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

Year : 2026 | Volume : 13 | Issue : 02 | Page :
    By

    Naveen Kumar,

  • Happy Srivastava,

  • R. Prasad,

  1. M. Tech Student, Department of Mechanical,Institute of Technology & Management, Aligarh, Uttar Pradesh, India
  2. Assistant professor, Department of Mechanical,Institute of Technology & Management, Aligarh, Uttar Pradesh, India
  3. Associate Professor, Department of Mechanical, Hindustan College of Science & Technology, Mathura, Uttar Pradesh, India

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: Solar still, Potable water, Machine Learning, Basin Material

[This article belongs to Emerging Trends in Chemical Engineering ]

How to cite this article:
Naveen Kumar, Happy Srivastava, R. Prasad. Study on Single-Slope Solar Still for Experimental and Data-Driven Analysis for Improving Productivity with Different Basin Materials.. Emerging Trends in Chemical Engineering. 2026; 13(02):-.
How to cite this URL:
Naveen Kumar, Happy Srivastava, R. Prasad. Study on Single-Slope Solar Still for Experimental and Data-Driven Analysis for Improving Productivity with Different Basin Materials.. Emerging Trends in Chemical Engineering. 2026; 13(02):-. Available from: https://journals.stmjournals.com/etce/article=2026/view=242904


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Regular Issue Subscription Original Research
Volume 13
Issue 02
Received 28/04/2026
Accepted 01/05/2026
Published 04/05/2026
Publication Time 6 Days
105

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