Application of genetic algorithms for process integration and optimization considering environmental factors

A systematic methodology for pollution prevention based on process integration is presented in this report. In this methodology, process simulation was carried out to provide mass and energy information of the chemical process. An artificial neural network (ANN) was used to replace rigorous process simulation in the optimization process to improve computational efficiency. Multiobjective optimization was performed to obtain coordinate optimization of process performance for both economics and environmental aspects. A multiobjective genetic algorithm was used to solve the multiobjective optimization problems. Mass and energy use were considered simultaneously in this program. A case study of a wastewater recovery system in ammonia production process is discussed to illustrate the effectiveness of this pollution prevention methodology. © 2004 American Institute of Chemical Engineers Environ Prog, 2004