Thermal-Economic Optimization of Plate–Fin Heat Exchanger Using Improved Gaussian Quantum-Behaved Particle Swarm Algorithm

• DOI: 10.3390/math10142527
• Publication Date: 2022-07-21T07:34:40Z
• Authors: Joo Hyun Moon; Kyun Ho Lee; Haedong Kim; Dong In Han
• Keywords: plate–fin heat exchanger; thermal-economic optimization; modified local attractor; plate–fin heat exchanger; thermal-economic optimization; improved Gaussian quantum-behaved particle swarm optimization; modified local attractor; improved Gaussian quantum-behaved particle swarm optimization; QA1-939; 0202 electrical engineering, electronic engineering, information engineering; 02 engineering and technology; Mathematics

Heat exchangers are usually designed using a sophisticated process of trial-and-error to find proper values unknown parameters which satisfy given requirements. Recently, the design heat evolutionary optimization algorithms has received attention. The major aim present study is propose an improved Gaussian quantum-behaved particle swarm (GQPSO) algorithm for enhanced performance and its verification through application multivariable thermal-economic problem crossflow plate–fin exchanger (PFHE). Three single objective functions: number entropy generation units (NEGUs), total annual cost (TAC), surface area (A), were minimized separately by evaluating optimal seven variables four different PSO-based methods. By comparing obtained best fitness values, GQPSO approach could search quickly better global solutions preventing particles from falling local minimum due modified attractor scheme based on distributed random numbers. For example, proposed predict further 40% NEGUs, 17% TAC, 4.5% A, respectively. Consequently, suggests that with can be efficient in rapidly finding more suitable optimizing PFHE.