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<p>This study employs Response Surface Methodology (RSM) based on a Box-Behnken design to optimize the performance of a single-slope solar still. The modified solar still (MODSS-1), incorporating a 20° solar angle, mirrors, and jute storage material demonstrated enhanced performance compared to the conventional still (CSS). Furthermore, MODSS-2, which additionally utilized pebbles as thermal storage materials, achieved an increase in 55.56% distillate production and 51.42% still efficiency relative to CSS. To identify the best operating and design parameters, optimization techniques were applied by using surface plots and ANOVA to evaluate the parameters, while regression models establish the quantitative relationships between variables and responses. A hybrid Neuro-Genetic algorithm was subsequently applied to optimize both still efficiency and distilled output. The identified optimal settings were validated experimentally. The proposed optimization framework demonstrated reliable with prediction errors of 4.23% for still efficiency and 5.76% for distillate output, confirming the accuracy and reproducibility of results.</p>

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Optimization of working parameters of a single slope solar still with thermal energy storage materials using hybrid neuro-genetic technique

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年6月29日

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  • Chinmaya P. Mohanty,
  • Aryan Sai Aneesh Tallam,
  • Tapano K. Hotta,
  • Dillip K. Biswal,
  • Aruna K. Behura,
  • Bibhuti B. Sahoo

摘要

This study employs Response Surface Methodology (RSM) based on a Box-Behnken design to optimize the performance of a single-slope solar still. The modified solar still (MODSS-1), incorporating a 20° solar angle, mirrors, and jute storage material demonstrated enhanced performance compared to the conventional still (CSS). Furthermore, MODSS-2, which additionally utilized pebbles as thermal storage materials, achieved an increase in 55.56% distillate production and 51.42% still efficiency relative to CSS. To identify the best operating and design parameters, optimization techniques were applied by using surface plots and ANOVA to evaluate the parameters, while regression models establish the quantitative relationships between variables and responses. A hybrid Neuro-Genetic algorithm was subsequently applied to optimize both still efficiency and distilled output. The identified optimal settings were validated experimentally. The proposed optimization framework demonstrated reliable with prediction errors of 4.23% for still efficiency and 5.76% for distillate output, confirming the accuracy and reproducibility of results.