Heat Exchanger Network Synthesis using a Genetic Algorithms-Particle Swarm Optimization Hybrid Method and Parallel Processing

Abstract

Abstract In Heat Exchanger Network (HEN) synthesis capital savings and pollutant emission reduction can be achieved. The mathematical modeling of the HEN synthesis problem requires elaborated solution strategies given the particularities of their non-linear formulations and non-convex problems. The use of heuristic approach accounts for a large computational load, and hence a high processing time until convergence. In the present paper a hybrid model for HEN synthesis using Genetic Algorithms (GA) and Particle Swarm Optimization (PSO) is presented. The potentialities of applying parallel processing techniques to solve the problem were studied. Two examples from the literature 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 in both cases with the proposed methodology led to Total Annual Costs (TAC) equal or lower to those presented by the literature. As one could expect, parallel processing usage multiplied the algorithm speed by the number of cores used (processing time was close to 75% lower by using 4 processing cores). Hence, it can be concluded that the hybrid algorithm proposed has potential to find near-optimal solutions, and the application of multiprocessing techniques to such non-deterministic approaches represents a substantial reduction in the execution time.