Parameter optimization of passive heat supply tower of ground source heat pump based on NSGA-II

• Published in: Solar energy Vol. 190; pp. 453 - 464
• Main Authors: Song, Yanli, Zou, Mingyin, Chen, Xin, Deng, Junyu, Du, Tao
• Format: Journal Article
• Language: English
• Published: New York Elsevier Ltd 15-09-2019; Pergamon Press Inc
• Subjects: Air flow; Algorithms; Analytic hierarchy process; Classification; Decision making; Energy consumption; Flow rates; Flow velocity; Genetic algorithms; Ground source heat pump; Heat exchangers; Heat pumps; Heat transfer; Mathematical models; Multi-objective optimization; Multiple objective analysis; Non-dominated solution; NSGA-II; Objective function; Optimization; Parameter estimation; Power consumption; Solar energy; Sorting algorithms; Three-time fit; Water flow; NSGA-II; Multi-objective optimization; Non-dominated solution; Three-time fit; Ground source heat pump; Analytic hierarchy process
• ISSN: 0038-092X; 1471-1257
• DOI: 10.1016/j.solener.2019.08.043

•The passive heat supply tower can solve the phenomenon of soil cold accumulation.•Five parameters of the passive heat supply tower are studied.•The NSGA-II method is used to optimize parameters of the passive heat supply tower.•The heat and mass exchange in the tower is independent of the mass flow of water.•Determined that water ratio of the passive heat supply tower is 0.48.
A model that could ensure the lowest energy consumption and optimize all possible performance parameters simultaneously was proposed based on experiments that run the ground source heat pump passive heat supply tower at given conditions. By studying the relationship between the range, approach, tower characteristic ratio, effectiveness, air condensation rate of the passive heat supply tower and water-air ratio, a multi-objective optimization model and its objective function are proposed. With the target of reducing power consumption to the greatest extent, the unconstrained optimization of all objective functions was performed using the Elite Non-dominated Sorting Genetic Algorithm (NSGA-II), in which five performance parameters were adopted to estimate the air flow via decision making at a given water flow rate. The results show that the heat transfer is independent of the water flow. An optimal water-air ratio of 0.48 is determined according to comprehensive analysis of the parameter weights. Under constant water flow rate, the optimal air flow rate and water flow rate is 108.5 g/s and 47.2 g/s, respectively, as observed by comparing the Decision Matrix Score (DMS) value. According to the multi-objective optimization method and the NSGA-II algorithm, optimizations of the performance of supplementary tower are more effective and reasonable as the Decision Making Matrix (DMM) can reasonably allocate parameters expected by users to occupy a larger weight.