化工学报2023，Vol.74Issue(11)：4466-4474,9.DOI:10.11949/0438-1157.20230942
图神经网络预测烃类工质的热力学性质
Prediction of thermodynamic properties of hydrocarbon working fluids by graph neural network models
摘要
Abstract
Organic Rankine cycle(ORC)has attracted much attention due to its ability to convert low-grade heat to electricity.One of the important tasks to promote the application of ORC is to find efficient and environmentally friendly working fluids to replace high-GWP(global warming potential)hydrochlorofluorocarbon(HCFC)and hydrofluorocarbon(HFC).In this article,a prediction model for the thermodynamic properties of ORC hydrocarbon working fluids based on graph neural networks(GNN)is constructed.GNN is used to learn the characteristics of molecular structure,and the combination of molecular structure characteristics and temperature is used to build a prediction model of molecular structure and properties using multilayer perceptron(MLP).The model is based on a training set of 2508 linear,cyclic,and aromatic hydrocarbons with carbon chain lengths ranging from 2 to 10.The obtained model achieves better prediction results than previous literature on predicting critical temperature,evaporation enthalpy and gas-phase and liquid-phase molar heat capacity.In addition,the resulting model was applied to predict the thermodynamic properties of over 430000 hydrofluoroolefins.关键词
热力学性质/预测/神经网络/ORC工质/氢氟烯烃Key words
thermodynamic properties/prediction/neural network/ORC working fluids/hydrofluoroolefin分类
化学化工引用本文复制引用
洪小东,董轩,林美金,廖祖维,任聪静,杨遥,蒋斌波,王靖岱,阳永荣..图神经网络预测烃类工质的热力学性质[J].化工学报,2023,74(11):4466-4474,9.基金项目
国家自然科学基金项目(U22A20415) （U22A20415）
浙江省尖兵领雁计划项目(2022C01SA442617) （2022C01SA442617）
