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

Area of Science:

• Materials Science
• Renewable Energy Engineering
• Chemical Engineering

Background:

• MXene-based aqueous ionic liquids show potential for solar energy systems but require property optimization.
• Simultaneous optimization of thermal conductivity (TC), dynamic viscosity (DV), and specific heat capacity (SHC) is crucial for efficient heat transfer.
• Current understanding of MXene-based nanofluid thermophysical properties needs further data-driven exploration.

Purpose of the Study:

• To optimize MXene-based aqueous ionic liquids for enhanced thermophysical properties in solar energy applications.
• To investigate the impact of system temperature and MXene mass fraction (MF) on heat transfer performance.
• To develop a data-driven methodology for optimizing nanofluid properties.

Main Methods:

• Utilized Response Surface Methodology (RSM) for predictive modeling of thermophysical properties.
• Applied multi-objective optimization algorithms: Enhanced Hill Climbing (EHC), Non-Dominated Sorting Genetic Algorithm II (NSGA-II), and Multi-Objective Generalized Normal Distribution Optimizer (MOGNDO).
• Employed weighted decision-making tools (Desirability Function, MARCOS method) to refine optimal solutions.

Main Results:

• Cubic RSM models accurately predicted the relationships between input variables and thermophysical responses.
• MOGNDO provided superior Pareto front coverage and solution diversity compared to NSGA-II.
• Optimal performance achieved at 50°C with MXene mass fraction between 0.00188% and 0.2%.

Conclusions:

• Achieved optimal thermal conductivity up to 0.797 W/m·K, DV between 2.028–2.157 mPa·s, and SHC from 2.192–2.503 J/g·K.
• The data-driven methodology offers a scalable strategy for optimizing MXene-based nanofluids for solar systems.
• Findings contribute to advancing renewable energy solutions and provide a framework for engineering optimization problems.