Improved Heat Exchanger Network Synthesis without Stream Splits Based on Comprehensive Learning Particle Swarm Optimizer

Abstract

In this paper, an improved heat exchanger network (HEN) synthesis method based on the comprehensive learning particle swarm optimizer algorithm (CLPSO) is proposed to synthesize HENs without stream splits. Compared with the standard particle swarm algorithm, CLPSO employs a novel learning strategy that preserves the diversity of the swarm to discourage premature convergence. However, while the algorithm's global exploration capability is enhanced, the local search capability decreases and the convergence speed becomes slow. In addition, the solution quality of CLPSO is largely determined by the randomly generated particles' best previous position (pbest) during initialization. Hence, the solution may be unstable due to different pbest. For the abovementioned considerations, this paper proposes a new HEN initialization and renovation method to improve the quality of pbest, reduce the initial cost, and retain the obtained optimization results as much as possible in the optimization process to speed up the convergence of the algorithm. Four typical cases are simulated to verify the effectiveness of the proposed method. This method only needs a single-level optimization algorithm to obtain high-quality solutions, which will give it a bright prospect in research and application.