Optimization of biofuel production from corn stover under supply uncertainty in Ontario

Ranisau, Jonathan; Ogbe, Emmanuel; Traino, Aaron; Barbouti, Mohammed; Elsholkami, Mohamed; Elkamel, Ali; Fowler, Michael

doi:10.18331/BRJ2017.4.4.4

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	Optimization of biofuel production from corn stover under supply uncertainty in Ontario
Article 4, Volume 4, Issue 4, Autumn 2017, Page 721-729 PDF (8.33 MB)
Document Type: Research Paper
DOI: 10.18331/BRJ2017.4.4.4
Authors
Jonathan Ranisau¹; Emmanuel Ogbe¹; Aaron Traino¹; Mohammed Barbouti¹; Mohamed Elsholkami¹; Ali Elkamel¹^{, 2}; Michael Fowler ¹
¹Department of Chemical Engineering, University of Waterloo, Waterloo, Canada.
²Department of Chemical Engineering, Khalifa University, The Petroleum Institute, Abu Dhabi, UAE.
Abstract
In this paper, a biofuel production supply chain optimization framework is developed that can supply the fuel demand for 10% of Ontario. Different biomass conversion technologies are considered, such as pyrolysis and gasification and subsequent hydro processing and the Fischer-Tropsch process. A supply chain network approach is used for the modeling, which enables the optimization of both the biorefinery locations and the associated transportation networks. Gasification of corn stover is examined to convert waste biomass into valuable fuel. Biomass-derived fuel has several advantages over traditional fuels including substantial greenhouse gas reduction, generating higher quality synthetic fuels, providing a use for biomass waste, and potential for use without much change to existing infrastructure. The objective of this work is to show the feasibility of the use of corn stover as a biomass feedstock to a hydrocarbon biofuel supply chain in Ontario using a mixed-integer linear programming model while accounting for the uncertainty in the availability of corn stover. In the case study, the exact number of biorefineries is left as a policy decision and the optimization is carried out over a range of the possible numbers of facilities. The results obtained from the case study suggests implementing gasification technology followed by Fischer-Tropsch at two different sites in Ontario. The optimal solution satisfied 10% of the yearly fuel demand of Ontario with two production plants (14.8 billion L of fuel) and requires an investment of $42.9 billion, with a payback period of about 3 years.
Graphical Abstract

Highlights
Examined gasification of corn stover as means to convert waste biomass into fuel. Biorefinery siting locations selected in Ontario counties. MILP model accounts for corn stover supply uncertainty with stochastic programming. At least two biorefinery facilities are required to meet all province demands.
Keywords
Biofuel; Supply chain optimization; Biomass to biofuels; Two-stage stochastic programming; Uncertainty


References
[1] Acevedo, J., Pistikopoulos, E.N., 1998. Stochastic optimization based algorithms for process synthesis under uncertainty. Comput. Chem. Eng. 22(4-5), 647-671. [2] AFDC, Alternative Fuels Data Center, 2017. Renewable hydrocarbon biofuels. United States Department of Energy. [3] 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. Sust. Energy. 30(4), 720-732. [4] 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 J. 59(12), 4655-4672. [5] Andersen, F., Iturmendi, F., Espinosa, S., Diaz, M.S., 2012. Optimal design and planning of biodiesel supply chain with land competition. Comput. Chem. Eng. 47, 170-182. [6] Anex, R.P., Aden, A., Kazi, F.K., Fortman, J., Swanson, R.M., Wright, M.M., Satrio, J.A., Brown, R.C., Daugaard, D.E., Platon, A., Kothandaraman, G., 2010. Techno-economic comparison of biomass-to-transportation fuels via pyrolysis, gasification, and biochemical pathways. Fuel. 89, S29-S35. [7] Bain, R.L., 1992. Material and energy balances for methanol from biomass using biomass gasifiers. National Renew. Energy Lab(NREL). 136. [8] Birge, J.R., Louveaux, F., 2011. Introduction to Stochastic Programming. New York, Springer. [9] CNW, Canada News Wire, 2016. Government of Canada announces pan-Canadian pricing on carbon pollution. [10] Čuček, L., Lam, H.L., Klemeš, J.J., Varbanov, P.S., Kravanja, Z., 2010. Synthesis of regional networks for the supply of energy and bioproducts. Clean. Technol. Environ. Policy. 12(6), 635-645. [11] Demirbaş, A., 2001. Biomass resource facilities and biomass conversion processing for fuels and chemicals. Energy Convers. Manage. 42(11), 1357-1378. [12] Demirbas, A., 2011. Competitive liquid biofuels from biomass. Appl. Energy. 88(1), 17-28. [13] Diwekar, U.M., Rubin, E.S., 1991. Stochastic modeling of chemical processes. Comput. Chem. Eng. 15(2), 105-114. [14] D'Jesús, P., Boukis, N., Kraushaar-Czarnetzki, B., Dinjus, E., 2006. Gasification of corn and clover grass in supercritical water. Fuel. 85(7-8), 1032-1038. [15] Dutta, A., Phillips, S.D., 2009. Thermochemical ethanol via direct gasification and mixed alcohol synthesis of lignocellulosic biomass. National Renew. Energy Lab(NREL). 144. [16] Ebadian, M., 2015. Demonstration of corn stover harvest in Canada’s outdoor farm show. BioFuelNet Canada. [17] FAO, Food and Agriculture Organization of the United Nations, 1986. Gasification Fuels. [18] Focus on Geography Series; Province of Ontario, 2011. Canada Statistics. [19] Gebreslassie, B.H., 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. [20] Google Maps APIs. [21] Grisi, E.F., Yusta, J.M., Khodr, H.M., 2011. A short-term scheduling for the optimal operation of Biorefineries. Energy Convers. Manag. 52(1), 447-456. [22] Hillier, F.S., Lieberman, G.J., 2000. Introduction to Operations Research. Boston, MA: McGraw-Hill, Inc. [23] Kim, J., Realff, M.J., Lee, J.H., 2011. Optimal design and global sensitivity analysis of biomass supply chain networks for biofuels under uncertainty. Comput. Chem. Eng. 35(9), 1738-1751. [24] Kim, J., Realff, M.J., Lee, J.H., Whittaker, C., Furtner, L., 2011. Design of biomass processing network for biofuel production using an MILP model. Biomass Bioenergy. 35(2), 853-871. [25] Larson, E.D., Jin, H., Celik, F.E., 2009. Large-scale gasification-based coproduction of fuels and electricity from switchgrass Eric. Biofuels, Bioprod. Biorefin. 3(2), 174-194. [26] Leibbrandt, N.H., Knoetze, J.H., Görgens, J.F., 2011. Comparing biological and thermochemical processing of sugarcane bagasse: an energy balance perspective. Biomass Bioenergy. 35(5), 2117-2126. [27] Marie-Andrée, H., Dorff, E., 2015. Corn: Canada’s third most valuable crop. Statistics Canada. [28] McLean, K., Li, X., 2013. Robust Scenario Formulations for Strategic Supply Chain Optimization under Uncertainty. Ind. Eng. Chem. Res. 52(16), 5721-5734. [29] McLean, K., Ogbe, E., Li, X., 2015. Novel formulation and efficient solution strategy for strategic optimization of an industrial chemical supply chain under demand uncertainty. Can. J. Chem. Eng. 93, 971-985. [30] Mochizuki, T., Chen, S.Y., Toba, M., Yoshimura, Y., 2014. Deoxygenation of guaiacol and woody tar over reduced catalysts. Appl. Catal. B. 146, 237-243. [31] Natural Resources Canada, 2016. Bioenergy from biomass. [32] NEB, National Energy Board (NEB), 2016. Canada’s Energy Future, 2016. Energy supply and demand projections to 2040. [33] NETL, National Energy Technology Laboratory. Advantages and efficiency of gasification. [34] Ogbe, E., Li, X., 2017. A new cross decomposition method for stochastic mixed-integer linear programming. Eur. J. Oper. Res. 256(2), 487-499. [35] Provincial Field Crop Production and Prices, 2017. Ontario ministry of agriculture, food and rural affairs. [36] Pham, V., El‐Halwagi, M., 2012. Process synthesis and optimization of biorefinery configurations. AIChE J. 58(4), 1212-1221. [37] Rezaiyan, J., Cheremisinoff, N.P., 2005. Gasification technologies: a primer for engineers and scientists. CRC Press. [38] Sammons, N.E., Yuan, W., Eden, M.R., Aksoy, B., Cullinan, H.T., 2008. Optimal biorefinery product allocation by combining process and economic modeling. Chem. Eng. Res. Des. 86(7), 800-808. [39] Simchi-Levi, D., Kaminsky, P., Simchi-Levi, E., 1999. Managing the Supply Chains: Concepts, Strategies and Cases. New York, McGraw-Hill, Inc. [40] Solomon, S., Qin, D., Manning, M., Chen, Z., Marquis, M., Averyt, K., Tignor, M.M.H.L., Miller, H., 2007. The physical science basis. [41] Swanson, R.M., Platon, A., Satrio, J.A., Brown, R.C., 2010. Techno-economic analysis of biomass-to-liquids production based on gasification. Fuel. 89, S11-S19. [42] Tijmensen, M.J., Faaij, A.P., Hamelinck, C.N., van Hardeveld, M.R., 2002. Exploration of the possibilities for production of Fischer Tropsch liquids and power via biomass gasification. Biomass Bioenergy. 23(2), 129-152. [43] USEIA, United States Energy Information Administration, 2017a. Oil and the environment. [44] USEIA, United States Energy Information Administration, 2017b. Biomass renewable energy from plants and animals. [45] Van Bibber, L., Shuster, E., Haslbeck, J., Rutkowski, M., Olsen, S., Kramer, S., 2007. Baseline technical and economic assessment of a commercial scale fischer-tropsch liquids facility. US Dept.Energy. Rep. [46] Wan, S., Wang, Y., 2014. A review on ex situ catalytic fast pyrolysis of biomass. Front. Chem. Sci. Eng. 8(3), 280-294. [47] Zamboni, A., Bezzo, F., Shah, N., 2009. Spatially explicit static model for the strategic design of future bioethanol production systems. 2. multi-objective environmental optimization. Energy Fuels. 23(10), 5134-5143. [48] Zhou, C.H., Xia, X., Lin, C.X., Tong, D.S., Beltramini, J., 2011. Catalytic conversion of lignocellulosic biomass to fine chemicals and fuels. Chem. Soc. Rev. 40(11), 5588-5617. [49] Yue, D., You, F., Snyder, S.W., 2014. Biomass-to-bioenergy and biofuel supply chain optimization: overview, key issues and challenges. Comput. Chem. Eng. 66, 36-56.
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