Economic Optimization of A Shellandtube Heat Exchanger (Sthe) Based on New Method By Grasshopper Optimization Algorithm (Goa)

Today, minimizing the cost of heat exchangers (hes) is a major goal for the designer. in this study, a fast and reliable method is used to simulate, optimize design parameters and evaluate heat transfer enhancement and the economic optimization of sthe. taking into account the importance of sthes in industrial applications and the complexity in their geometry, the goa methodology is adopted to obtain an optimal geometric configuration. the goa is a metaheuristics search algorithm based on the mimics and simulates grasshoppers ’behavior in nature and the grasshoppers ’ move to food sources. the total annual cost (tac) (including the capital investment cost and the total operating cost consumption to overcome the pressure drop) is chosen as the objective function, and the design variables include number of tubes, number of tube passes, length of tubes, the arrangement of tubes, size and percentage of baffle cut, tube diameter, tubular step ratio have been considered. the developed algorithm is applied to two case studies and the results are compared with the original design and other optimization methods available in literature such as genetic algorithm (ga), hybrid geneticparticle swarm optimization (gapso), gravitational search algorithm (gsa), falcon optimization algorithm (foa), artificial bee colony (abc), bio geography based optimization (bbo), cuckoo search algorithm (csa) and firefly algorithm (ffa). in order to investigate the feasibility of the proposed method, two case studies have been presented that show a significant tac reduction of up to 30% with respect to traditional designed sthes.

Economic optimization of a shellandtube heat exchanger (STHE) based on new method by Grasshopper Optimization Algorithm (GOA)

Today, minimizing the cost of heat exchangers (HEs) is a major goal for the designer. In this study, a fast and reliable method is used to simulate, optimize design parameters and evaluate heat transfer enhancement and the economic optimization of STHE. Taking into account the importance of STHEs in industrial applications and the complexity in their geometry, the GOA methodology is adopted to obtain an optimal geometric configuration. The GOA is a metaheuristics search algorithm based on the mimics and simulates grasshoppers ’behavior in nature and the grasshoppers ’ move to food sources. The total annual cost (TAC) (including the capital investment cost and the total operating cost consumption to overcome the pressure drop) is chosen as the objective function, and the design variables include number of tubes, number of tube passes, length of tubes, the arrangement of tubes, size and percentage of baffle cut, tube diameter, tubular step ratio have been considered. The developed algorithm is applied to two case studies and the results are compared with the original design and other optimization methods available in literature such as Genetic Algorithm (GA), Hybrid GeneticParticle Swarm Optimization (GAPSO), Gravitational Search Algorithm (GSA), Falcon Optimization Algorithm (FOA), Artificial Bee Colony (ABC), Bio geography Based Optimization (BBO), Cuckoo Search Algorithm (CSA) and Firefly Algorithm (FFA). In order to investigate the feasibility of the proposed method, two case studies have been presented that show a significant TAC reduction of up to 30% with respect to traditional designed STHEs.