Multiobjective optimization for the socio-eco-efficient conversion of lignocellulosic biomass to biofuels and bioproducts
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This work focuses on a way to integrate the social, environmental and economic aspects (socio-eco-efficient aspects) together in the decisions concerning the sustainable process synthesis of a lignocellulosic (Agave bagasse) biorefinery. This challenge is addressed by the formulation and solution of a multiobjective optimization model of a process superstructure for the lignocellulosic biomass conversion into biofuels and products, where social (human toxicity potential), economic (cost), environmental (environmental impact) and socioeconomic (product demand) criteria are included in the formulation of the objective function. The optimization resulted model is a MINLP (Mixed Integer Non-Linear Programing) problem. To solve this problem, a process synthesis methodology is proposed, which includes the epsilon constraint (ε-constraint) method, the use of the GAMS software and a benchmarking using Aspen Plus process simulator to include the energy balance of the different processes. As results, four feasible configurations of biorefineries were obtained, including the best configuration selected after the benchmarking using energy requirements, reduced use of energy after integration and water used as indicators. With the approach proposed in this paper, it was possible to evaluate in a short time a vast number of options included in the superstructure, as well as to select the best option that fulfills the three aspects of sustainability. © 2018, Springer-Verlag GmbH Germany, part of Springer Nature.
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Biorefinery; Lignocellulosic biomass; MINLP; Multiobjective optimization; Process synthesis; Socio-eco-efficient sustainability Benchmarking; Bioconversion; Biofuels; Biomass; Bioproducts; Cellulosic ethanol; Computer software; Environmental impact; Integer programming; Lignocellulosic biomass; Linear programming; Nonlinear programming; Refining; Sustainable development; Biorefineries; Energy requirements; Human toxicity potentials; MINLP; Multi-objective optimization models; Objective functions; Process synthesis; Sustainable process; Multiobjective optimization
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