Mostrar el registro sencillo del ítem

dc.contributor.authorAhmed, Rasel
dc.contributor.authorMahadzir, Shuhaimi
dc.contributor.authorMota-Babiloni, Adrián
dc.contributor.authorAl-Amin, Md.
dc.contributor.authorUsmani, Abdullah Y
dc.contributor.authorAshraf Rana, Zaid
dc.contributor.authoryassin, hayati
dc.contributor.authorShaik, Dr. Saboor
dc.contributor.authorHussain, Fayaz
dc.date.accessioned2023-05-02T09:09:10Z
dc.date.available2023-05-02T09:09:10Z
dc.date.issued2023-02-03
dc.identifier.citationAhmed R, Mahadzir S, Mota-Babiloni A, Al-Amin M, Usmani AY, Ashraf Rana Z, et al. (2023) 4E analysis of a two-stage refrigeration system through surrogate models based on response surface methods and hybrid grey wolf optimizer. PLoS ONE 18(2): e0272160.ca_CA
dc.identifier.issn1932-6203
dc.identifier.urihttp://hdl.handle.net/10234/202353
dc.description.abstractRefrigeration systems are complex, non-linear, multi-modal, and multi-dimensional. However, traditional methods are based on a trial and error process to optimize these systems, and a global optimum operating point cannot be guaranteed. Therefore, this work aims to study a two-stage vapor compression refrigeration system (VCRS) through a novel and robust hybrid multi-objective grey wolf optimizer (HMOGWO) algorithm. The system is modeled using response surface methods (RSM) to investigate the impacts of design variables on the set responses. Firstly, the interaction between the system components and their cycle behavior is analyzed by building four surrogate models using RSM. The model fit statistics indicate that they are statistically significant and agree with the design data. Three conflicting scenarios in bi-objective optimization are built focusing on the overall system following the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) and Linear Programming Technique for Multidimensional Analysis of Preference (LINMAP) decision-making methods. The optimal solutions indicate that for the first to third scenarios, the exergetic efficiency (EE) and capital expenditure (CAPEX) are optimized by 33.4% and 7.5%, and the EE and operational expenditure (OPEX) are improved by 27.4% and 19.0%. The EE and global warming potential (GWP) are also optimized by 27.2% and 19.1%, where the proposed HMOGWO outperforms the MOGWO and NSGA-II. Finally, the K-means clustering technique is applied for Pareto characterization. Based on the research outcomes, the combined RSM and HMOGWO techniques have proved an excellent solution to simulate and optimize two-stage VCRS.ca_CA
dc.format.extent27ca_CA
dc.format.mimetypeapplication/pdfca_CA
dc.language.isoengca_CA
dc.publisherPLoSca_CA
dc.relation.isPartOfPLoS ONE, Vol. 18(2) (2023)ca_CA
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/ca_CA
dc.subjectwolvesca_CA
dc.subjectoptimizationca_CA
dc.subjectdecision makingca_CA
dc.subjectalgorithmsca_CA
dc.subjectflow rateca_CA
dc.subjectpredationca_CA
dc.subjectvaporsca_CA
dc.subjectmachine learning algorithmsca_CA
dc.title4E analysis of a two-stage refrigeration system through surrogate models based on response surface methods and hybrid grey wolf optimizerca_CA
dc.typeinfo:eu-repo/semantics/articleca_CA
dc.identifier.doihttps://doi.org/10.1371/journal.pone.0272160
dc.rights.accessRightsinfo:eu-repo/semantics/openAccessca_CA
dc.type.versioninfo:eu-repo/semantics/publishedVersionca_CA
project.funder.nameUniversity Brunei Darussalamca_CA
oaire.awardNumberUB-WRSCH/1.3/FICBF(b)/2020/011ca_CA


Ficheros en el ítem

Thumbnail

Este ítem aparece en la(s) siguiente(s) colección(ones)

Mostrar el registro sencillo del ítem

http://creativecommons.org/licenses/by/4.0/
Excepto si se señala otra cosa, la licencia del ítem se describe como: http://creativecommons.org/licenses/by/4.0/