.; Prins, C., Modeling and optimization of biomass supply chains: A review and a critical look, IFAC-PapersOnLine, 49, 12, 604-615 (2016) · doi:10.1016/j.ifacol.2016.07.742
[3] Birge, JR; Louveaux, F., Introduction to stochastic programming (2011), Berlin: Springer, Berlin · Zbl 1223.90001 · doi:10.1007/978-1-4614-0237-4
[4] Brownsort, PA, Biomass pyrolysis processes: Review of scope, control and variability, 38 (2009), Edinburgh: UK Biochar Research Center, Edinburgh
[5] Casler, M.; Boe, A., Cultivar \(\times\) environment interactions in switchgrass, Crop Science, 43, 6, 2226-2233 (2003) · doi:10.2135/cropsci2003.2226
[6] Castillo-Villar, KK; Eksioglu, SD; Taherkhorsandi, M., Integrating biomass quality variability in stochastic supply chain modeling and optimization for large-scale biofuel production, Journal of Cleaner Production, 149, 904-918 (2017) · doi:10.1016/j.jclepro.2017.02.123
[7] Center of Transportation Analysis. (2017). Railroad network. Retrieved May 2, 2017 form,http://cta.ornl.gov/transnet/RailRoads.html. Accessed 5 Feb 2017.
[8] Centers for Disease Control and Prevention. (2016). NLDAS daily precipitation. Retrieved February 1, 2019 form, https://wonder.cdc.gov/NASA-precipitation.html. Accessed 2 Jan 2019.
[9] Chen, CW; Fan, Y., Bioethanol supply chain system planning under supply and demand uncertainties, Transportation Research Part E: Logistics and Transportation Review, 48, 1, 150-164 (2012) · doi:10.1016/j.tre.2011.08.004
[10] Cobuloglu, HI; Büyüktahtakın, İE, A two-stage stochastic mixedinteger programming approach to the competition of biofuel and food production, Computers & Industrial Engineering, 107, 251-263 (2017) · doi:10.1016/j.cie.2017.02.017
[11] Cobuloglu, H. I., & Büyüktahtakin, I. E. (2014). A review of lignocellulosic biomass and biofuel supply chain models. In Proceedings of IIE annual conference (p. 4013). Institute of Industrial and Systems Engineers (IISE).
[12] Davis, R., et al. (2013). Process design and economics for the conversion of lignocellulosic biomass to hydrocarbons: dilute-acid and enzymatic deconstruction of biomass to sugars and biological conversion of sugars to hydrocarbons. Technical report. National Renewable Energy Laboratory (NREL), Golden, CO.
[13] Ekşioğlu, SD, Analyzing the design and management of biomass-to-biorefinery supply chain, Computers & Industrial Engineering, 57, 4, 1342-1352 (2009) · doi:10.1016/j.cie.2009.07.003
[14] Farahani, RZ, Hub location problems: A review of models, classification, solution techniques, and applications, Computers & Industrial Engineering, 64, 4, 1096-1109 (2013) · doi:10.1016/j.cie.2013.01.012
[15] Huang, Y.; Chen, CW; Fan, Y., Multistage optimization of the supply chains of biofuels, Transportation Research Part E: Logistics and Transportation Review, 46, 6, 820-830 (2010) · doi:10.1016/j.tre.2010.03.002
[16] Jacobson, J. J., et al. (2014). Biomass feedstock supply system design and analysis. Technical report. Idaho National Laboratory (INL), Idaho Falls, ID (US).
[17] Jones, S. B., et al. (2013). Process design and economics for the conversion of lignocellulosic biomass to hydrocarbon fuels: Fast pyrolysis and hydrotreating bio-oil pathway. Technical report. Pacific Northwest National Laboratory (PNNL), Richland, WA, USA.
[18] Kall, P.; Wallace, SW, Stochastic programming (1994), Berlin: Springer, Berlin · Zbl 0812.90122
[19] Kenney, KL, Understanding biomass feedstock variability, Biofuels, 4, 1, 111-127 (2013) · doi:10.4155/bfs.12.83
[20] Khatib, H., IEA World Energy Outlook 2011—A comment, Energy policy, 48, 737-743 (2012) · doi:10.1016/j.enpol.2012.06.007
[21] Kim, J.; Realff, MJ; Lee, JH, Optimal design and global sensitivity analysis of biomass supply chain networks for biofuels under uncertainty, Computers & Chemical Engineering, 35, 9, 1738-1751 (2011) · doi:10.1016/j.compchemeng.2011.02.008
[22] Lamers, P., Techno-economic analysis of decentralized biomass processing depots, Bioresource Technology, 194, 205-213 (2015) · doi:10.1016/j.biortech.2015.07.009
[23] Langholtz, M. H., et al. (2016). 2016 Billion-ton report: Advancing domestic resources for a thriving bioeconomy, volume 1: Economic availability of feedstocks. Technical report. Oak Ridge National Laboratory (ORNL), Oak Ridge, TN, USA.
[24] Leão, RRDCC; Hamacher, S.; Oliveira, F., Optimization of biodiesel supply chains based on small farmers: A case study in Brazil, Bioresource Technology, 102, 19, 8958-8963 (2011) · doi:10.1016/j.biortech.2011.07.002
[25] Leduc, S., Optimal location of wood gasification plants for methanol production with heat recovery, International Journal of Energy Research, 32, 12, 1080-1091 (2008) · doi:10.1002/er.1446
[26] Marufuzzaman, M.; Eksioglu, SD; Huang, YE, Two-stage stochastic programming supply chain model for biodiesel production via wastewater treatment, Computers & Operations Research, 49, 1-17 (2014) · Zbl 1349.90110 · doi:10.1016/j.cor.2014.03.010
[27] Memişoğlu, G.; Üster, H., Integrated bioenergy supply chain network planning problem, Transportation Science, 50, 1, 35-56 (2015) · doi:10.1287/trsc.2015.0598
[28] Miles, T. R., et al. (1995). Alkali deposits found in biomass power plants: A preliminary investigation of their extent and nature. Volume 1. Technical report. National Renewable Energy Lab., Golden, CO, USA; Miles (Thomas R.), Portland, OR, USA; Sandia National Labs., Livermore, CA, USA; Foster Wheeler Development Corp., Livingston, NJ, USA; California University, Davis, CA, USA; Bureau of Mines, Albany, OR, USA. Albany Research Center.
[29] National Renewable Energy Laboratory. (2017). https://maps.nrel.gov/bioenergyatlas. Accessed 17 March 2017.
[30] Project-OSRM. (2017). Project-OSRM/osrm-backend. Retrieved March 14, 2017 from, https://github.com/Project-OSRM/osrm-backend.
[31] R Core Team. (2017). The R project for statistical computing. Retrieved April 10, 2017 form, https://www.r-project.org/.
[32] Renewable Fuels Association. (2016). Retrieved April 12, 2017 from, http://www.ethanolrfa.org/pages/annual-industry-outlook.
[33] Roni, M. S., Eksioglu, S. D., & Cafferty, K. G. (2014). A multi-objective, huband-spoke supply chain design model for densified biomass. In IIE annual conference. Proceedings (p. 643). Institute of Industrial and Systems Engineers (IISE).
[34] Roni, MS, Analyzing the impact of a hub and spoke supply chain design for long-haul, high-volume transportation of densified biomass (2013), Starkville: Mississippi State University, Starkville
[35] Roni, M. S., Thompson, D. N., et al. (2018). Herbaceous feedstock 2018 state of technology report. Technical report. Idaho National Laboratory.
[36] Sultana, A.; Kumar, A., Optimal configuration and combination of multiple lignocellulosic biomass feedstocks delivery to a biorefinery, Bioresource Technology, 102, 21, 9947-9956 (2011) · doi:10.1016/j.biortech.2011.07.119
[37] Tunc, H., Hub-based network design: A review, International Journal of Networking, 1, 2, 17-24 (2011)
[38] U.S. (2007). Energy independence and security act of 2007. US Government Printing Office.
[39] U.S. Census Bureau. (2012). U.S. Gazetteer: 2010, 2000, and 1990. Retrieved March 04, 2017 from, https://www.census.gov/geo/maps-data/ddata/gazetteer.html.
[40] U.S. Department of Energy. (2017). Retrieved July 23, 2016, from https://bioenergykdf.net/billionton2016/overview.
[41] U.S. Energy Information Administration. (2016). Independent statistics and analysis. Retrieved July 25, 2016 from, http://www.eia.gov/totalenergy/data/monthly.
[42] Üster, H.; Memişoğlu, G., Biomass logistics network design under price-based supply and yield uncertainty, Transportation Science, 52, 2, 474-492 (2017) · doi:10.1287/trsc.2017.0766
[43] Yu, TE, Influence of particle size and packaging on storage dry matter losses for switchgrass, Biomass and Bioenergy, 73, 135-144 (2015) · doi:10.1016/j.biombioe.2014.12.009
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