Experimental and machine learning-based analysis of encapsulated lauric acid in geopolymer composites for thermal energy storage

Phase change materials (PCM) have demonstrated enhanced efficacy as heat storage materials and actively respond to heating demands. The main aim of utilizing PCM is to enhance thermal inertia and decrease energy usage in heating and cooling applications. In this research investigation, specific attention is given to the methods to stabilize organic lauric acid (LA) as PCM by encapsulation in calcium cross-linked alginate (LA@Alg) and silica shell (LA@SiO 2 ) to evaluate the thermal regulation performance in fly ash-ground granulated blast furnace slag geopolymer. Results ensured that LA@SiO 2 exhibited a greater latent heat capacity of 133.1 J/g than that of LA@Alg, which was 15.82 J/g with the lower mass ratio of LA in alginate. The liquid migration test results showed the tightness of LA@SiO 2 , which remained stable until 520 thermal cycles, when compared with that of LA@Alg with 127 cycles. Geopolymer incorporated with LA@ SiO 2 and LA@Alg at 20 wt.% effectively reduced the heat transfer by lowering the indoor temperature by 6 °C and 3 °C, respectively. Furthermore, FTIR and strength tests revealed that the chemical compatibility and loss of strength are as low as 14%. A data-driven Machine Learning was employed, and reliable predictions were obtained for the voting ensemble mode, which showed a greater accuracy of 0.91. This investigation is specifically intended to contribute to the field of using encapsulated lauric acid for target temperature control. • Attempts have been made for the stabilization of LA in a silica and calcium alginate shell. • LA@SiO 2 showed a maximum encapsulation efficiency of 75% to that of LA@Alg. • A temperature difference of 6°C was exhibited by Gp-LA@SiO 2 20% . • Maximum prediction accuracy of 91% was observed for the Voting Ensemble model.