Mohammad Hamdan, Lubna Badri and Eman Abdelhafez “Modeling Triple Solar Still Production Using Jordan Weather Data and Artificial Neural Networks” International Journal of Thermal and Environmental Engineering (IJTEE), 2014, volume 7, Number 2, pages 87-93.
Abstract:
The objective of the study were to asses the sensitivity of the Artificial Mural Networks (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. Satisfactory results for the triple solar still 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. To accomplish this objective, a study has been performed to determine the effectiveness of triple solar still efficiency (η) using ANNs. The study used the following parameters as an input to the ANN: time, hourly variation of cover glass
temperature (Tg), water temperature in the upper basin (Tw1), water temperature in the middle basin (Tw2) and water temperature in the lower basin of the triple basin still (Tw3), distillate volume, ambient temperature (Ta), plate temperature (TP) and hourly solar intensity (Is).