QSPR modeling of azeotropic temperatures and compositions for binary azeotropes containing lower alcohols using a genetic function approximation  

作　　者：Yixin Ma Kang Ma Huixin Wang Xueli Geng Jun Gao Zhaoyou Zhu Yinglong Wang 

机构地区：[1]College of Chemical and Environmental Engineering, Shandong University of Science and Technology, Qingdao 266590, China [2]College of Chemical Engineering, Qingdao University of Science and Technology, Qingdao 266042, China [3]National Registration Center for Chemicals, SINOPEC Research Institute of Safety Engineering, Qingdao 266071, China

出　　处：《Chinese Journal of Chemical Engineering》2019年第4期835-844,共10页中国化学工程学报（英文版）

基　　金：Supported by the National Natural Science Foundation of China(21776145,21676152);Key Research Project of Shandong Province(2016GSF116004)

摘　　要：Binary azeotropes, which contain two chemicals with a relative volatility of 1, are very common in the chemical industry. Understanding azeotropes is essential for effectively separating binary azeotropes containing lower alcohols. Experimental techniques and ab initio approaches can produce accurate results;however, these two processes are time consuming and labor intensive. Although thermodynamic equations such as UNIFAC are widely used, experimental values are required, and it is difficult to choose the best groups to represent a complex system. Because of their high efficiency and fast calculation speed, quantitative structure–property relationship(QSPR) tools were used in this work to predict the azeotropic temperatures and compositions of binary azeotropes containing lower alcohols. The QSPR models for 64 binary azeotropes based on centroid approximation and weighted-contribution-factor approximation were established using the genetic function approximation(GFA) procedure in Materials Studio software, and a leave-one-out cross-validation procedure was conducted.External tests of an additional 16 azeotropes were also investigated, and high determination coefficient values were obtained. The best QSPR models were explained in terms of the molecular structure of the azeotropes,and good predictive ability was obtained within acceptable prediction error levels.Binary azeotropes, which contain two chemicals with a relative volatility of 1, are very common in the chemical industry. Understanding azeotropes is essential for effectively separating binary azeotropes containing lower alcohols. Experimental techniques and ab initio approaches can produce accurate results; however, these two processes are time consuming and labor intensive. Although thermodynamic equations such as UNIFAC are widely used, experimental values are required, and it is difficult to choose the best groups to represent a complex system. Because of their high efficiency and fast calculation speed, quantitative structure–property relationship(QSPR) tools were used in this work to predict the azeotropic temperatures and compositions of binary azeotropes containing lower alcohols. The QSPR models for 64 binary azeotropes based on centroid approximation and weighted-contribution-factor approximation were established using the genetic function approximation(GFA) procedure in Materials Studio software, and a leave-one-out cross-validation procedure was conducted.External tests of an additional 16 azeotropes were also investigated, and high determination coefficient values were obtained. The best QSPR models were explained in terms of the molecular structure of the azeotropes,and good predictive ability was obtained within acceptable prediction error levels.

关 键 词：QSPR AZEOTROPIC temperature AZEOTROPIC composition Genetic function approximation BINARY AZEOTROPES 

分 类 号：TQ[化学工程]