Cost and Entropy Generation Minimization of a Cross-Flow Plate Fin Heat Exchanger Using Multi-Objective Genetic Algorithm

Abstract

In the present work, a thermal modeling is conducted for optimal design of compact heat exchangers in order to minimize cost and entropy generation. In this regard, an ε−NTU method is applied for estimation of the heat exchanger pressure drop, as well as effectiveness. Fin pitch, fin height, fin offset length, cold stream flow length, no-flow length, and hot stream flow length are considered as six decision variables. Fast and elitist nondominated sorting genetic algorithm (i.e., nondominated sorting genetic algorithm II) is applied to minimize the entropy generation units and the total annual cost (sum of initial investment and operating and maintenance costs) simultaneously. The results for Pareto-optimal front clearly reveal the conflict between two objective functions, the number of entropy generation units and the total annual cost. It reveals that any geometrical changes, which decrease the number of entropy generation units, lead to an increase in the total annual cost and vice versa. Moreover, for prediction of the optimal design of the plate fin heat exchanger, an equation for the number of entropy generation units versus the total annual cost is derived for the Pareto curve. In addition, optimization of heat exchangers based on considering exergy destruction revealed that irreversibilities, such as pressure drop and high temperature difference between cold and hot streams, play a key issue in exergy destruction. Thus, more efficient heat exchanger leads to have a heat exchanger with higher total cost rate. Finally, the sensitivity analysis of change in the optimum number of entropy generation units and the total annual cost with change in the decision variables of the plate fin heat exchanger is also performed, and the results are reported.

A robust learning based evolutionary approach for thermal-economic optimization of compact heat exchangers

“…Various objectives, such as minimizing weight/volume, minimizing the number of entropy generation units and minimizing capital and operational costs, have been considered in different studies on PFHEs. To overcome the difficulties of this multi-faceted design process, various studies have proposed different strategies ranging from traditional mathematical formulations [4][5][6][7][8] to artificial neural networks [9] and evolutionary methods [3,[10][11][12][13][14][15][16][17][18][19][20][21].…”

Optimization of heat exchangers of refrigeration machines by entropy generation minimization method

“…Авторы работы [17] исследовали производство энтропии потоком жидкости в микро-каналах открытой теплопередающей поверхности. В работе [18] представлена математическая модель производства энтропии в пластинчатом теплообмен-нике с перекрестными потоками. Таким образом, при общем подходе к оптимизации в каждой работе рас-смотрена конкретная узкоспециализированная кон-струкция теплообменного аппарата.…”

A comparative study on the shell and tube and gasket-plate heat exchangers: The economic viewpoint

“…, condenser [4], plate fin [5][6][7], fin tube [8][9], rotary regenerator [10] as well as gasket plate [11] by using different algorithms including the Genetic Algorithm [1][2][3][4][4][5][6][7][8][9][10][11], Particle Swarm Algorithm [4], Firefly Algorithm [3] and by considering the different objective functions including total annual cost [1-6, 8-9,11], effectiveness [2-3, 5, 7-8, 10-11], pressure drop [7,10], exergy efficiency [1,6], entropy generation [6] and temperature approach [9]. Shetna et al studied the production of optimum thermal generation networks in terms of temperature distribution and cost [12].…”