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Identifying the key system parameters of the organic Rankine cycle using the principal component analysis based on an experimental database

机译：使用基于实验数据库的主成分分析来识别有机朗肯循环的关键系统参数

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摘要

The organic Rankine cycle (ORC) is a promising technology for medium-and-low temperature heat utilization. However, the mechanism of how system parameters affect output have been investigated very little in the experimental aspect. Experimental investigation on the impact of each system parameter on system performance requires decoupling these system parameters. In this work, a series of experiments are conducted on a 10 kW scale ORC experiment setup. Statistical analysis is performed to identify a key parameter subset based on an experimental database. 6 system parameters, including temperature (Te) and pressure (pe) at the evaporator outlet, temperature (Tc) and pressure (pc) at the condenser inlet, expander shaft efficiency (eta SSE), and working fluid pump efficiency (eta P) are obtained. Combined with the ORC net power output and thermal efficiency, an experimental database of system operation conditions is constructed. Subsequently, the principal component analysis (PCA) of ORC is conducted based on the experimental database. Prediction models are developed using multi-linear regression (MLR), back propagation artificial neural network (BP-ANN), and support vector regression (SVR). Finally, accounting for the prediction performance of models and system parameter intercorrelation behavior, the key parameter subset is determined with the exhaustive feature selection method. The results imply that the key parameter subset is (pe, eta P, pc, eta SSE). Further removing or including more system parameters would reduce the accuracy of prediction models. In addition, the MLR models are slightly less accurate than the more sophisticated BP-ANN and SVR models.
机译：有机朗肯循环（ORC）是中低温热利用的有希望的技术。然而，在实验方面，系统参数如何影响输出的机制。对每个系统参数对系统性能的影响的实验研究需要解耦这些系统参数。在这项工作中，在10千瓦级兽人实验设置上进行了一系列实验。执行统计分析以识别基于实验数据库的关键参数子集。 6系统参数，包括在冷凝器入口的蒸发器出口，温度（Tc）和压力（PC）处的温度（TE）和压力（PE），扩展器轴效率（ETA SSE）和工作流体泵效率（ETA P）获得。结合ORC净功率输出和热效率，构建了系统操作条件的实验数据库。随后，基于实验数据库进行ORC的主成分分析（PCA）。使用多线性回归（MLR），后传播人工神经网络（BP-ANN）和支持向量回归（SVR）开发预测模型。最后，占模型和系统参数互相关行为的预测性能，用穷举特征选​​择方法确定键参数子集。结果意味着关键参数子集是（PE，ETA P，PC，ETA SSE）。进一步删除或包括更多系统参数将降低预测模型的准确性。此外，MLR模型比更复杂的BP-ANN和SVR模型略低于准确。

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  • 来源
    《Energy Conversion & Management》 |2021年第7期|114252.1-114252.8|共8页
  • 作者

    Yan Dong; Yang Fubin; Yang Fufang; Zhang Hongguang; Guo Zhiyu; Li Jian; Wu Yuting;

  • 作者单位

    Beijing Univ Technol Fac Environm & Life Beijing Key Lab Heat Transfer & Energy Convers Key Lab Enhanced Heat Transfer & Energy Conservat Beijing 100124 Peoples R China;

    Beijing Univ Technol Fac Environm & Life Beijing Key Lab Heat Transfer & Energy Convers Key Lab Enhanced Heat Transfer & Energy Conservat Beijing 100124 Peoples R China;

    Tech Univ Denmark Dept Chem & Biochem Engn Ctr Energy Resources Engn CERE DK-2800 Lyngby Denmark;

    Beijing Univ Technol Fac Environm & Life Beijing Key Lab Heat Transfer & Energy Convers Key Lab Enhanced Heat Transfer & Energy Conservat Beijing 100124 Peoples R China;

    Beijing Univ Technol Fac Environm & Life Beijing Key Lab Heat Transfer & Energy Convers Key Lab Enhanced Heat Transfer & Energy Conservat Beijing 100124 Peoples R China;

    Tsinghua Univ Beijing Key Lab CO2 Utilizat & Reduct Technol Key Lab Thermal Sci & Power Engn MOE Beijing 100084 Peoples R China;

    Beijing Univ Technol Fac Environm & Life Beijing Key Lab Heat Transfer & Energy Convers Key Lab Enhanced Heat Transfer & Energy Conservat Beijing 100124 Peoples R China;

  • 收录信息 美国《科学引文索引》(SCI);美国《工程索引》(EI);
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

    Organic Rankine cycle; Experimental analysis; Principal component analysis; Machine learning; Key parameter subset;

    机译：有机朗肯循环;实验分析;主成分分析;机器学习;关键参数子集;

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