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

Heat exchangers are usually designed using a sophisticated process of trial-and-error to find proper values of unknown parameters which satisfy given requirements. Recently, the design of heat exchangers using evolutionary optimization algorithms has received attention. The major aim of the present...

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Bibliographic Details
Published in:Mathematics (Basel) Vol. 10; no. 14; p. 2527
Main Authors: Moon, Joo Hyun, Lee, Kyun Ho, Kim, Haedong, Han, Dong In
Format: Journal Article
Language:English
Published: Basel MDPI AG 01.07.2022
Subjects:
Cross flow
Design optimization
Economics
Efficiency
Evolutionary algorithms
Fitness
Heat exchangers
Heuristic
improved Gaussian quantum-behaved particle swarm optimization
Mathematics
modified local attractor
Optimization algorithms
Optimization techniques
Particle swarm optimization
plate–fin heat exchanger
Random numbers
Systems design
thermal-economic optimization
Velocity
ISSN:2227-7390, 2227-7390
Online Access:Get full text
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Summary:Heat exchangers are usually designed using a sophisticated process of trial-and-error to find proper values of unknown parameters which satisfy given requirements. Recently, the design of heat exchangers using evolutionary optimization algorithms has received attention. The major aim of the present study is to propose an improved Gaussian quantum-behaved particle swarm optimization (GQPSO) algorithm for enhanced optimization performance and its verification through application to a multivariable thermal-economic optimization problem of a crossflow plate–fin heat exchanger (PFHE). Three single objective functions: the number of entropy generation units (NEGUs), total annual cost (TAC), and heat exchanger surface area (A), were minimized separately by evaluating optimal values of seven unknown variables using four different PSO-based methods. By comparing the obtained best fitness values, the improved GQPSO approach could search quickly for better global optimal solutions by preventing particles from falling to the local minimum due to its modified local attractor scheme based on the Gaussian distributed random numbers. For example, the proposed GQPSO could predict further improved best fitness values of 40% for NEGUs, 17% for TAC, and 4.5% for A, respectively. Consequently, the present study suggests that the improved GQPSO approach with the modified local attractor scheme can be efficient in rapidly finding more suitable solutions for optimizing the thermal-economic problem of the crossflow PFHE.
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ISSN:2227-7390
2227-7390
DOI:10.3390/math10142527