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<p>The thermophysical properties of novel Graphene/MXene-based fluids have great potential for enhancing the efficiency of solar energy systems. However, optimizing these properties remains challenging due to the complex interactions between nanomaterial composition and system conditions. This study presents a new hybrid framework that combines response surface methodology (RSM), heuristic and metaheuristic optimization, and advanced decision-making techniques to enhance the thermal conductivity (TC) and dynamic viscosity (DV) of these fluids. RSM-based predictive models demonstrated high accuracy (R² = 0.9997 for TC and 0.9984 for DV), validated using regression graphs, violin plots, and absolute relative deviation analysis. Optimization was conducted using enhanced hill climbing (EHC), NSGA-II, and multi-objective ant lion optimizer (MOALO), with decision-making strategies such as desirability function and VIKOR technique. Results revealed that optimal MXene ratios depend on nanomaterial mass fraction (MF) and temperature, with optimal conditions clustering around 60&#xa0;°C, MF of 1.5–2 wt%, and MXene ratios of 0.47–0.5. Decision-making analysis highlighted the trade-offs between TC and DV based on varying weight distributions. This research provides a cost-effective methodology to optimize nanofluids for solar energy applications with high precision, reducing computational and laboratory costs.</p>

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Optimizing MXene graphene based fluids for solar energy conversion and storage using a novel intelligent framework

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年8月28日

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  • Mohamed Bechir Ben Hamida,
  • Ali Basem,
  • Ala Eldin A. Awouda,
  • Aman Sharma,
  • Narinderjit Singh Sawaran Singh,
  • Nashmi H. Alrasheedi,
  • Komal Parashar,
  • Loghman Mostafa

摘要

The thermophysical properties of novel Graphene/MXene-based fluids have great potential for enhancing the efficiency of solar energy systems. However, optimizing these properties remains challenging due to the complex interactions between nanomaterial composition and system conditions. This study presents a new hybrid framework that combines response surface methodology (RSM), heuristic and metaheuristic optimization, and advanced decision-making techniques to enhance the thermal conductivity (TC) and dynamic viscosity (DV) of these fluids. RSM-based predictive models demonstrated high accuracy (R² = 0.9997 for TC and 0.9984 for DV), validated using regression graphs, violin plots, and absolute relative deviation analysis. Optimization was conducted using enhanced hill climbing (EHC), NSGA-II, and multi-objective ant lion optimizer (MOALO), with decision-making strategies such as desirability function and VIKOR technique. Results revealed that optimal MXene ratios depend on nanomaterial mass fraction (MF) and temperature, with optimal conditions clustering around 60 °C, MF of 1.5–2 wt%, and MXene ratios of 0.47–0.5. Decision-making analysis highlighted the trade-offs between TC and DV based on varying weight distributions. This research provides a cost-effective methodology to optimize nanofluids for solar energy applications with high precision, reducing computational and laboratory costs.