Neural network approach for predicting solar still production using agricultural drainage as a feedwater source

Abstract

This study investigates the application of artificial neural network (ANN) for predicting solar still production (MD). Agricultural drainage water (ADW) was desalinated using a solar still. Important meteorological variables: ambient air temperature, relative humidity, wind speed, and solar radiation, together with the operational variables of flow rate, temperature, and total dissolved solids of feedwater, were considered as input parameters for ANN modeling. The output parameter was MD. The results revealed that the ANN model with five neurons and hyperbolic tangent transfer function was the most appropriate for MD prediction based on the minimum measures of error. The optimal ANN model had a 7–5–1 architecture. The ANN model was also compared to multiple linear regression (MLR). The results indicated that, compared to the MLR model, the ANN model provided better prediction results in all modeling stages. The average of the coefficient of determination between the ANN results and the experimental data was more than 0.96. Consequently, the ANN model was shown to have acceptable generalization capability and accuracy. The relative errors of forecasted MD values for the ANN model were mostly in the vicinity of ±10%. These results indicate that ANN can be successfully used in the MD prediction of a solar still desalinating ADW. One major output/contribution of this research involves assessment of the ANN modeling technique during ADW solar desalination, which adds a new perspective to the system analysis, design, and modeling for the potential productivity of a solar still to produce water during the ADW desalination process.