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

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

*Corresponding author for this work

Research output: Contribution to journalJournal articleResearchpeer-review

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Abstract

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 (ηSSE), and working fluid pump efficiency (ηP) 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 inter-correlation behavior, the key parameter subset is determined with the exhaustive feature selection method. The results imply that the key parameter subset is (Pe,ηP,Pc,ηSSE). 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.
Original languageEnglish
Article number114252
JournalEnergy Conversion and Management
Volume240
Number of pages8
ISSN0196-8904
DOIs
Publication statusPublished - 2021

Keywords

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

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