Optimization of Thermal Performance in Lauric Acid‐Based Phase Change Materials Using a Priority Clustering Approach

ABSTRACT This study investigates the thermal properties of lauric acid (LA) as a phase change material (PCM) using the K ‐Means clustering method to analyze the melting characteristics. This study focuses on the optimization of PCMs using a hybrid methodology of analytic hierarchy process (AHP) and K ‐Means clustering. LA, enhanced with zinc oxide (ZnO) nanoparticles, was evaluated for its thermal performance. LA's suitability as a PCM is evaluated based on initial temperature, heating rate, final temperature, and time to melt. AHP was employed to determine the weightage for three critical outcomes: latent heat, melting point, and thermal conductivity. The weightages assigned were 59%, 31%, and 11%, respectively, reflecting the relative importance of each outcome in assessing the efficiency of LA as a PCM. Furthermore, K ‐Means clustering was then applied to categorize the data based on these weighted outcomes. AHP was utilized to determine the weightage of input parameters, assigning 27% to initial temperature, 15% to heating rate, and 22% to final temperature, underscoring their significance in the analysis. The optimal input conditions identified were an initial temperature of 24.8°C, a ieating rate of 5.6°C/min, a final temperature of 81.4°C, and a time to melt of 10.6 min. These conditions resulted in optimal outcomes of 208 J/g for latent heat, a melting point of 80.9°C, and a thermal conductivity of 0.21 W/m·K. This hybrid approach provides a robust framework for optimizing PCM performance, facilitating enhanced thermal energy storage and release in practical applications.