Prediction of tubular solar still performance by machine learning integrated with Bayesian optimization algorithm

•Two machine learning models were developed to predict the productivity of tubular solar still.•Bayesian optimization algorism was considered for both models.•Optimized models more accurately predicted production with better evaluation indicators.•Random forest was less sensitive to hyper parameters...

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Veröffentlicht in:	Applied thermal engineering Jg. 184; S. 116233
Hauptverfasser:	Wang, Yunpeng, Kandeal, A.W., Swidan, Ahmed, Sharshir, Swellam W., Abdelaziz, Gamal B., Halim, M.A., Kabeel, A.E., Yang, Nuo
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Oxford Elsevier Ltd 05.02.2021 Elsevier BV
Schlagworte:	Algorithms Artificial intelligence Artificial neural network Artificial neural networks Bayesian analysis Bayesian optimization Machine learning Mathematical models Neural networks Optimization Parameter sensitivity Prediction models Random forest Regression model Studies Tubular solar still Random forest Artificial neural network Bayesian optimization Regression model Tubular solar still Machine learning
ISSN:	1359-4311, 1873-5606
Online-Zugang:	Volltext
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