石油化工2024，Vol.53Issue(4)：518-524,7.DOI:10.3969/j.issn.1000-8144.2024.04.006
基于图对比学习网络的碳捕集利用与封存过程临界物性预测
Prediction of critical physical properties in carbon capture,utilization and storage based on graph contrastive learning
摘要
Abstract
In response to the high cost of traditional experimental and computational methods for obtaining the critical temperature(Tc)of compounds,the graph comparative learning(GCL)algorithm was applied to predict the Tc of crude oil components.The differences between the GCL algorithm and traditional computational models were compared by combining the existing Tc dataset with supplementary related data of crude oil components.The calculation results indicate that the GCL method can capture the characteristics of nodes and edges in the graph,while requiring less training data,making it suitable for predicting the properties of molecules.The GCL method shows higher prediction accuracy,and the encoding method of adjusting the 2D and 3D of molecular can improve the predictive performance of GCL.关键词
分子性质预测/图对比学习/碳捕集利用与封存/CO2驱油Key words
molecular property prediction/graph contrastive learning/carbon capture,utilization and storage/carbon dioxide flooding分类
化学化工引用本文复制引用
蔡一涵,崔乐雨,李欣,苏智青,何秀娟,李应成..基于图对比学习网络的碳捕集利用与封存过程临界物性预测[J].石油化工,2024,53(4):518-524,7.基金项目
中国石化集团公司重点研发项目(KL22055). （KL22055）
