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

doi:10.1016/j.enconman.2021.114252

Dong Yan, Fubin Yang + Show 5 more

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https://doi.org/10.1016/j.enconman.2021.114252

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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.

Highlights

Organic Rankine cycle (ORC) is an efficient and affordable technol ogy for the utilization of low-and-medium temperature heat source from the waste heat and renewable energy [1,2]
Statistical analysis is performed to identify a key parameter subset based on an experimental database. 6 system parameters, including temperature (Te) and pressure at the evaporator outlet, temperature (Tc) and pressure at the condenser inlet, expander shaft efficiency, and working fluid pump efficiency are obtained
Dickes et al [13] analyzed the influence of fluid charge on the organic Rankine cycle (ORC) system performance and pointed out that the fluid charge and receiver size are the key parameters

Summary

Introduction

Organic Rankine cycle (ORC) is an efficient and affordable technol ogy for the utilization of low-and-medium temperature heat source from the waste heat and renewable energy [1,2]. Yang et al [11] investigated the effect of working fluid characteristic parameters on the ORC thermodynamic performance based on a corresponding state approach. Unamba et al [12] investi gated the influence of heat source temperature, working fluid flow rates and pressure ratios on the ORC overall efficiency based on experiments. Dickes et al [13] analyzed the influence of fluid charge on the ORC system performance and pointed out that the fluid charge and receiver size are the key parameters. Sarkar [14] evaluated the effect of pinch point design and working fluid mass flow on the subcritical-supercritical ORC system performance. Branchini et al [16] performed a parametric investigation of ORC system and found the evaporation pressure was the most important key parameter which could improve system efficiency. Meng et al [17] investigated the influence of centrifugal pump efficiency on ORC system performance and found that increasing the pump efficiency could

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