Phase diagram calculation and neural network prediction of nitrate/nitrite molten salts with wide working temperature range for thermal storage system

Yuanyuan Wang, Zixuan Wang, Yuanwei Lu, Yuting Wu and Cancan Zhang

Energy, 2025, vol. 322, issue C

Abstract: Molten salt with wide temperature range is an excellent thermal energy storage medium, which is essential in concentrating solar power plants. In this work, the novel nitrate/nitrite molten salts for thermal energy storage (TES) were designed. The eutectic points of the NaNO3-KNO2 and NaNO3-NaNO2 systems were predicted by developing the back-propagation (BP) neural network optimized based on genetic algorithm (GA). In addition, the phase diagrams of the molten salts were calculated using the thermodynamic sub-regular solution model (SSM). The nitrate/nitrite thermodynamic database was supplemented. Based on experimental validation, both BP-GA and SSM methods were effective means of predicting the molten salts eutectic points, with maximum prediction errors of 7.9 % and 6.0 %, respectively. The liquid usage temperature ranges for NaNO3-KNO2 and NaNO3-KNO2 eutectic salts were 152.4–612.2 °C and 232.4–602.0 °C, respectively. The sensible heat storage density of NaNO3-KNO2 molten salt was 729.8 kJ/kg, which was about 63.5 % higher than the Solar salt. This work not only provided methods for screening target molten salt, but also provided the theoretical and experimental basis for the widespread application of molten salts for TES. In future work, we will further optimize the prediction algorithm and explore the long-term thermal stability of the preferred molten salts.

Keywords: Molten salt; Phase diagram computation; Machine learning; Wide working temperature range; Thermophysical properties (search for similar items in EconPapers)
Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:eee:energy:v:322:y:2025:i:c:s0360544225012800

DOI: 10.1016/j.energy.2025.135638

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