Modeling solar still production using local weather data and artificial neural networks

A study has been performed to predict solar still distillate production from single examples of two different commercial solar stills that were operated for a year and a half. The purpose of this study was to determine the effectiveness of modeling solar still distillate production using artificial neural networks (ANNs) and local weather data. The study used the principal weather variables affecting solar still performance, which are the daily total insolation, daily average wind velocity, daily average cloud cover, daily average wind direction and daily average ambient temperature. The objectives of the study were to assess the sensitivity of the ANN predictions to different combinations of input parameters as well as to determine the minimum amount of inputs necessary to accurately model solar still performance. It was found that 31–78% of ANN model predictions were within 10% of the actual yield depending on the input variables that were selected. By using the coefficient of determination, it was found that 93–97% of the variance was accounted for by the ANN model. About one half to two thirds of the available long term input data were needed to have at least 60% of the model predictions fall within 10% of the actual yield. Satisfactory results for two different solar stills suggest that, with sufficient input data, the ANN method could be extended to predict the performance of other solar still designs in different climate regimes.

[1]  Siaka Toure,et al.  A numerical model and experimental investigation for a solar still in climatic conditions in Abidjan (Côte d'Ivoire) , 1997 .

[2]  Soteris A. Kalogirou,et al.  Seawater desalination using renewable energy sources , 2005 .

[3]  J. W. Bloemer,et al.  Energy balances in solar distillers , 1961 .

[4]  Hassan E.S. Fath,et al.  Solar distillation: a promising alternative for water provision with free energy, simple technology and a clean environment , 1998 .

[5]  P. I. Cooper,et al.  Digital simulation of transient solar still processes , 1969 .

[6]  Hassan E.S. Fath,et al.  A naturally circulated humidifying/dehumidifying solar still with a built-in passive condenser , 2004 .

[7]  G. Tiwari,et al.  Simple multiple wick solar still: Analysis and performance , 1981 .

[8]  Hassan E.S. Fath,et al.  Effect of adding a passive condenser on solar still performance , 1992 .

[9]  N. Venkatesh.  Performance evaluation of single and double-basin solar stills in Las Vegas, Nevada , 2007 .

[10]  Bassam Abu-Hijleh,et al.  Enhanced solar still performance using water film cooling of the glass cover , 1996 .

[11]  M. S. Sodha,et al.  Double basin solar still , 1980 .

[12]  Ernani Sartori.  ON THE NOCTURNAL PRODUCTION OF A CONVENTIONAL SOLAR STILL USING SOLAR PRE-HEATED WATER , 1988 .

[13]  Simon Haykin,et al.  Neural Networks: A Comprehensive Foundation , 1998 .

[14]  Geoffrey E. Hinton,et al.  Learning internal representations by error propagation , 1986 .

[15]  Soteris A. Kalogirou,et al.  Artificial neural networks in renewable energy systems applications: a review , 2001 .

[16]  G. N. Tiwari,et al.  Transient performance of a single basin solar still with water flowing over the glass cover , 1984 .

[17]  G. N. Tiwari,et al.  Transient analysis of solar still in the presence of dye , 1989 .

[18]  H. N. Singh,et al.  Present status of solar distillation , 2003 .

[19]  G. N. Tiwari,et al.  Effect of water depths on heat and mass transfer in a passive solar still: in summer climatic condition , 2006 .