烷烃临界温度预测方法的研究  

The study of prediction of critical temperature of alkanes

作　　者：董廷泽[1] 邓文生[1] 伍科[1] 

机构地区：[1]北京理工大学化工与环境学院化工原理教研室,北京100081

出　　处：《计算机与应用化学》2010年第7期979-982,共4页Computers and Applied Chemistry

摘　　要：从马氏手册中收集了69个烷烃分子,采用ChemOffice软件中的MM2方法优化分子结构,以此为初始构象,运用Gaussian03程序中的RHF/6-31G方法优化结构,得到分子的优势构象,并基于分子图形学技术,计算出7个结构描述子。采用人工神经网络和支持向量机,选定其中60个烷烃分子作样本集,其余9个作预测集,建立分子结构和临界温度的定量关系模型,预测结果的均方根误差与Joback法相近,能够满足工程需要;引入正常沸点数据后,预测结果的均方根误差比Joback法减少37%以上,表明本文提出的方法优点显著。The molecule structure was first optimized useing MM2 method,a more precise optimization was done using Gaussian03 with the RHF/6-31G method,obtaining the dominant conformation of molecular.Seven structure desccriptors were calculated based on the technology of molecule graphics.This paper chose 60 alkanes as the sample set,using artificial neural network and support vector machine to establish QSPR model to predict the critical temperature,the root-mean-square error of prediction results were close to the group contribution method;When the normal boiling point was introduced,the RMS error of prediction values was reduced over 37%comparing that of Joback method,which indicated that our method is promising.

关 键 词：临界温度 描述子 Joback基团贡献法 人工神经网络 支持向量机 

分 类 号：TQ015.9[化学工程] TP391.9[自动化与计算机技术—计算机应用技术]