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Productivity Modeling Enhancement of a Solar Desalination Unit with Nanofluids Using Machine Learning Algorithms Integrated with Bayesian Optimization
Author(s) -
Kandeal Abdallah W.,
An Meng,
Chen Xiangquan,
Algazzar Almoataz M.,
Kumar Thakur Amrit,
Guan Xiaoyu,
Wang Jianyong,
Elkadeem Mohamed R.,
Ma Weigang,
Sharshir Swellam W.
Publication year - 2021
Publication title -
energy technology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.91
H-Index - 44
eISSN - 2194-4296
pISSN - 2194-4288
DOI - 10.1002/ente.202100189
Subject(s) - algorithm , artificial neural network , support vector machine , machine learning , random forest , mean squared error , productivity , computer science , artificial intelligence , mathematics , statistics , economics , macroeconomics
Herein, double slope solar still (DSSS) performance is accurately forecast with the aid of four different machine learning (ML) models, namely, artificial neural network (ANN), random forest (RF), support vector regression (SVR), and linear SVR. Furthermore, the tuning of ML models is optimized using the Bayesian optimization algorithm (BOA) to get the optimal performance of all models and identify the best predictive one. All the models are trained, tested, and validated depending on experimental data acquired under Egyptian climatic conditions. The results reveal that ML models can be a powerful tool to forecast DSSS performance. Among them, RF is the most potent ML model obtaining the highest determination coefficient ( R 2 ) and the lowest absolute error percentage of 0.997% and 2.95%, respectively. Furthermore, the experimental results also show that the mean value of accumulated (daily) freshwater productivity from DSSS is 4.3 L m −2 .