Jasim, Dheyaa J.Rajab, HusamAlizadeh, As'adSharma, KamalAhmed, MohsenKassim, MurizahMaleki, Hamid2024-10-152024-10-15202402590-123010.1016/j.rineng.2024.1028582-s2.0-85203523133https://doi.org/10.1016/j.rineng.2024.102858https://hdl.handle.net/20.500.14517/6574Accurately 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.eninfo:eu-repo/semantics/openAccessSolar energy conversionPVT solar panelsArtificial neural networkMachine learningMXeneGrapheneArticleQ124WOS:001314576900001