化工学报2019，Vol.70Issue(9)：3256-3266,11.DOI:10.11949/0438-1157.20181521
基于神经网络的有机朗肯循环过程及循环性能计算方法
Novel prediction method of process and system performance for organic Rankine cycle based on neural network
摘要
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.关键词
神经网络/热力学性质/预测/基团贡献法/有机朗肯循环Key words
neural networks/thermophysical properties/prediction/group contribution method/organic Rankine cycle分类
能源科技引用本文复制引用
王羽鹏,梁俊伟,罗向龙,李逸帆,陈健勇,陈颖..基于神经网络的有机朗肯循环过程及循环性能计算方法[J].化工学报,2019,70(9):3256-3266,11.基金项目
国家自然科学基金项目(51476037) （51476037）
广东省应用型科技研发专项资金项目(2016B020243010) （2016B020243010）
