Abstract
This chapter describes how to optimise energy systems considering their economic and environmental performance simultaneously. To this end, we follow an approach that combines life cycle assessment with multi-objective optimisation. We illustrate how to apply such a framework to the strategic design and planning of biofuel supply chains. The problem is formulated in mathematical terms as a multi-objective mixed-integer linear programme. The aim of the design/planning task is to maximise the net present value while the environmental impact is minimised simultaneously. Eco-indicator 99 is the life cycle assessment methodology incorporated in the model to quantify the environmental damage. The implementation of the algorithm in a case study based on the Argentine industry reveals the conflictive trade-off between economic and environmental objectives. The proposed framework provides valuable insight into the incidence of key operational features in the optimal biofuel supply chain network.
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References
Abo-Hamad, W. & Arisha, a M.R., 2011. Simulation-Optimisation Methods in Supply Chain Applications: A Review. Irish Journal of Management, 30(2), pp.95–124. Available at: http://search.ebscohost.com/login.aspx?direct=true&db=buh&AN=65271281&site=ehost-live.
Almansoori, A. & Shah, N., 2006. Design and Operation of a Future Hydrogen Supply Chain. Chemical Engineering Research and Design, 84(6), pp.423–438. Available at: http://linkinghub.elsevier.com/retrieve/pii/S0263876206729185.
Azapagic, a. & Clift, R., 1999. Life cycle assessment and multiobjective optimisation. Journal of Cleaner Production, 7(2), pp.135–143.
Beamon, B.M., 1998. Supply Chain Design and Analysis : Models and Methods. International Journal of Production Economics, 55(1), pp.1–22.
Brandenburg, M. et al., 2014. Quantitative models for sustainable supply chain management: Developments and directions. European Journal of Operational Research, 233(2), pp.299–312. Available at: http://dx.doi.org/10.1016/j.ejor.2013.09.032.
Corsano, G., Vecchietti, A.R. & Montagna, J.M., 2011. Optimal design for sustainable bioethanol supply chain considering detailed plant performance model. Computers & Chemical Engineering, 35(8), pp.1384–1398. Available at: http://linkinghub.elsevier.com/retrieve/pii/S009813541100010X.
Dehghanian, F. & Mansour, S., 2009. Designing sustainable recovery network of end-of-life products using genetic algorithm. Resources, Conservation and Recycling, 53(10), pp.559–570. Available at: http://linkinghub.elsevier.com/retrieve/pii/S0921344909000664.
Dreyer, L.C., Niemann, A.L. & Hauschild, M.Z., 2003. Comparison of Three Different LCIA Methods: EDIP97, CML2001 and Eco-indicator 99. The International Journal of Life Cycle Assessment, 8(4), pp.191–200.
Elia A., J. et al., 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. Computers and Chemical Engineering, 35(8), pp.1399–1430. Available at: http://dx.doi.org/10.1016/j.compchemeng.2011.01.019.
Eskandarpour, M. et al., 2015. Sustainable supply chain network design: an optimization-oriented review. Omega, 54, pp.11–32. Available at: http://www.sciencedirect.com/science/article/pii/S0305048315000080.
Giarola, S., Zamboni, A. & Bezzo, F., 2012. Environmentally conscious capacity planning and technology selection for bioethanol supply chains. Renewable Energy, 43, pp. 61–72. Available at: http://linkinghub.elsevier.com/retrieve/pii/S0960148111006811.
Goedkoop, M. & Spriensma, R., 2001. The Eco-indicator 99 - A damage oriented method for Life Cycle Impact Assessment. Assessment, p.144.
Guillén-Gosálbez, G. et al., 2006. Addressing the design of chemical supply chains under demand uncertainty. Computer Aided Chemical Engineering, 21(C), pp.1095–1100.
Hugo, A. et al., 2005. Hydrogen infrastructure strategic planning using multi-objective optimization. International Journal of Hydrogen Energy, 30(15), pp.1523–1534. Available at: http://linkinghub.elsevier.com/retrieve/pii/S0360319905001163.
Kim, J. et al., 2011. Design of biomass processing network for biofuel production using an MILP model. Biomass and bioenergy, 35(2), pp.853–871.
Kostin, A. et al., 2012. Identifying Key Life Cycle Assessment Metrics in the Multiobjective Design of Bioethanol Supply Chains Using a Rigorous Mixed-Integer Linear Programming Approach. Industrial & Engineering Chemistry Research, 51(14), pp.5282–5291. Available at: http://pubs.acs.org/doi/abs/10.1021/ie2027074.
Luo, X. et al., 2013. Operational planning optimization of steam power plants considering equipment failure in petrochemical complex. Applied Energy, 112(1), pp.1247–1264. Available at: http://dx.doi.org/10.1016/j.apenergy.2012.12.039.
Meixell, M.J. & Gargeya, V.B., 2005. Global supply chain design: A literature review and critique. Transportation Research Part E: Logistics and Transportation Review, 41(6 SPEC. ISS.), pp.531–550.
Mele, F., Espuna, A. & Puigjaner, L., 2005. Environmental impact considerations into supply chain management based on life-cycle assessment. In Innovation by life cycle management LCM 2005 international conference.
Mele, F.D., Guillén-Gosálbez, G. & Jiménez, L., 2009. Optimal Planning of Supply Chains for Bioethanol and Sugar Production with Economic and Environmental Concerns., (October 2015), pp.997–1002. Available at: http://linkinghub.elsevier.com/retrieve/pii/S157079460970166X.
Mele, F.D. et al., 2011. Multiobjective model for more sustainable fuel supply chains. A case study of the sugar cane industry in argentina. Industrial and Engineering Chemistry Research, 50(9), pp.4939–4958.
Michelsen, O., Fet, A.M. & Dahlsrud, A., 2006. Eco-efficiency in extended supply chains: A case study of furniture production. Journal of Environmental Management, 79(3), pp.290–297.
Neiro, S.M.S. & Pinto, J.M., 2004. A general modeling framework for the operational planning of petroleum supply chains. Computers & Chemical Engineering, 28(6–7), pp.871–896. Available at: http://linkinghub.elsevier.com/retrieve/pii/S0098135403002308.
Nikolopoulou, A. & Ierapetritou, M.G., 2012. Optimal design of sustainable chemical processes and supply chains: A review. Computers and Chemical Engineering, 44, pp.94–103. Available at: http://dx.doi.org/10.1016/j.compchemeng.2012.05.006.
Papageorgiou, L.G., 2009. Supply chain optimisation for the process industries: Advances and opportunities. Computers and Chemical Engineering, 33(12), pp.1931–1938.
Pieragostini, C., Mussati, M.C. & Aguirre, P., 2012. On process optimization considering LCA methodology. Journal of Environmental Management, 96(1), pp.43–54. Available at: http://linkinghub.elsevier.com/retrieve/pii/S0301479711003951.
Pinto-Varela, T., Barbosa-Póvoa, A.P.F.D. & Novais, A.Q., 2011. Bi-objective optimization approach to the design and planning of supply chains: Economic versus environmental performances. Computers & Chemical Engineering, 35(8), pp.1454–1468. Available at: http://linkinghub.elsevier.com/retrieve/pii/S0098135411000998.
Ruiz-Femenia, R. et al., 2013. Multi-objective optimization of environmentally conscious chemical supply chains under demand uncertainty. Chemical Engineering Science, 95, pp.1–11.
Sabio, N. et al., 2014. Multiobjective optimization under uncertainty of the economic and life-cycle environmental performance of industrial processes. AIChE Journal, 60(6), pp.2098–2121. Available at: http://onlinelibrary.wiley.com/doi/10.1002/aic.14385/abstract\nhttp://onlinelibrary.wiley.com/doi/10.1002/aic.14385/abstract;jsessionid=962ADE4225E69F85F3AAF92FA31078CE.f04t03?systemMessage=Wiley+Online+Library+will+be+disrupted+on+the+18th+October+from+1.
Sarkis, J., Zhu, Q. & Lai, K.H., 2011. An organizational theoretic review of green supply chain management literature. International Journal of Production Economics, 130(1), pp.1–15. Available at: http://dx.doi.org/10.1016/j.ijpe.2010.11.010.
Seuring, S., 2013. A review of modeling approaches for sustainable supply chain management. Decision Support Systems, 54(4), pp.1513–1520. Available at: http://linkinghub.elsevier.com/retrieve/pii/S0167923612001741.
Stern, M.O., Ayres, R.U. & Saxton, J.C., 1973. Tax Strategies for Industrial Pollution Abatement. IEEE Transactions on Systems, Man, and Cybernetics, 3(6), pp.588–603. Available at: http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=4309312.
You, F. et al., 2012. Optimal design of sustainable cellulosic biofuel supply chains: multiobjective optimization coupled with life cycle assessment and input–output analysis. AIChE Journal, 58(4), pp.1157–1180.
Yue, D. et al., 2014. Sustainable design and operation of cellulosic bioelectricity supply chain networks with life cycle economic, environmental, and social optimization. Industrial & Engineering Chemistry Research, 53(10), pp.4008–4029.
Zamboni, A., Shah, N. & Bezzo, F., 2009. Spatially Explicit Static Model for the Strategic Design of Future Bioethanol Production Systems. 1. Cost Minimization. Energy & fuels, 23(10), pp.5121–5133. Available at: http://dx.doi.org/10.1021/ef900456w\nhttp://pubs.acs.org/doi/pdfplus/10.1021/ef900456w.
Zhou, Z., Cheng, S. & Hua, B., 2000. Supply chain optimization of continuous process industries with sustainability considerations. Computers & Chemical Engineering, 24(2–7), pp.1151–1158.
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Páez, M.A., Mele, F.D., Guillén-Gosálbez, G. (2016). Multi-objective Optimisation Incorporating Life Cycle Assessment. A Case Study of Biofuels Supply Chain Design. In: Martín, M. (eds) Alternative Energy Sources and Technologies. Springer, Cham. https://doi.org/10.1007/978-3-319-28752-2_17
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DOI: https://doi.org/10.1007/978-3-319-28752-2_17
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