Optimization of MXene-based aqueous ionic liquids for solar systems using conventional and AI-based techniques.

MXene-based aqueous ionic liquids hold significant promise for enhancing heat transfer in solar energy systems. However, their full potential remains underexplored, particularly concerning the simultaneous optimization of key thermophysical properties such as thermal conductivity (TC), dynamic viscosity (DV), and specific heat capacity (SHC). This study employs an integrated data-driven approach to optimize MXene-based aqueous ionic liquids by varying system temperature and MXene mass fraction (MF). Response surface methodology (RSM) is used for predictive modeling, while enhanced hill climbing (EHC), non-dominated sorting genetic algorithm II (NSGA-II), and the multi-objective generalized normal distribution optimizer (MOGNDO) are applied for multi-objective optimization. Weighted decision-making tools, including the desirability function and the MARCOS method, refine the selection of optimal solutions. The cubic RSM models effectively captured the relationships between input variables and responses, facilitating accurate optimization. MOGNDO demonstrated broader solution diversity and more comprehensive Pareto front coverage compared to NSGA-II. Optimal thermophysical performance was observed at 50 °C with MF ranging from 0.00188 to 0.2%. Predicted optimal values include TC up to 0.797 W/m K, DV between 2.028 and 2.157 mPa s, and SHC ranging from 2.192 to 2.503 J/g K. The proposed methodology offers a reliable and scalable strategy for optimizing MXene-based nanofluids, contributing to improved thermo-hydraulic performance in solar systems. These findings support the advancement of renewable energy solutions and provide a robust framework applicable to broader engineering optimization problems.