Automated heat exchanger network synthesis by using hybrid natural algorithms and parallel processing

Heat exchanger network (HEN) synthesis can be formulated as an optimization problem, which can be solved by meta-heuristics. These approaches account for a large computational time until convergence. In the present paper the potentialities of applying parallel processing techniques to a non-deterministic approach based on a hybridization between Genetic Algorithms (GA) and Particle Swarm Optimization (PSO) were investigated. Six literature examples were used as benchmarks for the solutions obtained. Comparative experiments were carried out to investigate the time efficiency of the method while implemented using series or parallel processing. The solutions obtained led to lower Total Annual Costs (TAC) than those presented by the literature. As expected, parallel processing usage multiplied the algorithm speed by the number of cores used. Hence, it can be concluded that the proposed method is capable of finding excellent local optimal solutions, and the application of multiprocessing techniques represented a substantial reduction in execution time.