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Artificial intelligence techniques in refrigeration system modelling and optimization: A multi-disciplinary review

Refrigeration systems currently utilize 17 of total electric energy, and this consumption is expected to increase by more than 30 by 2050. Moreover, these systems significantly contribute to global warming (10) and environmental degradation. Optimization of these systems can overcome these critical...

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Main Authors: Ahmed, R., Mahadzir, S., Erniza B Rozali, N., Biswas, K., Matovu, F., Ahmed, K.
Format: Article
Published: Elsevier Ltd 2021
Online Access:https://www.scopus.com/inward/record.uri?eid=2-s2.0-85111218702&doi=10.1016%2fj.seta.2021.101488&partnerID=40&md5=5372ff54d3e22e78a2e058aeed2efcd6
http://eprints.utp.edu.my/30375/
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spelling my.utp.eprints.303752022-03-31T11:51:55Z Artificial intelligence techniques in refrigeration system modelling and optimization: A multi-disciplinary review Ahmed, R. Mahadzir, S. Erniza B Rozali, N. Biswas, K. Matovu, F. Ahmed, K. Refrigeration systems currently utilize 17 of total electric energy, and this consumption is expected to increase by more than 30 by 2050. Moreover, these systems significantly contribute to global warming (10) and environmental degradation. Optimization of these systems can overcome these critical issues, increase thermal efficiency, and decrease the total cost. Refrigeration system optimization problems are complex, multi-modal, non-linear, and time-consuming. Both classic and non-classic (computational intelligence (CI)) methods are successfully applied to overcome these challenges. This comprehensive review presents state-of-the-art theory and application of the most widely used CI techniques such as GA, PSO, SA, DE, HTS, CRO, MOGA, and NSGA II in the optimization of various refrigeration systems. The properties of these algorithms, their computational efficiency, robustness, and applications are highlighted most. Additionally, the authors discuss several surrogate modelling techniques and their applications in refrigeration systems, including ANN, RSM, and regression analysis. According to trend analysis, the major cost function to optimize is the COP, followed by total cost, exergetic efficiency, energy consumption, and cooling capacity. Subsequently, the application of CI approaches has increased dramatically in the optimization of refrigeration systems, where GA and its variants are typically used, and authors are more interested in multi-objective optimization. © 2021 Elsevier Ltd Elsevier Ltd 2021 Article NonPeerReviewed https://www.scopus.com/inward/record.uri?eid=2-s2.0-85111218702&doi=10.1016%2fj.seta.2021.101488&partnerID=40&md5=5372ff54d3e22e78a2e058aeed2efcd6 Ahmed, R. and Mahadzir, S. and Erniza B Rozali, N. and Biswas, K. and Matovu, F. and Ahmed, K. (2021) Artificial intelligence techniques in refrigeration system modelling and optimization: A multi-disciplinary review. Sustainable Energy Technologies and Assessments, 47 . http://eprints.utp.edu.my/30375/
institution Universiti Teknologi Petronas
building UTP Resource Centre
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Teknologi Petronas
content_source UTP Institutional Repository
url_provider http://eprints.utp.edu.my/
description Refrigeration systems currently utilize 17 of total electric energy, and this consumption is expected to increase by more than 30 by 2050. Moreover, these systems significantly contribute to global warming (10) and environmental degradation. Optimization of these systems can overcome these critical issues, increase thermal efficiency, and decrease the total cost. Refrigeration system optimization problems are complex, multi-modal, non-linear, and time-consuming. Both classic and non-classic (computational intelligence (CI)) methods are successfully applied to overcome these challenges. This comprehensive review presents state-of-the-art theory and application of the most widely used CI techniques such as GA, PSO, SA, DE, HTS, CRO, MOGA, and NSGA II in the optimization of various refrigeration systems. The properties of these algorithms, their computational efficiency, robustness, and applications are highlighted most. Additionally, the authors discuss several surrogate modelling techniques and their applications in refrigeration systems, including ANN, RSM, and regression analysis. According to trend analysis, the major cost function to optimize is the COP, followed by total cost, exergetic efficiency, energy consumption, and cooling capacity. Subsequently, the application of CI approaches has increased dramatically in the optimization of refrigeration systems, where GA and its variants are typically used, and authors are more interested in multi-objective optimization. © 2021 Elsevier Ltd
format Article
author Ahmed, R.
Mahadzir, S.
Erniza B Rozali, N.
Biswas, K.
Matovu, F.
Ahmed, K.
spellingShingle Ahmed, R.
Mahadzir, S.
Erniza B Rozali, N.
Biswas, K.
Matovu, F.
Ahmed, K.
Artificial intelligence techniques in refrigeration system modelling and optimization: A multi-disciplinary review
author_facet Ahmed, R.
Mahadzir, S.
Erniza B Rozali, N.
Biswas, K.
Matovu, F.
Ahmed, K.
author_sort Ahmed, R.
title Artificial intelligence techniques in refrigeration system modelling and optimization: A multi-disciplinary review
title_short Artificial intelligence techniques in refrigeration system modelling and optimization: A multi-disciplinary review
title_full Artificial intelligence techniques in refrigeration system modelling and optimization: A multi-disciplinary review
title_fullStr Artificial intelligence techniques in refrigeration system modelling and optimization: A multi-disciplinary review
title_full_unstemmed Artificial intelligence techniques in refrigeration system modelling and optimization: A multi-disciplinary review
title_sort artificial intelligence techniques in refrigeration system modelling and optimization: a multi-disciplinary review
publisher Elsevier Ltd
publishDate 2021
url https://www.scopus.com/inward/record.uri?eid=2-s2.0-85111218702&doi=10.1016%2fj.seta.2021.101488&partnerID=40&md5=5372ff54d3e22e78a2e058aeed2efcd6
http://eprints.utp.edu.my/30375/
_version_ 1738657098490707968
score 13.211869

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