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Thermal behavior evolution of single tank molten salt energy storage system and the temperature regression prediction
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Yong SUN1, Xiaobiao FU1, Baoju LI1, Hongyun HU2, Yuhao LIU2, Qiqi DAI2, Jiakun FANG3
Thermal Power Generation | 2025, 54(10) : 21 - 30
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Thermal Power Generation | 2025, 54(10): 21-30
• Special topic on energy storage and power generation coupling technology •
Thermal behavior evolution of single tank molten salt energy storage system and the temperature regression prediction
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Yong SUN1, Xiaobiao FU1, Baoju LI1, Hongyun HU2, Yuhao LIU2, Qiqi DAI2, Jiakun FANG3
Affiliations
  • 1.State Grid Jilin Electric Power Co., Ltd., Changchun 130021, China
  • 2.School of Energy and Power Engineering, Huazhong University of Science and Technology, Wuhan 430074, China
  • 3.School of Electrical and Electronic Engineering, Huazhong University of Science and Technology, Wuhan 430074, China
Published: 2025-10-25 doi: 10.19666/j.rlfd.202412267
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Molten salt energy storage technology is widely used in solar thermal power generation due to its high thermal capacity and good thermal stability. To optimize the influence of key operating parameters on energy storage efficiency, numerical simulation methods are used to analyze the mechanism of input velocity, initial temperature, temperature difference and other parameters on the formation of thermocline and heat storage efficiency at different horizontal positions. The results show that, increasing the temperature difference and the input speed can significantly promote the development of the thermocline, and increase the heat storage efficiency by more than 10%. The parameter optimization algorithm based on response surface methodology identifies an optimized parameter combination, which improves the heat storage efficiency by a maximum of 16.3 percentage points compared to the previous simulations. At the same time, to quickly and accurately predict the operating temperature of the system, three machine learning models are compared, and it finds out that the random forest model has the best prediction with an accuracy rate of 98.78%. The research results provide theoretical basis and application reference for the optimization design of molten salt energy storage systems.

molten salt energy storage  /  heat storage efficiency  /  parameter optimization  /  temperature prediction
Yong SUN, Xiaobiao FU, Baoju LI, Hongyun HU, Yuhao LIU, Qiqi DAI, Jiakun FANG. Thermal behavior evolution of single tank molten salt energy storage system and the temperature regression prediction[J]. Thermal Power Generation, 2025 , 54 (10) : 21 -30 . DOI: 10.19666/j.rlfd.202412267
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  • National Key Research and Development Program(2022YFB2404001)
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Year 2025 volume 54 Issue 10
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Article Info
doi: 10.19666/j.rlfd.202412267
  • Receive Date：2024-12-16
  • Online Date：2026-03-05
  • Published：2025-10-25
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  • Received：2024-12-16
Funding
National Key Research and Development Program(2022YFB2404001)
Affiliations
    1.State Grid Jilin Electric Power Co., Ltd., Changchun 130021, China
    2.School of Energy and Power Engineering, Huazhong University of Science and Technology, Wuhan 430074, China
    3.School of Electrical and Electronic Engineering, Huazhong University of Science and Technology, Wuhan 430074, China
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https://castjournals.cast.org.cn/joweb/rlfd/EN/10.19666/j.rlfd.202412267
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表12种不同金属材料的力学参数
科
Family		 属数
Number of
genus		 种数
Number of
species		 占总种数比例
Percentage of
total species (%)		 属
Genus		 种数
Number of
species		 占总种数比例
Percentage of total
species (%)
鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
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