Single- and multi-objective optimization of a plate-fin heat exchanger with offset strip fins adopting the genetic algorithm. (August 2019)

• Record Type: Journal Article
• Authors: Song, Rui; Cui, Mengmeng
• Is Part Of: Applied thermal engineering. Volume 159(2019)
• Publication Date: 2019-08
• Subjects: Plate-fin heat exchanger -- Genetic algorithm -- Thermodynamic performance -- Economic performance -- Optimization; Heat engineering -- Periodicals; Heating -- Equipment and supplies -- Periodicals; Periodicals; 621.40205
• DOI: 10.1016/j.applthermaleng.2019.113881
• Languages: English
• ISSNs: 1359-4311

Abstract

Highlights: Single- and multi-objective optimizations of PFHE by GA are studied. Objective functions based on thermodynamics and economic analysis are selected. Comparative analysis of single- and multi-objective functions is conducted. Abstract: Based on the new correlation for heat transfer and flow friction characteristics of the offset strip fins in our previous work, the single- and multi-objective optimizations of plate-fin heat exchanger by genetic algorithm are studied in this paper. Different objective functions of effectiveness, modified entropy number, and total annual cost are selected based on the first law of thermodynamics, the second law of thermodynamics and economic analysis. Comparative analysis of single- and multi-objective functions is conducted. The target with the maximum of the effectiveness is equivalent to the minimum of the entropy number generated by heat transfer. The poor thermodynamic performance of the total annual cost is mainly determined by the large proportion of operating costs in the total cost, the optimization process of which is focus on the minimum of the entropy number generated by pressure drop. The modified entropy generation number coordinates the comprehensive performance of the heat exchanger in both economic and thermodynamic aspects. The effective compromise of the single targets is achieved by the non-dominated sorting genetic algorithm with elite strategy (NSGA-II). The multi-objective optimizations are flexible in providing multiple solutions to the selection of practical production.