基于定量结构-性质关系预测含水二元共沸物的共沸温度与组成

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摘要以125种含水二元共沸物为研究对象，基于定量结构-性质关系，对该类共沸物在常压下的共沸温度及组成进行了预测研究，分别建立了多个预测模型。首先，利用HyperChem 8.0软件绘制了纯组分的三维分子结构，并利用分子力学方法和量子力学半经验方法对分子结构进行优化；然后，利用Materials Studio 8.0软件计算纯物质的分子描述符；其次，利用遗传算法分别筛选出与共沸温度及组成最为密切的特征描述符；再运用多元线性回归方法建立了6个共沸温度预测模型及5个共沸组成预测模型，并对模型的稳定性、拟合能力和预测能力进行对比分析；最后，对最适宜模型分别进行内部验证、外部验证、应用域分析、与文献中同类模型及UNIFAC基团贡献法进行对比。结果表明：最适宜共沸温度/组成预测模型分别是利用8/5个特征描述符所建立的模型；其复相关系数，调整复相关系数，均方根误差，平均绝对误差，留一法交叉验证系数和外部验证系数分别为0.960 6/0.997 0、0.957 2/0.996 9、2.940 0/0.016 1、1.890 0/0.010 4、0.947 5/0.995 7和0.943 9/0.997 6，且模型的稳定性、预测能力和泛化能力均优同类模型。

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	曾行艳
	诸林
	吕利平
	李兵

关键词：定量结构-性质关系(QSPR)；共沸温度；共沸组成；预测；含水二元共沸物

Abstract： Several prediction models based on quantitative structure-property relationship, which was performed for estimating azeotropic temperature and composition of 125 binary azeotropes containing water at 101.325 kPa, were established. First of all, the three-dimensional molecular structure of each pure component was plotted by HyperChem 8.0 software, meanwhile the pre-optimization and further optimization of the molecular structure were implemented by the molecular mechanics method and the quantum mechanics semi-empirical method, respectively. Besides, the stable structures with minimum energy were imported into Material Studio 8.0 software to calculate descriptors. Moreover, the feature descriptors which were most associated with azeotropic temperature or composition were selected by genetic algorithm. Then, the azeotropic temperature and composition prediction models were built by using multiple linear regression methods. In the last place, the established optimal model was internally validated, externally validated, applied domain analyzed, compared with similar models in the literature and UNIFAC group contribution method. The results show that the optimal azeotropic temperature and composition prediction model that was made up of 8/5 feature descriptors, which multiple correlation coefficient, adjusted multiple correlation coefficient, root mean square errors, mean absolute deviation, leave-one-out cross-validation coefficient and external validation coefficient were 0.960 6/0.997 0, 0.957 2/0.996 9, 2.94/0.016 1, 1.89/0.010 4, 0.947 5/0.995 7 and 0.943 9/0.997 6,respectively, and the established optimal models were provided with more excellent stability, favorable generalization and fantastic predictive than the similar models.

Keywords： quantitative structure-property relationship (QSPR)； azeotropic temperature； azeotropic composition； prediction； a binary azeotrope containing water

Received 2019-07-02;

Fund:重庆市"科技创新领军人才支持计划"（CSTCCXLJRC201703）；重庆市社会事业与民生保障科技创新专项项目（cstc2017shmsA90016）。

Corresponding Authors: 诸林,E-mail:zhulinswpi65@gmail.com。 Email: zhulinswpi65@gmail.com

About author: 曾行艳(1996-),女,硕士研究生,研究方向为化学工程。

引用本文:

曾行艳, 诸林, 吕利平, 李兵.基于定量结构-性质关系预测含水二元共沸物的共沸温度与组成[J]. 化学工业与工程, 2020,37(4): 30-39

Zeng Xingyan, Zhu Lin, Lü Liping, Li Bing.Prediction of Azeotropic Temperature and Composition of Binary Azeotrope Containing Water Based on Quantitative Structure-Property Relationship[J]. Chemcial Industry and Engineering, 2020,37(4): 30-39

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