Effectiveness of evolutionary algorithms for optimization of heat exchangers

Khosravi, Rihanna, Khosravi, Abbas, Nahavandi, Saeid and Hajabdollahi, Hassan 2015, Effectiveness of evolutionary algorithms for optimization of heat exchangers, Energy conversion and management, vol. 89, pp. 281-288, doi: 10.1016/j.enconman.2014.09.039.

Attached Files
Name Description MIMEType Size Downloads

Title Effectiveness of evolutionary algorithms for optimization of heat exchangers
Author(s) Khosravi, Rihanna
Khosravi, AbbasORCID iD for Khosravi, Abbas orcid.org/0000-0001-6927-0744
Nahavandi, SaeidORCID iD for Nahavandi, Saeid orcid.org/0000-0002-0360-5270
Hajabdollahi, Hassan
Journal name Energy conversion and management
Volume number 89
Start page 281
End page 288
Total pages 8
Publisher Elsevier
Place of publication Amsterdam, The Netherlands
Publication date 2015-01-01
ISSN 0196-8904
Keyword(s) Cuckoo search
Firefly algorithm
Genetic algorithm
Heat exchanger
Science & Technology
Physical Sciences
Energy & Fuels
Physics, Nuclear
Summary This paper comprehensively investigates performance of evolutionary algorithms for design optimization of shell and tube heat exchangers (STHX). Genetic algorithm (GA), firefly algorithm (FA), and cuckoo search (CS) method are implemented for finding the optimal values for seven key design variables of the STHX model. ε-NTU method and Bell-Delaware procedure are used for thermal modeling of STHX and calculation of shell side heat transfer coefficient and pressure drop. The purpose of STHX optimization is to maximize its thermal efficiency. Obtained results for several simulation optimizations indicate that GA is unable to find permissible and optimal solutions in the majority of cases. In contrast, design variables found by FA and CS always lead to maximum STHX efficiency. Also computational requirements of CS method are significantly less than FA method. As per optimization results, maximum efficiency (83.8%) can be achieved using several design configurations. However, these designs are bearing different dollar costs. Also it is found that the behavior of the majority of decision variables remains consistent in different runs of the FA and CS optimization processes.
Language eng
DOI 10.1016/j.enconman.2014.09.039
Field of Research 080108 Neural, Evolutionary and Fuzzy Computation
Socio Economic Objective 970103 Expanding Knowledge in the Chemical Sciences
HERDC Research category C1 Refereed article in a scholarly journal
ERA Research output type C Journal article
Copyright notice ©2014, Elsevier
Persistent URL http://hdl.handle.net/10536/DRO/DU:30075810

Connect to link resolver
Unless expressly stated otherwise, the copyright for items in DRO is owned by the author, with all rights reserved.

Version Filter Type
Citation counts: TR Web of Science Citation Count  Cited 32 times in TR Web of Science
Scopus Citation Count Cited 38 times in Scopus
Google Scholar Search Google Scholar
Access Statistics: 562 Abstract Views, 2 File Downloads  -  Detailed Statistics
Created: Wed, 26 Aug 2015, 09:09:01 EST

Every reasonable effort has been made to ensure that permission has been obtained for items included in DRO. If you believe that your rights have been infringed by this repository, please contact drosupport@deakin.edu.au.