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<p>This study adopts response surface methodology (RSM) to identify the optimal operating parameters for a pyramid solar still (PSS) integrated with silver nanoparticles (nAg) and coupled with a solar pond. PSS has proved highly efficient in water desalination, offering a sustainable solution for freshwater production. Integrating nAg and coupling the still with a solar pond (SP) are hypothesized to enhance thermal performance and productivity. Key parameters, including solar intensity (300–900&#xa0;W&#xa0;m<sup>−2</sup>), nanoparticle concentration (0.5–1.5 mass%), water depth (4–8&#xa0;cm), and solar pond temperature (50–70&#xa0;°C), were analyzed to optimize the system’s yield. RSM identifies the optimal values for enhancing the solar still’s water productivity (Pw) and water temperature (Tw). Results indicate that nAg enhancement and solar pond integration significantly increase water output, providing a promising and efficient solution for sustainable water resources. The desirability analysis revealed that the optimal conditions for achieving maximum Tw and Pw include a solar intensity of 900&#xa0;W&#xa0;m<sup>−2</sup>, a nAg concentration of 1 mass%, a water depth of 4&#xa0;cm, and a solar pond temperature of 70&#xa0;°C. The predicted Pw of 2.569&#xa0;kg&#xa0;m<sup>−2</sup> closely aligns with the experimental value of 2.626&#xa0;kg&#xa0;m<sup>−2</sup>. Similarly, the predicted water temperature of 75.54&#xa0;°C is validated by an experimental result of 76.2&#xa0;°C. Comparing the RSM-predicted values with experimental outcomes revealed error % of 2.21 and 0.87 for Pw and Tw. A comparison was made to evaluate the results obtained from RSM against those derived from nature-inspired metaheuristic algorithms, namely particle swarm optimization (PSO) and teaching–learning-based optimization (TLBO). The analysis revealed that the TLBO algorithm demonstrated superior performance, as indicated by its minimal objective function value. When comparing RSM predictions with TLBO, the error % of Pw and Tw were found to be 4.16 and 3.60%, respectively.</p> Graphical Abstract <p></p>

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Investigation on silver nanoparticle-enhanced pyramid solar still integrated with solar pond using response surface methodology and metaheuristic algorithms

  • 期刊论文

  • 发表日期：

    2025年6月24日

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  • M. Balamurugan,
  • N. Senthilkumar,
  • M. Yuvaperiyasamy,
  • G. Perumal,
  • B. Deepanraj

摘要

This study adopts response surface methodology (RSM) to identify the optimal operating parameters for a pyramid solar still (PSS) integrated with silver nanoparticles (nAg) and coupled with a solar pond. PSS has proved highly efficient in water desalination, offering a sustainable solution for freshwater production. Integrating nAg and coupling the still with a solar pond (SP) are hypothesized to enhance thermal performance and productivity. Key parameters, including solar intensity (300–900 W m−2), nanoparticle concentration (0.5–1.5 mass%), water depth (4–8 cm), and solar pond temperature (50–70 °C), were analyzed to optimize the system’s yield. RSM identifies the optimal values for enhancing the solar still’s water productivity (Pw) and water temperature (Tw). Results indicate that nAg enhancement and solar pond integration significantly increase water output, providing a promising and efficient solution for sustainable water resources. The desirability analysis revealed that the optimal conditions for achieving maximum Tw and Pw include a solar intensity of 900 W m−2, a nAg concentration of 1 mass%, a water depth of 4 cm, and a solar pond temperature of 70 °C. The predicted Pw of 2.569 kg m−2 closely aligns with the experimental value of 2.626 kg m−2. Similarly, the predicted water temperature of 75.54 °C is validated by an experimental result of 76.2 °C. Comparing the RSM-predicted values with experimental outcomes revealed error % of 2.21 and 0.87 for Pw and Tw. A comparison was made to evaluate the results obtained from RSM against those derived from nature-inspired metaheuristic algorithms, namely particle swarm optimization (PSO) and teaching–learning-based optimization (TLBO). The analysis revealed that the TLBO algorithm demonstrated superior performance, as indicated by its minimal objective function value. When comparing RSM predictions with TLBO, the error % of Pw and Tw were found to be 4.16 and 3.60%, respectively.

Graphical Abstract