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

doi:10.1016/j.cjche.2018.06.031

中国化学工程学报 ›› 2019, Vol. 27 ›› Issue (4): 835-844.DOI: 10.1016/j.cjche.2018.06.031

• Separation Science and Engineering • 上一篇下一篇

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

Yixin Ma¹, Kang Ma², Huixin Wang³, Xueli Geng², Jun Gao¹, Zhaoyou Zhu², Yinglong Wang²

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

收稿日期:2018-04-18 修回日期:2018-06-15 出版日期:2019-04-28 发布日期:2019-06-14
通讯作者: Yinglong Wang
基金资助:
Supported by the National Natural Science Foundation of China (21776145, 21676152) and Key Research Project of Shandong Province (2016GSF116004).

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

Yixin Ma¹, Kang Ma², Huixin Wang³, Xueli Geng², Jun Gao¹, Zhaoyou Zhu², Yinglong Wang²

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

Received:2018-04-18 Revised:2018-06-15 Online:2019-04-28 Published:2019-06-14
Contact: Yinglong Wang
Supported by:
Supported by the National Natural Science Foundation of China (21776145, 21676152) and Key Research Project of Shandong Province (2016GSF116004).

摘要/Abstract

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.

关键词: QSPR, Azeotropic temperature, Azeotropic composition, Genetic function approximation, Binary azeotropes

Abstract: 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.

Key words: QSPR, Azeotropic temperature, Azeotropic composition, Genetic function approximation, Binary azeotropes

Yixin Ma, Kang Ma, Huixin Wang, Xueli Geng, Jun Gao, Zhaoyou Zhu, Yinglong Wang. QSPR modeling of azeotropic temperatures and compositions for binary azeotropes containing lower alcohols using a genetic function approximation[J]. 中国化学工程学报, 2019, 27(4): 835-844.

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

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

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