Superstructure Optimization in Heat Exchanger Network (HEN) Synthesis Using Modular Simulators and a Genetic Algorithm Framework

Abstract

Heat exchanger network synthesis (HENS) is one of the most efficient process integration tools to save energy in chemical plants. In this work, a new optimization framework is proposed for the synthesis of HENS, based on a genetic algorithm (GA) coupled with a commercial process simulator through the ActiveX capability of the simulator. The use of GA provides a robust search in complex and nonconvex spaces of mathematical problems, while the use of a simulator facilitates the formulation of rigorous models for different alternatives. To include the most common heat exchanger structures in the model, a promising superstructure has been used. Allowing nonisothermal mixing of streams in the new simulation-based approach leads to the discovery of truly optimal network configurations. The performance of the proposed approach is demonstrated using some case studies, and the obtained solutions are compared with those available in the literature.