Robust fuzzy and multi-objective optimization approaches to generate alternate solutions for resource conservation of eco-industrial park involving various future events

Abstract

Eco-industrial parks (EIPs), typically including industrial plants and companies, provide a promising resource conservation strategy that includes opportunities for material exchange between participating plants and minimizes the consumption of resources. However, when the participating plants in the EIP fail to share data, various possible future events are ought to happen in future. In this paper, we present various robust optimization approaches in order to generate alternate solutions for resource conservation in EIPs through water reuse networks that take various future events into consideration. Three different optimization approaches are discussed and solved in this study. In the first optimization approach, the objective function is set to minimize the overall emergy of the EIP. In the second approach, an emergy-based fuzzy optimization model is presented and the objective function of the model is to maximize the overall degree of satisfaction. Finally, a solution method incorporating the lexicographic optimization and augmented ɛ-constraint method is proposed in order to solve the multi-objective optimization (MOO) problem, which simultaneously minimizes the overall freshwater consumption and the regenerated water flow-rate. The resulting model is formulated as a mixed-integer linear programming problem taking into consideration two scenarios (EIP reuse network with a regenerator and direct reuse EIP network without a regenerator). The applicability of the proposed optimization approaches is demonstrated by comparison with a literature case study and by comparing the optimal resource conservation results for the two scenarios. The results show that a regeneration unit with a fixed outlet concentration into the base system causes a 15.6%, 9.8%, 21.2%, and 16.2% reduction in the overall emergy, TAC, freshwater, and wastewater, respectively, in comparison to that of direct reuse.