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

The 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...

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Main Authors: Yousefi, Moslem, Darus, Amer Nordin, Yousefi, Milad, Hooshyar, Danial
Format: Article
Published: Elsevier Ltd 2015
Subjects:
TJ Mechanical engineering and machinery
Online Access:http://eprints.utm.my/id/eprint/58615/
http://dx.doi.org/10.1016/j.applthermaleng.2015.03.011
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spelling my.utm.586152021-12-15T07:45:32Z http://eprints.utm.my/id/eprint/58615/ Multi-stage thermal-economical optimization of compact heat exchangers: a new evolutionary-based design approach for real-world problems Yousefi, Moslem Darus, Amer Nordin Yousefi, Milad Hooshyar, Danial TJ Mechanical engineering and machinery The 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). Elsevier Ltd 2015-05-25 Article PeerReviewed Yousefi, Moslem and Darus, Amer Nordin and Yousefi, Milad and Hooshyar, Danial (2015) Multi-stage thermal-economical optimization of compact heat exchangers: a new evolutionary-based design approach for real-world problems. Applied Thermal Engineering, 83 . pp. 71-80. ISSN 1359-4311 http://dx.doi.org/10.1016/j.applthermaleng.2015.03.011 DOI:10.1016/j.applthermaleng.2015.03.011
institution Universiti Teknologi Malaysia
building UTM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Teknologi Malaysia
content_source UTM Institutional Repository
url_provider http://eprints.utm.my/
topic TJ Mechanical engineering and machinery
spellingShingle TJ Mechanical engineering and machinery
Yousefi, Moslem
Darus, Amer Nordin
Yousefi, Milad
Hooshyar, Danial
Multi-stage thermal-economical optimization of compact heat exchangers: a new evolutionary-based design approach for real-world problems
description The 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).
format Article
author Yousefi, Moslem
Darus, Amer Nordin
Yousefi, Milad
Hooshyar, Danial
author_facet Yousefi, Moslem
Darus, Amer Nordin
Yousefi, Milad
Hooshyar, Danial
author_sort Yousefi, Moslem
title Multi-stage thermal-economical optimization of compact heat exchangers: a new evolutionary-based design approach for real-world problems
title_short Multi-stage thermal-economical optimization of compact heat exchangers: a new evolutionary-based design approach for real-world problems
title_full Multi-stage thermal-economical optimization of compact heat exchangers: a new evolutionary-based design approach for real-world problems
title_fullStr Multi-stage thermal-economical optimization of compact heat exchangers: a new evolutionary-based design approach for real-world problems
title_full_unstemmed Multi-stage thermal-economical optimization of compact heat exchangers: a new evolutionary-based design approach for real-world problems
title_sort multi-stage thermal-economical optimization of compact heat exchangers: a new evolutionary-based design approach for real-world problems
publisher Elsevier Ltd
publishDate 2015
url http://eprints.utm.my/id/eprint/58615/
http://dx.doi.org/10.1016/j.applthermaleng.2015.03.011
_version_ 1720436881950244864
score 13.211869

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