Forecasting the efficiency of solar still production (SSP) can reduce the capital risks involved in a solar desalination project. Solar desalination is an attractive method of water desalination and offers a more reliable water source. In this study, to estimate SSP, we employed the data obtained from experimental fieldwork. SSP is assumed to be a function of ambient temperature, relative humidity, wind speed, solar radiation, feed flow rate, temperature of feed water, and total dissolved solids in feed water. In this study, back-propagation artificial neural network (ANN) models with two transfer functions were adopted for predicting SSP. The best performance was obtained by the ANN model with one hidden layer having eight neurons which employed the hyperbolic transfer function. Results of the ANN model were compared with those of stepwise regression (SWR) model. ANN model produced more accurate results compared to SWR model in all modeling stages. Mean values for the coefficient of determination and root mean square error by ANN model were 0.960 and 0.047 L/m2/h, respectively. Relative errors of predicted SSP values by ANN model were about ±10%. In conclusion, the ANN model showed greater potential in accurately predicting SSP, whereas the SWR model showed poor performance.
Artificial intelligence for predicting solar still production and comparison with stepwise regression under arid climate
Ahmed F. Mashaly, A. A. Alazba; Artificial intelligence for predicting solar still production and comparison with stepwise regression under arid climate. Journal of Water Supply: Research and Technology-Aqua 1 May 2017; 66 (3): 166–177. doi: https://doi.org/10.2166/aqua.2017.046
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