Abstract
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/m 2 /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.
Highlights
Solar stills are widely used in solar desalination
The coefficient of variation (CV) for all of the parameters was different with the greatest variation being observed in the wind speed (WS), while the smallest variation was identified in MF
The artificial neural network (ANN) model with 7-8-1 architecture was selected as the best model for modeling the solar still production (SSP)
Summary
Solar still production (SSP) is very low. Increasing SSP has been the focus of intensive study. Much of the literature has focused on experiments to find a better design for solar stills to improve the SSP (e.g., Tanaka & Nakatake ; Kabeel et al ; Ayoub et al ; Koilraj Gnanadason et al ). These experimental studies are costly, laborious and time-consuming. Mathematical modeling (MM) may be the best alternative for finding better designs. MM is one of the most effective methods for providing a clear obvious understanding of solar still behavior and enhancing SSP. MM by artificial intelligence (AI) gives the most accurate results and is much faster than classical MM
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