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

更新时间：2025-11-21

- Quantitative analysis and multi-objective optimization of Brayton cycle based on artificial neural network method
- Thermal Power Generation Issue 11, Pages: 42-48(2025)
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- DOI：10.19666/j.rlfd.202501012
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- Published：2025
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顾新壮, 李青芯, 石明, et al. Quantitative analysis and multi-objective optimization of Brayton cycle based on artificial neural network method[J]. Thermal Power Generation, 2025, (11): 42-48.
DOI：

顾新壮, 李青芯, 石明, et al. Quantitative analysis and multi-objective optimization of Brayton cycle based on artificial neural network method[J]. Thermal Power Generation, 2025, (11): 42-48. 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~2值的降低而增加，当不含上述重要参数时，其对应的R~2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~2值大于0.999，其中热效率和输入热量的R~2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。

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