Enhancing solar energy conversion efficiency: Thermophysical property predicting of MXene/Graphene hybrid nanofluids via bayesian-optimized artificial neural networks

dc.authorscopusid 57225906716
dc.authorscopusid 58112691900
dc.authorscopusid 56999952800
dc.authorscopusid 56047171100
dc.authorscopusid 57404280300
dc.authorscopusid 55310783100
dc.authorscopusid 23028598900
dc.authorwosid Jasim, Dheyaa/GPS-5013-2022
dc.authorwosid Sharma, Kamal/AAL-3794-2020
dc.authorwosid Ahmed, Mohsen/ABF-9207-2021
dc.authorwosid Rajab, Husam/AAI-8991-2020
dc.contributor.author Jasim, Dheyaa J.
dc.contributor.author Rajab, Husam
dc.contributor.author Alizadeh, As'ad
dc.contributor.author Sharma, Kamal
dc.contributor.author Ahmed, Mohsen
dc.contributor.author Kassim, Murizah
dc.contributor.author Maleki, Hamid
dc.date.accessioned 2024-10-15T20:20:23Z
dc.date.available 2024-10-15T20:20:23Z
dc.date.issued 2024
dc.department Okan University en_US
dc.department-temp [Jasim, Dheyaa J.] Al Amarah Univ Coll, Dept Petr Engn, Maysan, Iraq; [Rajab, Husam] Alasala Univ, Coll Engn, Mech Engn Dept, King Fahad Bin Abdulaziz Rd,POB 12666, Dammam 31483, Saudi Arabia; [Alizadeh, As'ad] Cihan Univ Erbil, Coll Engn, Dept Civil Engn, Erbil, Iraq; [Sharma, Kamal] GLA Univ, Inst Engn & Technol, Mathura 281406, UP, India; [Ahmed, Mohsen] Imam Abdulrahman Bin Faisal Univ, POB 1982, Dammam 31441, Eastern Provinc, Saudi Arabia; [Kassim, Murizah] Univ Teknol MARA, Inst Big Data Analyt & Artificial Intelligence IBD, Shah Alam 40450, Selangor, Malaysia; [Kassim, Murizah] Univ Teknol MARA, Coll Engn, Sch Elect Engn, Shah Alam 40450, Selangor, Malaysia; [AbdulAmeer, S.] Univ Babylon, Coll Engn, Dept Automobile Engn, Al Musayab, Iraq; [AbdulAmeer, S.] Ahl Al Bayt Univ, Kerbala, Iraq; [Alwan, Adil A.] Natl Univ Sci & Technol, Coll Tech Engn, Dhi Qar 64001, Iraq; [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; [Maleki, Hamid] Renewable Energy Res Grp, Esfahan, Iran en_US
dc.description.abstract Accurately predicting thermo-physical properties (TPPs) of MXene/graphene-based nanofluids is crucial for photovoltaic/thermal solar systems, driving focused research on developing precise TPP predictive models. This study presents optimized multi-layer perceptron neural network (MLPNN) models, leveraging Bayesian optimization to refine architectural and training hyperparameters, including hidden layers, neurons, activation functions, standardization, and regularization terms. A comparative analysis of Bayesian acquisition functions-the probability of improvement (POI), lower confidence bound (LCB), expected improvement (EI), expected improvement plus (EIP), expected improvement per second plus (EIPSP), and expected improvement per second (EIPS)-demonstrated that the POI-MLPNN achieves the most accurate results, as evidenced by the lowest MAPE of 1.0923 % and exceptional consistency with an R-value of 0.99811. The EI-MLPNN and EIP-MLPNN models recorded the same outputs. The EI/EIP-MLPNN (R = 0.99668) model excels in consistency over LCB-MLPNN (R = 0.99529) and EIPSP-MLPNN (R = 0.99667). The optimized models offer a reliable, cost-efficient alternate for experimental and computational TPP analyses. Leveraging insights from these models enables better control over nanofluid TPPs in solar systems, enhancing energy conversion efficiency. en_US
dc.description.woscitationindex Emerging Sources Citation Index
dc.identifier.citationcount 0
dc.identifier.doi 10.1016/j.rineng.2024.102858
dc.identifier.issn 2590-1230
dc.identifier.scopus 2-s2.0-85203523133
dc.identifier.scopusquality Q1
dc.identifier.uri https://doi.org/10.1016/j.rineng.2024.102858
dc.identifier.uri https://hdl.handle.net/20.500.14517/6574
dc.identifier.volume 24 en_US
dc.identifier.wos WOS:001314576900001
dc.language.iso en
dc.publisher Elsevier en_US
dc.relation.publicationcategory Makale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı en_US
dc.rights info:eu-repo/semantics/openAccess en_US
dc.scopus.citedbyCount 10
dc.subject Solar energy conversion en_US
dc.subject PVT solar panels en_US
dc.subject Artificial neural network en_US
dc.subject Machine learning en_US
dc.subject MXene en_US
dc.subject Graphene en_US
dc.title Enhancing solar energy conversion efficiency: Thermophysical property predicting of MXene/Graphene hybrid nanofluids via bayesian-optimized artificial neural networks en_US
dc.type Article en_US
dc.wos.citedbyCount 9

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