281701 Multi-Objective Optimization of a Biorefinery's Supply Network

Thursday, November 1, 2012: 4:55 PM
325 (Convention Center )
Lidija Čuček1, Mariano Martin2,3, Ignacio E. Grossmann4 and Zdravko Kravanja1, (1)University of Maribor, Faculty of Chemistry and Chemical Engineering, Maribor, Slovenia, (2)Department of Chemical Engineering, University of Salamanca, Spain, (3)Carnegie Mellon University, Department of Chemical Engineering, Pittsburgh, PA, (4)Department of Chemical Engineering, Carnegie Mellon University, Pittsburgh, PA


Biomass is one of the renewable energy sources having increasing importance (Rentizelas et al., 2009), and from which heat, electricity and biofuels can be generated. Nowadays, the main concerns include the increasing shortages of energy and water resources, increasing prices, the demand and dependency on fossil fuels, as well as growing environmental concerns, global climate change being, in general, the greatest environmental threat, have led to the consideration of alternative and renewable energy sources. Developing clean and renewable energy resources ranks as one of the greatest challenges facing mankind over the medium- to long-term (Mata et al., 2011). In the short-term, in the case of the transportation sector, only biofuels from biomass provide an alternative that can be implemented because of its high density, compatibility with current automobile engines, and existing fuels' distribution infrastructures. Thus, bioethanol and biodiesel have become the more promising alternatives (Martín and Grossmann, 2012).

However, the competiveness of biomass as an energy source faces some challenges, and strongly depends on the biomass supply chain (Yu et al., 2009). Biomass is usually seasonally and locally available, has low energy density and high moisture content, degrades during storage, and requires extensive infrastructure for harvesting, transportation, storage, and processing (Lam et al., 2010). The first generation of biofuels, based on starch, sugar and oil crops brought about an ethical trade-off, since the raw material could also be used for food, resulting in an increase in food prices and, eventually, supply risks. Consequently, a second generation has been proposed as a solution, which is still at an early stage of development, not to mention the so-called third generation. Therefore, for the biofuel industry to be competitive with petroleum-based fuels, as well as providing further benefits, a more profitable, better integrated and more sustainable biorefinery supply network design is crucial for attracting investment in the production of more sustainable biofuels. 

This contribution presents a synthesis of regional renewable biomass and bioenergy supply chains, based on a Mixed-Integer Linear Programming (MILP) approach. This method addresses the main challenges presented by biomass and water resources, the distributive and varied availabilities regarding location and time. The production processes from different sources of biomass include first, second, and third generations of biofuels such as bioethanol, biodiesel, hydrogen, and FT-diesel (Martín and Grossmann, 2012). The aim was to maximize the economically viable utilization of resources, by accounting for the competition between fuels and food production. Biorefinery supply networks are typically comprised of four-layer supply network superstructure, including the agricultural (layer 1 – L1), collection and preprocessing (layer 2 – L2), core processing (layer 3 – L3), and usage (layer 4 – L4) layers, including transportation between these layers. A region is divided into zones (Lam et al., 2010) for optimizing conversion operations and transportation flows. An MILP model for the synthesis of regional networks for the supply of energy and bioproducts (Čuček et al., 2010; Lam et al., 2011) was extended in order to be applied within an optimally-integrated biorefinery’s supply network, so that the final products from one process could now become the raw materials for another process; e.g., bioethanol is a product from biochemical, thermochemical or thermo-biochemical processes, and could become a raw material for the production of biodiesel or hydrogen, a product from lignocellulosic materials that may be needed for FT synthesis. The multi-objective optimization of a heat-integrated biorefinery’s supply network was performed, with maximization of the economic performance and minimization of the environmental impact, evaluated by the carbon and energy footprints. The objective of the synthesis was to determine economically-efficient and environmentally-benign solutions using the optimal selection of raw materials, technologies, intermediate, and final product flows.        

Keywords: Biomass, Bioenergy, Biorefinery’s supply network, Multi-objective optimization, Carbon footprint


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 Technologies and Environmental Policy, 12(6), 635-645

Lam H.L., Varbanov P.S., Klemeš J.J., 2010, Optimisation of regional energy supply chains utilising renewables: P-graph approach, Computers & Chemical Engineering, 34, 782-792

Lam H.L., Klemeš J.J., Kravanja Z., 2011, Model-size reduction techniques for large-scale biomass production and supply networks, Energy, 36(8), 4599-4608

Martín M., Grossmann I.E., 2012, Systematic synthesis of sustainable biorefineries: A review, Industrial & Engineering Chemistry Research, submitted for publication

Mata T.M., Martins A.A., Sikdar S.K., Costa C.A.V., 2011, Sustainability considerations of biodiesel based on supply chain analysis, Clean Technologies and Environmental Policy, 13, 655–671

Rentizelas A.A., Tolis A.J., Tatsiopoulos I.P., 2009, Logistic issues of biomass: The storage problem and the multi-biomass supply chain, Renewable and Sustainable Energy Reviews, 13(4), 887-894

Yu Y., Bartle J., Li C.Z., Wu H., 2009, Mallee biomass as a key bioenergy source in Western Australia: Importance of biomass supply chain, Energy Fuels, 26(6), 3290-3299

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See more of this Session: Supply Chain Optimization II
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