Heat exchanger networks using hybrid meta-heuristic approach

Abstract

Meta-heuristic approaches have been used to achieve good solutions in the heat exchanger network (HEN) synthesis task. Several meta-heuristic approaches have been proposed in the literature. Two of the most important techniques are Simulated Annealing (SA) and Particle Swarm Optimization (PSO). In general, SA is able to provide good solutions, but with large computational efforts. PSO is faster than SA in finding good solutions, but it is not capable of handling discrete variables properly. In the present work, a bi-level HEN synthesis approach is presented. SA is used to a single heat exchanger addition, along with group optimizations to improve PSO performance. A parallel processing technique is also presented in order to improve local searching performance. The method was tested in 3 literature case studies and results were compared to literature solutions. The solutions presented have lower Total Annual Costs (TAC) when compared to other HEN. The proposed method is able to present near-optimal solutions by more efficiently exploring the search space and using simple moves for local searches.