Abstract
•Solar still was used to produce water.•The instantaneous thermal efficiency (ηith) of solar still was modeled.•Multilayer perceptron (MLP) neural network and multiple linear regression (MLR) were used in the modeling process.•The MLP model was better than MLR model.•Using the MLP model provides ηith values with high accuracy.
The purpose of this study was to determine the viability of modeling the instantaneous thermal efficiency (ηith) of a solar still, using weather and operational data with Multi-Layer Perceptron (MLP) neural network and multiple linear regressions (MLR). This study used weather and operational variables that were hypothesized to affect solar still performance. In the MLP model, nine variables were used as input parameters: Julian day, ambient temperature, relative humidity, wind speed, solar radiation, temperature of feed water, temperature of brine water, total dissolved solids of feed water, and total dissolved solids of brine water. The ηith was the one node present in the output layer. The same parameters were used in the MLR model. Discussions of advantages and disadvantages are given from different points of view for both models. Performance evaluation criteria indicated that the MLP model was better than the MLR model. The average value of the coefficient of determination for the MLP model was higher by 11.23% than for the MLR model. The average value of the root mean square error for the MLP model (2.74%) was lower compared to the MLR model. The relative errors of predicted ηith values for the MLP model were mostly in the vicinity of ±10%. Therefore, the MLP model is preferred as a highly precise model in predicting ηith compared to the MLR model. It is expected that this study could be highly beneficial to those dealing with the design of solar desalination systems.