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Presented is a new generation prediction model of a tubular solar still (TSS)productivity utilizing two machine learning (ML) techniques, namely:Randomforest (RF) and Artificial neural network (ANN). Prediction models wereconducted based on experimental data recorded under Egyptian climate.Meteorological and operational thermal parameters were utilized as inputlayers. Moreover, Bayesian optimization algorithm (BOA) was used to obtain theoptimal performance of RF and ANN models. In addition, these models resultswere compared to those of a multilinear regression (MLR) model. As resulted,experimentally, the average value accumulated productivity was 4.3 L/(m2day).For models results, RF was less sensitive to hyper parameters than ANN as ANNperformance could be significantly improved by BOA more than RF. In addition,RF achieved better prediction performance of TSS on the current dataset. Thedetermination coefficients (R2) of RF and ANN were 0.9964 and 0.9977,respectively, which were much higher than MLR models, 0.9431. Based on therobustness performance and high accuracy, RF is recommended as a stable methodfor predicting the productivity of TSS.

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