摘要
有机朗肯循环(ORC)因其低温热电转换的能力而备受关注,寻找高效环保工质,以代替具有较高全球变暖潜能值(GWP)的氢氯氟烃(HCFC)和氢氟烃(HFC),是推动ORC应用的重要任务之一。构建一个基于图神经网络(GNN)的ORC烃类工质热力学性质预测模型,通过图神经网络学习分子结构的特征,并将分子结构信息与温度结合,利用多层感知机(MLP)构建热力学性质预测模型。模型基于2508种C_(2)~C_(10)的链状烃、环烃和芳香烃分子构建训练集,所得模型在预测临界温度、蒸发焓、气相摩尔热容和液相摩尔热容上均取得良好效果,优于文献的预测效果。此外,应用所得模型预测了超43万个氢氟烯烃(HFO)的热力学性质。
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 wasapplied to predict the thermodynamic properties of over 430000 hydrofluoroolefins.
作者
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
HONG Xiaodong;DONG Xuan;LIN Meijin;LIAO Zuwei;REN Congjing;YANG Yao;JIANG Binbo;WANG Jingdai;YANG Yongrong(Engineering Research Center of Functional Materials Intelligent Manufacturing of Zhejiang Province,ZJU-Hangzhou Global Scientific and Technological Innovation Center,Zhejiang University,Hangzhou 311215,Zhejiang,China;State Key Laboratory of Chemical Engineering,Zhejiang University,Hangzhou 310027,Zhejiang,China;Ningbo Innovation Center,Zhejiang University,Ningbo 315100,Zhejiang,China)
出处
《化工学报》
EI
CSCD
北大核心
2023年第11期4466-4474,共9页
CIESC Journal
基金
国家自然科学基金项目(U22A20415)
浙江省尖兵领雁计划项目(2022C01SA442617)。
关键词
热力学性质
预测
神经网络
ORC工质
氢氟烯烃
thermodynamic properties
prediction
neural network
ORC working fluids
hydrofluoroolefin
