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• W3211036174 endingPage "115411" @default.
• W3211036174 startingPage "115411" @default.
• W3211036174 abstract "Today artificial intelligence is a major utility technology that can help industry and research in many fields, including solar thermal energy for water desalination purposes. Hence, the main aim of the paper focus on utilizing an artificial intelligent regression model to predict the efficiency of water desalination, which depends on solar thermal energy. The contribution helps manufacturers and research determine the productivity of the designed desalination system in the beta environment before realizing the system. The used desalination system merges and hybrids in our experimental modelling solar still and air humidification and dehumidification. Regards the prediction of performance and productivity, a wide range of regression algorithms, including multilayer perceptron (MLP), decision trees (DT), and Bayesian ridge regression (BRR), were applied. Also, the MLP is optimized to enhance the prediction by employing the Adam optimization to propose OMLP. The OMLP is compared to regular MLP, DT, and BRR using a variety of evaluation metrics, including statistical measures, the goodness of fit, and regression accuracy. The OMLP achieves better performance of dependent variables; YHDH, YSS, Tswh,in, Tswh,o, Ta,H,in, Ta,H,o, and Φa,H,in using whole independent variables by RMSE ∈ [0.07171, 8.05636], Max Error ∈ [0.10108, 14.19858], and CRM ∈ [0.0202, 0.0329] and using top three correlated independent variables by MAE ∈ [0. 0514, 5.777], RMSE ∈ [0.07171, 7.0559], and CRM ∈ [0.0029, 0.0498]." @default.
• W3211036174 created "2021-11-08" @default.
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• W3211036174 date "2022-01-01" @default.
• W3211036174 modified "2023-10-17" @default.
• W3211036174 title "Predictive modelling for solar power-driven hybrid desalination system using artificial neural network regression with Adam optimization" @default.
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• W3211036174 doi "https://doi.org/10.1016/j.desal.2021.115411" @default.
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