Novel prediction method of process and system performance for organic Rankine cycle based on neural network

Yupeng WANG; Junwei LIANG; Xianglong LUO; Yifan LI; Jianyong CHEN; Ying CHEN

doi:10.11949/0438-1157.20181521

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Novel prediction method of process and system performance for organic Rankine cycle based on neural network

更新时间：2026-03-25

- Novel prediction method of process and system performance for organic Rankine cycle based on neural network
- CIESC Journal Vol. 70, Issue 9, Pages: 3256-3266(2019)
- 作者机构：
  
  广东工业大学材料与能源学院,广东,广州,510006
- 作者简介：
- 基金信息：
- DOI：10.11949/0438-1157.20181521
  CLC： TK112
- Online First：05 September 2019，
  
  Published：05 September 2019
- 稿件说明：
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Yupeng WANG, Junwei LIANG, Xianglong LUO, et al. Novel prediction method of process and system performance for organic Rankine cycle based on neural network[J]. CIESC Journal, 2019, 70(9): 3256-3266.
DOI：

Yupeng WANG, Junwei LIANG, Xianglong LUO, et al. Novel prediction method of process and system performance for organic Rankine cycle based on neural network[J]. CIESC Journal, 2019, 70(9): 3256-3266. DOI： 10.11949/0438-1157.20181521.

摘要

有机朗肯循环（ORC）是中低温热能-电能转换中最具前景的技术之一，近年来受到越来越多的关注。工质是ORC实现的载体，由于热源及可选工质的多样性，工质筛选及系统的优化对于提升ORC综合性能非常重要，而物性及过程特性的准确预测是关键。提出了基于神经网络-基团贡献法的ORC系统性能计算方法，建立了涵盖11个基团的基团表，从REFPROP中调用51种工质7958组数据进行神经网络训练，获得了ORC中各个热力过程能量转换和熵差的计算关联式。计算了21种常用工质在1584组工况下的ORC系统性能，并与基于传统方法计算的ORC系统性能参数进行了对比。结果显示预测得到的ORC系统热效率、净输出功和系统?效率与用REFPROP计算得出的结果相比误差分别为1.01%、1.02%和1.61%，相比传统方法，预测精度有显著提高。

Abstract

Organic Rankine cycle (ORC) is one of the most promising technologies for medium and low temperature thermal energy-electric energy conversion

and has received more and more attention in recent years. Working fluid is the carrier for energy transport or conversion in the ORC. Because of the diversity of the heat source and working substances

the screening of working fluids and the optimization of the system are very important to improve the comprehensive performance of the ORC. Accurate prediction of working fluid properties is significant for the accurate prediction and optimization of the ORC performance. Based on the artificial neural network and group contribution method (ANN-GCM)

a prediction method for the ORC performance is presented. A group table covering 11 groups established

7958 of data are derived from REFPROP for ANN training

obtaining the correlation of the energy transform and entropy difference in DRC. The performance of the ORC is tested by using 21 common refrigerants in 1584 working conditions. The error of predicting the thermal efficiency

output power

and exergetic efficiency of the ORC system with the experimental data is 1.01%

1.02% and 1.61%. Comparing with the traditional method

the prediction accuracy is significantly improved.

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