Low-cost bilayered structure for improving the performance of solar stills: Performance/cost analysis and water yield prediction using machine learning

This paper aims to enhance the performance of conventional solar still (CSS) using a low cost heat localization bilayered structure (HLBS). The HLBS consists of a bottom supporting layer (SL) made of low thermal conductivity as well as low density material and a top absorbing layer (AL) made of a photo thermal material with a high sunlight absorptivity as well as an enhanced conversion efficiency. The developed HLBS helps in increasing the evaporation rate and minimize the heat losses in a modified solar still (MSS). Two similar SSs were designed and tested to evaluate SSs' performance without and with HLBS (CSS and MSS). Moreover, three machine learning (ML) methods were utilized as predictive tools to obtain the water yield of the SSs, namely artificial neural network (ANN), support vector machine (SVM), and adaptive neuro-fuzzy inference system (ANFIS). The prediction accuracy of the models was evaluated using different statistical measured. The obtained results showed that the daily freshwater yield, energy efficiency, and exergy efficiency of the MSS was enhanced by 34%, 34%, and 46% compared with that of CSS. The production cost per liter of the MSS is 0.015 $/L. Moreover, SVM outperformed other ML methods for both SSs based on different statistical measures.

Automated summary

This study aimed to enhance the performance of conventional solar stills using a low cost heat localization bilayered structure and machine learning methods for prediction and evaluation. Results showed significant improvements in daily freshwater yield, energy efficiency, and exergy efficiency of the modified solar still compared to the conventional solar still, with support vector machine outperforming other machine learning methods in prediction accuracy.