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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Bibliographic Details
Published in:	Applied thermal engineering Vol. 184; p. 116233
Main Authors:	Wang, Yunpeng, Kandeal, A.W., Swidan, Ahmed, Sharshir, Swellam W., Abdelaziz, Gamal B., Halim, M.A., Kabeel, A.E., Yang, Nuo
Format:	Journal Article
Language:	English
Published:	Oxford Elsevier Ltd 05.02.2021 Elsevier BV
Subjects:	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 Access:	Get full text
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