Experimental and modeling study to estimate the productivity of inclined passive solar still using ANN methodology in arid conditions

Abstract

An artificial neural network (ANN) was developed for predicting solar still production (MD) under a hyper-arid environment. A three-layer feed-forward neural network based on back-propagation algorithm was used in the modeling process. The inputs comprise air temperature, relative humidity, wind speed, solar radiation, feed water temperature, feed water total dissolved solids, and feed water flow rate. The output was MD. The ANN model with optimal prediction performance was found by testing several networks. Then, the findings obtained from the ANN model were compared with the findings from the multiple linear regression (MLR) model. The optimal ANN model had a 7-8-1 architecture with a hyperbolic tangent transfer function. Statistical criteria revealed that the ANN model performed better than MLR in predicting MD. The root-mean-square errors during the testing process for MD were 0.070 and 0.128 for the ANN and MLR models, respectively. The coefficient of determination values for the training, testing, and validation data sets in the prediction of MD by ANN were 0.990, 0.918, and 0.945, respectively. The relative errors of the predicted MD values for the ANN model were approximately ±10%. Therefore, the ANN model can successfully predict MD.