Optimal heat exchanger network synthesis using particle swarm optimization

Heat exchanger network (HEN) synthesis has been a well-studied subject over the past decades. Many studies and methodologies were proposed to make possible the energy recovery, minimizing the utilities consumption and the number of heat transfer equipment. Most of papers published in this subject are based on Pinch Analysis and mathematical programming. Some recent papers use meta-heuristic techniques like Genetic Algorithms or Simulated Annealing to solve the HEN synthesis problem and good results are found but with large computational effort. In this paper an optimization model for the synthesis of HEN is proposed. The approach is based on the use of Particle Swarm Optimization to determine the HEN that minimizes the total annualized cost, accounting for capital costs of heat exchangers and the energy costs for utilities and pumping duties. The algorithm is based on a superstructure simultaneous optimization model for the HEN synthesis considering stream splitting. Some examples from the literature were used to show the application of the proposed algorithm, and the results confirm the achievement of the optimum HEN configuration with little computational effort.