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Study on Single-Slope Solar Still for Experimental and Data-Driven Analysis for Improving Productivity with Different Basin Materials.

  1. Naveen Kumar Department of Mechanical,Institute of Technology & Management, Aligarh
  2. Happy Srivastava Department of Mechanical,Institute of Technology & Management, Aligarh
  3. R. Prasad Department of Mechanical, Hindustan College of Science & Technology, Mathura

Abstract

This study investigates the single-slope solar still under the diurnal variation of water temperature and distillate yield under identical operating conditions. Experimental analysis was conducted to evaluate the performance enhancement through the incorporation of natural basin materials, namely hemp and sand. The water distillation process is focused on improving potable water productivity and thermal behaviour. The inclusion of hemp and sand in the basin leads to noticeable differences in productivity and peak temperature, with the improvement of evaporation characteristics, heat retention and heat absorption. On the basis of experimental data, machine learning models—Random Forest (RF) and Support Vector Regression (SVR) are assessed and predicted system performance. The RF model exhibited superior predictive capability, closely following the experimental trends with minimal deviation. A comparative analysis based on predicted and actual water temperatures is executed. In contrast, the SVR model indicates its limited ability to capture nonlinear thermal dynamics, showing significant underestimation, particularly at higher temperature ranges. The basin modification using hemp and sand enhances thermal efficiency as hemp shows improved heat retention characteristics, and eventually, results demonstrate improved distillate output. Additionally, a reliable tool for optimisation and performance estimation of solar still systems is obtained in the form of RF- based predictive modelling. To develop efficient and sustainable solar desalination technologies, an integral approach combining eco-friendly materials with data-driven techniques, on the basis of findings highlight.

Keywords

References (22)

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