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

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.

Automated summary

The study investigates the impact of system parameters on output in organic Rankine cycle, and determines a key parameter subset through experiments and statistical analysis. Prediction models are developed using machine learning algorithms, with the key parameter subset identified as (pe, eta P, pc, eta SSE).