Optimization of thermophysical properties of nanofluids using a hybrid procedure based on machine learning, multi-objective optimization, and multi-criteria decision-making

dc.authorid Zhang, Tao/0000-0002-0134-3094
dc.authorid Manafi Khajeh Pasha, Anahita/0000-0002-6235-3202
dc.authorscopusid 57216092176
dc.authorscopusid 57209368418
dc.authorscopusid 22136195900
dc.authorscopusid 57225906716
dc.authorscopusid 57222062476
dc.authorscopusid 57198301674
dc.authorscopusid 23028598900
dc.authorwosid Zhang, Tao/ACM-0777-2022
dc.contributor.author Zhang, Tao
dc.contributor.author Pasha, Anahita Manafi Khajeh
dc.contributor.author Sajadi, S. Mohammad
dc.contributor.author Jasim, Dheyaa J.
dc.contributor.author Nasajpour-Esfahani, Navid
dc.contributor.author Maleki, Hamid
dc.contributor.author Baghaei, Sh.
dc.date.accessioned 2024-05-25T11:37:27Z
dc.date.available 2024-05-25T11:37:27Z
dc.date.issued 2024
dc.department Okan University en_US
dc.department-temp [Zhang, Tao] China Agr Univ, Coll Resources & Environm Sci, Beijing Key Lab Farmland Soil Pollut Prevent & Rem, Key Lab Plant Soil Interact,Minist Educ, Beijing 100193, Peoples R China; [Pasha, Anahita Manafi Khajeh] Urmia Univ Med Sci, Fac Dent, Dept Periodont, Orumiyeh, Iran; [Sajadi, S. Mohammad] Cihan Univ Erbil, Dept Nutr, Erbil, Kurdistan, Iraq; [Jasim, Dheyaa J.] Al Amarah Univ Coll, Dept Petr Engn, Maysan, Iraq; [Nasajpour-Esfahani, Navid] Georgia Inst Technol, Dept Mat Sci & Engn, Atlanta, GA 30332 USA; [Maleki, Hamid; Baghaei, Sh.] Isfahan Univ Technol, Dept Mech Engn, Esfahan 8415683111, Iran; [Salahshour, Soheil] Istanbul Okan Univ, Fac Engn & Nat Sci, Istanbul, Turkiye; [Salahshour, Soheil] Bahcesehir Univ, Fac Engn & Nat Sci, Istanbul, Turkiye; [Salahshour, Soheil] Lebanese Amer Univ, Dept Comp Sci & Math, Beirut, Lebanon en_US
dc.description Zhang, Tao/0000-0002-0134-3094; Manafi Khajeh Pasha, Anahita/0000-0002-6235-3202 en_US
dc.description.abstract The rheological and thermal behavior of nanofluids in real-world scenarios is significantly affected by their thermophysical properties (TPPs). Therefore, optimizing TPPs can remarkably improve the performance of nanofluids. In this regard, in the present study, a hybrid strategy is proposed that combines machine learning (ML), multi-objective optimization (MOO), and multi-criteria decision-making (MCDM) to select optimal parameters for water-based multi-walled carbon nanotubes (MWCNTs)-oxide hybrid nanofluids. In the first step, four critical TPPs, including density ratio (DR), viscosity ratio (VR), specific heat capacity ratio (SHCR), and thermal conductivity ratio (TCR), are modeled using two efficient ML techniques, the group method of data handling neural network (GMDH-NN) and combinatorial (COMBI) algorithm. In the next step, the superior models are subjected to a four-objective optimization by the well-known non-dominated sorting genetic algorithm II (NSGA-II), which aims to minimize DR/VR and maximize SHCR/TCR. This study considers volume fraction (VF), oxide nanoparticle (NP) type, and system temperature as optimization variables. In the final step, two prominent MCDM techniques, TOPSIS and VIKOR, were used to identify the desirable optimal points from the Pareto fronts generated by the MOO algorithm. ML results reveal the COMBI algorithm's superior reliability in accurately modeling various TPPs. The pattern of Pareto fronts for all oxide-NPs indicated that over one-third of the optimal points have a VF > 1.5 %. On the other hand, the distribution of optimal points across different temperature ranges varied significantly depending on the type of oxide-NPs. For Al2O3-based nanofluid, around 90 % of the optimal points were within 40-50 degrees C. Conversely, for nanofluids containing CeO2 NPs, only approximately 24 % of the optimal points were found within the same temperature range. Considering diverse scenarios for weighting TPPs in the MCDM process implied that combining CeO2/ZnO oxide-NPs with MWCNTs in water-based nanofluids is highly effective across various real-world applications. en_US
dc.description.sponsorship National Key Tech- nology Research and Development Program of China [2023YFE0104700]; National Natural Science Foundation of China [31401944] en_US
dc.description.sponsorship The research was sustained by a grant from the National Key Tech- nology Research and Development Program of China [Grant number 2023YFE0104700] , the National Natural Science Foundation of China [Grant Number 31401944] . en_US
dc.identifier.citationcount 1
dc.identifier.doi 10.1016/j.cej.2024.150059
dc.identifier.issn 1385-8947
dc.identifier.issn 1873-3212
dc.identifier.scopus 2-s2.0-85186334090
dc.identifier.scopusquality Q1
dc.identifier.uri https://doi.org/10.1016/j.cej.2024.150059
dc.identifier.uri https://hdl.handle.net/20.500.14517/1169
dc.identifier.volume 485 en_US
dc.identifier.wos WOS:001208917000001
dc.identifier.wosquality Q1
dc.institutionauthor Salahshour S.
dc.language.iso en
dc.publisher Elsevier Science Sa en_US
dc.relation.publicationcategory Makale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı en_US
dc.rights info:eu-repo/semantics/closedAccess en_US
dc.scopus.citedbyCount 19
dc.subject Dynamic viscosity en_US
dc.subject Hybrid nanofluid en_US
dc.subject Machine learning en_US
dc.subject Multi -criteria decision -making en_US
dc.subject Multi -objective optimization en_US
dc.subject Thermal conductivity en_US
dc.title Optimization of thermophysical properties of nanofluids using a hybrid procedure based on machine learning, multi-objective optimization, and multi-criteria decision-making en_US
dc.type Article en_US
dc.wos.citedbyCount 18

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