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Quantitative analysis and multi-objective optimization of Brayton cycle based on artificial neural network method
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Xinzhuang GU, Qingxin LI, Ming SHI, Ruirui YANG, Yue YIN, Hang YANG, Wenming MA, Wuqing WEI, Shuochen ZHOU, Haopeng CHEN
Thermal Power Generation | 2025, 54(11) : 42 - 48
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Thermal Power Generation | 2025, 54(11): 42-48
• Advanced power cycle technology •
Quantitative analysis and multi-objective optimization of Brayton cycle based on artificial neural network method
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Xinzhuang GU, Qingxin LI, Ming SHI, Ruirui YANG, Yue YIN, Hang YANG, Wenming MA, Wuqing WEI, Shuochen ZHOU, Haopeng CHEN
Affiliations
  • Shanghai Investigation, Design & Research Institute Co, Ltd, Shanghai 200335, China
Published: 2025-11-25 doi: 10.19666/j.rlfd.202501012
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The Brayton cycle is widely recognized as a key power cycle in the third-generation solar thermal power generation technology. Leveraging the strengths of artificial neural network methods for importance evaluation and quantitative analysis, this research employs a control variable approach to identify critical parameters, including turbine inlet temperature and compression ratio, from a range of operating parameters. In this method, the significance of parameters increases as the R2 value decreases. Notably, when excluding these key parameters, the R2 values fall to 0.57 and 0.64, respectively, both are lower than other operating parameters. Furthermore, the quantitative analysis of output power in the Brayton cycle yields exceptional results, achieving an R2 value exceeding 0.999. The R2 values for thermal efficiency and input heat are 0.992 and 0.988, respectively. Finally, the multi-objective optimization results suggest optimal settings of 500 ℃ for turbine inlet temperature and 2.19 for the compression ratio, corresponding to a thermal efficiency of 46.58%, output power of 100.97 kJ/kg, and input heat of –176.5 kJ/kg. This study offers valuable insights for the operational efficiency and performance assessment of the Brayton cycle in solar thermal power plants.

Brayton cycle  /  neural network method  /  thermal efficiency  /  quantitative analysis  /  multi-objective optimization
Xinzhuang GU, Qingxin LI, Ming SHI, Ruirui YANG, Yue YIN, Hang YANG, Wenming MA, Wuqing WEI, Shuochen ZHOU, Haopeng CHEN. Quantitative analysis and multi-objective optimization of Brayton cycle based on artificial neural network method[J]. Thermal Power Generation, 2025 , 54 (11) : 42 -48 . DOI: 10.19666/j.rlfd.202501012
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  • Science and Technology Commission of Shanghai Municipality(23DZ1201003; 2024QT(83)-010; 2024QT(81)-011)
  • The First Batch of Non Directional Guidance Projects in 2024 of the China Three Gorges Corporation(2025QT(82)-001)
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Year 2025 volume 54 Issue 11
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Article Info
doi: 10.19666/j.rlfd.202501012
  • Receive Date：2025-01-13
  • Online Date：2026-01-13
  • Published：2025-11-25
Article Data
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  • Received：2025-01-13
Funding
Science and Technology Commission of Shanghai Municipality(23DZ1201003; 2024QT(83)-010; 2024QT(81)-011)
The First Batch of Non Directional Guidance Projects in 2024 of the China Three Gorges Corporation(2025QT(82)-001)
Affiliations
    Shanghai Investigation, Design & Research Institute Co, Ltd, Shanghai 200335, China
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https://castjournals.cast.org.cn/joweb/rlfd/EN/10.19666/j.rlfd.202501012
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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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