Comparison of Neural Network Models in the Estimation of the Performance of Solar Still Under Jordanian Climate

Abstract

Abstrac t—Three Artificial Neural Network models (Feedforward, Elman, and Nonlinear Autoregressive Exogenous (NARX) networks) were used to find the performance of triple solar still operating under Jordanian climate. Previously obtained experimental data was used to train the neural network. time, Hourly variation of cover glass temperature (Tg), w ater temperature in the upper basin (Tw1), w ater temperature in the middle basin (Tw2) and w ater temperature in the lower basin of the triple basin still (Tw3), Distillate volume, ambient temperature (Ta), plate temperature (TP) and hour ly solar intensity (Is) w ere used in the input layer of the network. The thermal efficiency (η) of a triple basin solar still was in the output layer. The obtained results were verified against previously obtained experimental data. It was found that Artificial Neural Network technique may be used to estimate the efficiency of the triple solar still with excellent accuracy with the coefficient of determination of Elman, feedforward and NARX models were found to be 0.9036, 0.99838 and 0.99863, respectively. The obtained results showed that feedforward model had the best ability to estimate the required performance, while NARX and Elman network had the lowest ability to estimate it.