Predictive model for assessing and optimizing solar still performance using artificial neural network under hyper arid environment

•Artificial neural network used to model performance of solar still for different waters.•Application of ANN agreed with experimental result in predicating solar still performance.•The contribution of different inputs on system performance was found.•Findings revealed that the developed ANN model was effective and accurate. A mathematical model to forecast the solar still performance under hyper arid conditions was developed using artificial neural network technique. The developed model expressed by different forms, water productivity (MD), operational recovery ratio (ORR) and thermal efficiency (ηth) requires ten input parameters. The input parameters included Julian day, ambient air temperature, relative humidity, wind speed, solar radiation, ultra violet index, temperature of the feed and brine water, and total dissolved solids of feed and brine water. The developed ANN model was trained, tested and validated based on measured data. The results showed that the coefficient of determination ranged from 0.991 to 0.99 and 0.94 to 0.98 for MD, ORR and ηth during training and testing process, respectively. The average values of root mean-square error for all water were 0.04L/m2/h, 2.60% and 3.41% for MD, ORR and ηth respectively. Findings revealed that the model was effective and accurate in predicting solar still performance with insignificant errors.