Predicting the yield of stepped corrugated solar distiller using kernel-based machine learning models

Recently, accurate prediction of the distilled productivity is a decisive issue to assess and compare the ability of these designs of solar stills to provide distilled water to a certain application without carrying out timeconsuming and more costly field experiments. Machine learning techniques have become powerful alternatives to conventional modeling approaches in different engineering disciplines. In this study, two improved machine learning models, namely relevance vector machine (RVM) and least squares support vector machine (LSSVM), are proposed to predict the productivity of two solar distillers. The two solar distillers are a conventional single slope single basin solar still (SSSS) and a modified stepped corrugated solar still (SCSS). Initially, the experimental energetic and exergetic performance of the SCSS is compared with that of SSSS. The experimental results showed that the water yield, energy efficiency, and exergy efficiency of the SCSS are improved by about 255 %, 239%, and 379%, respectively, compared with that of the SSSS. Then, the proposed RVM and LSSVM models are used to predict the productivity of two solar stills. During the simulation, different kernel functions, such as Laplace, linear, spline, Gaussian (G), and radial basis function (RBF), are embedded in both models to obtain the optimal function that maximizes the model accuracy. The simulated results showed that Gaussian and radial basis functions are the most appropriate kernel functions for RVM and LSSVM algorithms, respectively. Particularly, the LSSVM-RBF and RVM-G models predict the distilled production of the SCSS with determination coefficient values of 0.999 and 0.982, and root mean square error values of about 6.646 e-06, and 0.002, respectively Consequently, the LSSVM-RBF model is recommended as a promising prediction tool in predicting the production of both solar distillers.