Multi-objective optimization of a plate and frame heat exchanger via genetic algorithm

Abstract

In the present paper, a plate and frame heat exchanger is considered. Multi-objective optimization using genetic algorithm is developed in order to obtain a set of geometric design parameters, which lead to minimum pressure drop and the maximum overall heat transfer coefficient. Vividly, considered objective functions are conflicting and no single solution can satisfy both objectives simultaneously. Multi-objective optimization procedure yields a set of optimal solutions, called Pareto front, each of which is a trade-off between objectives and can be selected by the user, regarding the application and the project’s limits. The presented work takes care of numerous geometric parameters in the presence of logical constraints. A sensitivity analysis is also carried out to study the effects of different geometric parameters on the considered objective functions. Modeling the system and implementing the multi-objective optimization via genetic algorithm has been performed by MATLA

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].…”

Experimental performance investigation of a shell and tube heat exchanger by exergy based sensitivity analysis

“…Second law and entropy generation analysis are also a topic of the researcher for revealing the inefficiencies in heat exchangers [17][18][19]. Some studies focused on analytical investigation of heat exchangers in an dynamic structure with a feed-forward control heuristic [20]. An exergetic analysis conducted for investigating various nanofluids in an helically coiled heat exchanger considering parameters of particle volume concentration, heat exchanger duty parameter, coil to tube diameter ratio and Dean number [21].…”

Thermodynamic and Economic Optimization of Plate Fin Heat Exchangers Using the Bees Algorithm

“…In the case of plate fin heat exchangers, a single-objective GA and PSO (particle swarm optimization) have been used for optimizing plate fin heat exchangers [20][21][22][23][24][25] aiming at minimization of a variety of objectives such as the ratio of the number of heat transfer units (NTU) to the cold side pressure drop, total annual cost, total volume, total weight, and the number of entropy generation units. Moreover, some works aimed at multi-objective optimization [26][27][28][29] used GA.…”