A Hybrid Metaheuristic–Deterministic Optimization Strategy for Waste Heat Recovery in Industrial Plants

Abstract

This work presents a novel approach to recover industrial waste heat and to integrate it into utilities, refrigeration, and electricity production through the incorporation of heat exchanger networks and thermal engines (steam Rankine cycle, organic Rankine cycle, and absorption refrigeration cycle). The solution approach is based on the iteration between metaheuristic–deterministic optimization strategies. Metaheuristic optimization is established through the MS Excel-VBA-Aspen Plus link to obtain accurate modeling results. Deterministic optimization is implemented in the GAMS platform, where the mathematical formulation is based on a superstructure that considers all of the energy interconnections between the heat exchanger network, utilities, and thermal engines. Furthermore, economic, environmental, and social targets are evaluated. A case study is presented to show the applicability of the proposed methodology. The operating conditions obtained are presented (working fluid flow rate, temperatures, pressures, and efficiencies) for each thermal engine. Furthermore, the results show an increase in the total annual profit by 148%.