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Multi-stage thermal-economical optimization of compact heat exchangers: A new evolutionary-based design approach for real-world problems

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dc.citedby23
dc.contributor.authorYousefi M.en_US
dc.contributor.authorDarus A.N.en_US
dc.contributor.authorYousefi M.en_US
dc.contributor.authorHooshyar D.en_US
dc.contributor.authorid53985756300en_US
dc.contributor.authorid56584823100en_US
dc.contributor.authorid55247052200en_US
dc.contributor.authorid56572940600en_US
dc.date.accessioned2023-05-29T06:00:16Z
dc.date.available2023-05-29T06:00:16Z
dc.date.issued2015
dc.descriptionAlgorithms; Constraint theory; Design; Entropy; Fins (heat exchange); Genetic algorithms; Heat exchangers; Particle swarm optimization (PSO); Thermodynamics; Compact heat exchanger; Engineering applications; Entropy generation minimization; Evolutionary algorithms (EAs); Inequality constraint; Multi stage; Plate-fin heat exchanger; Variable operating condition; Evolutionary algorithmsen_US
dc.description.abstractThe complicated task of design optimization of compact heat exchangers (CHEs) have been effectively performed by using evolutionary algorithms (EAs) in the recent years. However, mainly due to difficulties of handling extra variables, the design approach has been based on constant rates of heat duty in the available literature. In this paper, a new design strategy is presented where variable operating conditions, which better represent real-world problems, are considered. The proposed strategy is illustrated using a case study for design of a plate-fin heat exchanger though it can be employed for all types of heat exchangers without much change. Learning automata based particle swarm optimization (LAPSO), is employed for handling nine design variables while satisfying various equality and inequality constraints. For handling the constraints, a novel feasibility based ranking strategy (FBRS) is introduced. The numerical results indicate that the design based on variable heat duties yields in more cost savings and superior thermodynamics efficiency comparing to a conventional design approach. Furthermore, the proposed algorithm has shown a superior performance in finding the near-optimum solution for this task when it is compared to the most popular evolutionary algorithms in engineering applications, i.e. genetic algorithm (GA) and particle swarm optimization (PSO). � 2015 Elsevier Ltd. All rights reserved.en_US
dc.description.natureFinalen_US
dc.identifier.doi10.1016/j.applthermaleng.2015.03.011
dc.identifier.epage80
dc.identifier.scopus2-s2.0-84926034565
dc.identifier.spage71
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-84926034565&doi=10.1016%2fj.applthermaleng.2015.03.011&partnerID=40&md5=b719b6edb7d6e082560fd3e13c7f794e
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/22332
dc.identifier.volume83
dc.publisherElsevier Ltden_US
dc.sourceScopus
dc.sourcetitleApplied Thermal Engineering
dc.titleMulti-stage thermal-economical optimization of compact heat exchangers: A new evolutionary-based design approach for real-world problemsen_US
dc.typeArticleen_US
dspace.entity.typePublication
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