Modification of molecular descriptor and modeling for boiling point prediction of aromatic hydrocarbons

Xiangcheng MA; Wei QIN; Qinglin CHEN; Bingjian ZHANG

doi:10.11949/0438-1157.20190425

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Modification of molecular descriptor and modeling for boiling point prediction of aromatic hydrocarbons

Process system engineering | 更新时间：2020-06-24

- Modification of molecular descriptor and modeling for boiling point prediction of aromatic hydrocarbons
- CIESC Journal Vol. 70, Issue 11, Pages: 4306-4314(2019)
- 作者机构：
  
  中山大学材料科学与工程学院，广东省石化节能工程技术研究中心，广东广州 519082
- 作者简介：
- 基金信息：
- DOI：10.11949/0438-1157.20190425
  CLC： TQ 028.8
- Received：24 April 2019，
  
  Revised：2019-07-04，
  
  Published：05 November 2019
- 稿件说明：
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马香成, 秦蔚, 陈清林, 张冰剑. 芳烃分子描述符的修正和沸点预测建模[J]. 化工学报, 2019, 70(11): 4306-4314

MA Xiangcheng, QIN Wei, CHEN Qinglin, ZHANG Bingjian. Modification of molecular descriptor and modeling for boiling point prediction of aromatic hydrocarbons[J]. CIESC Journal, 2019, 70(11): 4306-4314
马香成, 秦蔚, 陈清林, 张冰剑. 芳烃分子描述符的修正和沸点预测建模[J]. 化工学报, 2019, 70(11): 4306-4314 DOI： 10.11949/0438-1157.20190425.

MA Xiangcheng, QIN Wei, CHEN Qinglin, ZHANG Bingjian. Modification of molecular descriptor and modeling for boiling point prediction of aromatic hydrocarbons[J]. CIESC Journal, 2019, 70(11): 4306-4314 DOI： 10.11949/0438-1157.20190425.

摘要

根据芳烃分子中碳原子所处化学环境的差异，修正分子描述符Randić连接性指数中碳原子支化度参数

，提出新的分子描述符Modran；通过对比分析表明描述符Modran对芳烃分子的化学结构具有更好的选择性。分析芳烃沸点的影响因素，采用最优子集选择法，发现Modran一阶和二阶支化度指数、分子极化率和芳环碳原子占比四个分子描述符参数的组合对芳烃沸点具有良好的预测能力，进而建立包含Modran等四分子描述符的线性组合模型和神经网络模型。与Marrero-Pardillo基团贡献法比较，四分子描述符的神经网络模型将芳烃沸点预测的平均绝对误差由12.26 K降低到4.56 K。

Abstract

According to the differences of chemical environment of carbon atoms in aromatic hydrocarbons

the carbon atom branching degree index

in the Randić connectivity index is modified

and a new molecular descriptor Modran is presented. Comparative analysis shows that the descriptor Modran has better selectivity for the chemical structure of aromatic molecules. Through analyzing the factors influencing the boiling points of aromatic hydrocarbons and using the best subset selection method

it is found that the combination of four molecular descriptors

such as Modran first-order and second-order branching index

molecular polarizability and aromatic ring carbon atom ratio

has good predictive ability for aromatics boiling point

and then linear combination model and neural network model including the four descriptors are established. Compared with the Marrero-Pardillo group contribution method

the neural network model of the four-molecule descriptor reduces the average absolute error of the boiling point prediction of aromatics from 12.26 K to 4.56 K.

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references

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