Application of predictive models and thermal enhancement of phase change material with multi walled carbon nanotubes-Al2O3 hybrid nanoparticles

Abstract

Efficient thermal energy storage is critical for improving the reliability of renewable energy systems. This study investigates the thermal enhancement of paraffin-based phase change materials using hybrid nanoparticles composed of multi-walled carbon nanotubes (MWCNTs) and aluminum oxide (Al2O3). MWCNT-Al2O3 hybrids were synthesized with mixing ratios of 20:80 to 80:20 and dispersed in paraffin at concentrations of 0.5-2.0 wt%. Thermal conductivity and thermal diffusivity of hybrid nanoparticle enhanced phase change material were measured at 27 degrees C. Results revealed that the maximum thermal conductivity enhancement of 36.1% was achieved at 2 wt% with an 80:20 hybrid ratio. Enhanced latent heat of crystallization and latent heat of melting was achieved at 1 wt% for the mixing ratio of 20:80 and 80:20 ratios, respectively. Thermogravimetric analysis confirmed the thermal stability of all samples. Furthermore, two machine learning models, Extreme Gradient Boosting (XGBoost) and Sequential Minimal Optimization (SMO) were developed to predict thermal conductivity and latent heat properties. Statistical analysis and Taylor diagrams demonstrated that XGBoost consistently outperformed SMO, achieving higher test coefficient of determination (R2) values (0.995 vs. 0.980 for thermal conductivity), lower mean squared error, and superior Kling-Gupta efficiency.