Mass exchange network synthesis using genetic algorithms

Mass Exchange Networks (MENs) are used in the chemical industry to reduce t he waste generated by a plant to an acceptable level at the cheapest cost. Finding the optimal network, however, is often difficult due to the non-con vexity of the mathematical representation of the problem. This paper descri bes a novel approach for the synthesis of MENs and MENs with regeneration u sing Genetic Algorithms (GA), a stochastic optimisation technique based on the concepts of natural evolution. We present an encoding for a genetic alg orithm which describes a rich search space, considering both stream splitti ng and in-series exchangers. For a certain class of problems, all encoded s olutions are feasible and require a simple evaluation to yield a cost, resu lting in an efficient genetic algorithm. For other problems, the number of infeasible solutions is small, having little effect on the convergence of t he genetic algorithm. In comparison with other methods, the GA presented he rein is able to find better networks than have been reported elsewhere. (C) 1998 Elsevier Science Ltd. All rights reserved.