Enhancing Solar Energy Conversion Efficiency: Thermophysical Property Predicting of MXene/Graphene Hybrid Nanofluids via Bayesian-Optimized Artificial Neural Networks

jasim, Dheyaa J. and Rajab, Husam and Alizadeh, As'ad and Sharma, Kamal and Ahmed, Mohsen and Kassim, Murizah and AbdulAmeer, S. and Alwan, Adil A. and Salahshour, Soheil and Maleki, Hamid (2024) Enhancing Solar Energy Conversion Efficiency: Thermophysical Property Predicting of MXene/Graphene Hybrid Nanofluids via Bayesian-Optimized Artificial Neural Networks. Results in Engineering, 24. p. 102858. ISSN 25901230

[thumbnail of Research Article] Text (Research Article)
Article_RE_07-09-2024.pdf - Published Version
Available under License Creative Commons Attribution Non-commercial No Derivatives.

Download (5MB)

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.

Item Type: Article
Uncontrolled Keywords: Solar energy conversion PVT solar panels, Artificial neural network, Machine learning, MXene, Graphene
Subjects: T Technology > TA Engineering (General). Civil engineering (General)
Divisions: Department of Civil Engineering > Research papers
Depositing User: ePrints Depositor
Date Deposited: 21 Nov 2024 11:50
Last Modified: 21 Nov 2024 11:50
URI: https://eprints.cihanuniversity.edu.iq/id/eprint/2682

Actions (login required)

View Item
View Item