A comprehensive investigation and artificial neural network modeling of shape stabilized composite phase change material for solar thermal energy storage

Shape stabilized composite phase change materials (SSCPCM) are an attractive option for solar thermal energy storage applications. The objective of the present study is to understand and quantify the shape stability of a CPCM and its kinetics behavior. A Feed-Forward Backpropagation Neural Network (FFBPNN) algorithm is developed for the prediction of phase change temperature and heat flow of composite PCMs. Myristyl alcohol mixed with expanded graphite at various loadings (5%, 10%, and 15%) are used to study the shape stability of CPCM. The characterization of PCM and CPCM is done using Fourier-transform analysis, X-ray diffraction analysis, Scanning electron microscope, and Brunauer-Emmett-Teller (BET) analysis, Differential scanning calorimetry, and Thermogravimetric analysis. The thermophysical properties of PCM and CPCM are investigated using the C-Therm instrument. DSC experiments at different heating rates are performed to estimate the activation energy of the PCM and CPCM. The SSCPCM showed 11.82%, 11.70% higher activation energy than PCM as determined by Kissinger and Ozawa models. The activation energy of CPCM increases with the loading of expanded graphite. The charging and discharging time of SSCPCM is 29.8%, 23.3% lower than the PCM, respectively. The thermal conductivity of SSCPCM is 17.6 times higher than the PCM. Therefore, SSCPCM is an attractive solution to replace the conventional available PCMs, owing to no leakage problem and a considerable improvement in thermal conductivity. Artificial Neural Network (3-18-2) topology effectively predicts the phase change temperature and heat flow of shape stabilized composite PCMs with a coefficient of determination greater than 0.989, which is helpful in the design and selection of PCM for latent heat thermal energy storage systems.