Experimental investigation and neural network modeling of binary eutectic/expanded graphite composites for medium temperature thermal energy storage

The main objective of this present research work is to investigate the feasibility of LiNO3 + NaCl/expanded graphite (EG) composite phase change material for medium-temperature thermal energy storage systems. EG was used as supporting material to enhance the eutectic PCM samples thermal conductivity. The XRD, FTIR and SEM results reveal that EG particles are uniformly dispersed to the PCM material and show better chemical stability. The phase transition temperature and latent heat values of pure eutectic PCM and composite eutectic PCMs are experimentally measured with the help of differential scanning calorimetry (DSC). The calculated thermal conductivity intensification of composite PCM with 9% EG composition is 5.75, significantly more than pure PCM salt. The composite PCM showed good thermal reliability performance even after 500 thermal cycles. The charging time of the PCM significantly decreases with EG loading. The corrosion rate of five metal specimens is determined when embedded in pure PCM and composite PCM samples at more than phase transition temperature for 1440 h. The metal specimens embedded in composite PCM show good corrosion stability. Among all the selected metal specimens, stainless steel 316 L showed better corrosion resistivity in both PCM samples. Furthermore, an artificial neural network model is developed to predict the DSC output parameters such as temperature and heat flow at various EG loading (%), heating rate, and conversion points.