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Abstract
Solar still productivity (SSP) essentially describes the performance of solar still systems and is an important factor to consider in achieving a reliable design. This study presents the use of artificial neural networks (ANN), adaptive neuro-fuzzy inference systems (ANFIS), and multiple regression (MR) for forecasting the SSP of an inclined solar still in a hot, arid environment. The experimental data used for the modelling process included meteorological and operational variables. Input variables were relative humidity, solar radiation, feed flow rate, and total dissolved solids of feed and brine. The models were assessed statistically using the correlation coefficient (CC), root mean square error (RMSE), overall index of model performance (OI), mean absolute error (MAE), and mean absolute relative error (MARE). Overall, ANN was shown to be superior (CC = 0.98, RMSE = 0.05 L·m−2·h−1, OI = 0.95, MAE = 0.03 L·m−2·h−1, and MARE = 8.92%) to ANFIS and MR for SSP modelling. The relatively low errors obtained by the ANN technique led to high model predictability and feasibility of modelling the SSP. Thus, our findings indicate that ANN can be applied as an accurate method for predicting SSP.
Highlights
A solar still is a simple device that uses solar energy to convert available brackish or saline water into fresh water for both domestic and agricultural applications
We discussed the use of artificial neural network (ANN), adaptive neuro-fuzzy inference system (ANFIS), and multiple regression (MR) for Solar still productivity (SSP) modelling, acquired experimental data from a passive inclined solar still in an arid climate, and applied the above models to this data
We used a stepwise technique to arrive at 5 combinations
Summary
A solar still is a simple device that uses solar energy to convert available brackish or saline water into fresh water for both domestic and agricultural applications. The large amount of data required to measure the parameters needed for evaluating and validating an HMT model limit the effectiveness of such approaches in predicting solar still productivity (SSP). Soft computing comprises artificial neural networks (ANN), genetic algorithms (GAs), fuzzy logic (FL), adaptive neuro-fuzzy inference systems (ANFIS), support vector machines (SVMs), and data mining (DM). These approaches offer advantages over conventional modelling methods, including the capability to handle large amounts of noisy data from dynamic and non-linear systems, when the underlying physical processes are not fully comprehended. Two different soft computing methods, ANN and ANFIS, were extended in order to estimate the productivity of an inclined solar still operating under arid conditions.
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