Quantitative analysis and multi-objective optimization of Brayton cycle based on artificial neural network method

顾新壮; 李青芯; 石明; 杨瑞睿; 殷岳; 杨航; 马文明; 魏务卿; 周朔晨; 陈昊芃

doi:10.19666/j.rlfd.202501012

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Quantitative analysis and multi-objective optimization of Brayton cycle based on artificial neural network method

Thermal Power Generation Vol. 54, Issue 11, Pages: 42-48(2025)

更新时间：2026-08-11

- Quantitative analysis and multi-objective optimization of Brayton cycle based on artificial neural network method
- 作者机构：
  
  上海勘测设计研究院有限公司
- 作者简介：
- 基金信息：
- DOI：10.19666/j.rlfd.202501012
  CLC： TM615;TP183
- Published：2025
- 稿件说明：
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[1]顾新壮,李青芯,石明,等.基于神经网络方法的布雷顿循环定量分析及多目标优化[J],2025,54(11):42-48.
[1]顾新壮,李青芯,石明,等.基于神经网络方法的布雷顿循环定量分析及多目标优化[J],2025,54(11):42-48. DOI： 10.19666/j.rlfd.202501012.

DOI：10.19666/j.rlfd.202501012.

摘要

布雷顿循环被公认为第三代太阳能热发电技术的动力循环，得益于神经网络方法具有重要度评估和定量分析等优势。首先，采用控制变量法从多个运行参数中筛选出透平机进口温度和压缩比等重要参数，控制变量法中重要度随着R

值的降低而增加，当不含上述重要参数时，其对应的R

值分别降低到0.57和0.64，均低于其余运行参数；然后，采用布雷顿循环中的输出功定量分析结果，其R

值大于0.999，其中热效率和输入热量的R

值分别为0.992和0.988；最后，通过多目标优化结果所推荐的透平机进口温度和压缩比值分别为500℃和2.19，相对应的热效率、输出功和输入热量分别为46.58%、100.97 k J/kg和–176.5 kJ/kg。该研究可作为光热电站布雷顿循环的实际运行和性能研究的参考。

Abstract

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 R

value decreases. Notably

when excluding these key parameters

the R

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 R

value exceeding 0.999. The R

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.

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Keywords

references

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