Prediction of thermophysical properties of chlorine eutectic salts via artificial neural network combined with polar bear optimization

The eutectic salt as latent heat storage (LHS) media has been widely employed in various fields, but its thermophysical properties are difficult to be predicted accurately. Here, a prediction model of thermophysical properties of eutectic salts based on the backpropagation (BP) artificial neural network method combined with bio-inspired algorithms (polar bear optimization (PBO) or genetic algorithm (GA)) is proposed. The average prediction error of melting temperature of 26 chlorine eutectic salts is the lowest by employing the BP-PBO algorithm, which is reduced by 42 % and 38 % compared to the BP algorithm and BP-GA, respectively. The advantage of BP-PBO is more obvious in predicting phase change enthalpy of chlorine eutectic salts, whose average error decreases by 71 % and 68 % compared with BP and BP-GA, respectively. For more intuitive comparisons, experiment measurements, BP-PBO, phase diagram, and molecular dynamics are used for characterizing thermophysical properties of fabricated KCl-LiCl eutectics. Again, BP-PBO demonstrates extremely accurate prediction performance and unique advantage of avoiding the difficulty of choosing appropriate phase equilibrium data or potential functions. The work provides accurate and fast prediction methods of thermophysical properties of chlorine eutectic salts and guides their designs for different applications.

Automated summary

A prediction model based on BP artificial neural network and bio-inspired algorithm was proposed to accurately predict the thermophysical properties of eutectic salts. The BP-PBO algorithm demonstrated the lowest average prediction error for melting temperature and phase change enthalpy of chlorine eutectic salts, with significant improvements compared to BP algorithm and BP-GA algorithm. The study provides accurate and fast prediction methods for the thermophysical properties of chlorine eutectic salts.