主管：中华人民共和国应急管理部
主办：应急管理部天津消防研究所
ISSN 1009-0029  CN 12-1311/TU
消防科学与技术

消防科学与技术 ›› 2022, Vol. 41 ›› Issue (8): 1019-1022.

• •    下一篇

烃的含氧衍生物自燃点的QSPR预测研究

朱红亚1，李晶晶1，时静洁2   

  1. （1.应急管理部天津消防研究所，天津 300381；2.常州大学，江苏 常州 213164）
  • 出版日期:2022-08-15 发布日期:2022-08-15
  • 作者简介:作者简介：朱红亚（1985-），女，应急管理部天津消防研究所助理研究员，博士，主要从事工业火灾防控、消防应急救援研究，天津市南开区卫津南路110号，300381。
  • 基金资助:
    国家重点研发计划项目（2017YFC0806600）；应急管理部天津消防研究所基科费项目（2019SJ05）；江苏省高等学校自然科学研究面上项目（19KJB620002）

Study on QSPR prediction of auto-ignition temperature of oxygen-containing derivative of hydrocarbons

ZHU Hong-ya1, LI Jing-jing1, SHI Jing-jie2   

  1. (1. Tianjin Fire Science and Technology Research Institute of MEM, Tianjin 300381, China； 2. Changzhou University, Jiangsu Changzhou 213164, China)
  • Online:2022-08-15 Published:2022-08-15

摘要： 应用定量构效关系（QSPR）方法对烃的含氧衍生物的自燃点（AIT）及其与分子结构间的内在定量关系进行了研究。选取国际电工委员会（IEC）数据库中的76种烃的含氧衍生物作为样本集，选择65 种作为训练集用于建立预测模型，11 种作为测试集。采用遗传算法（GA）对变量进行筛选，结合线性和非线性方法分别建立多元线性回归( MLR) 模型和支持向量机( SVM) 模型，理论预测得到了11种烃的含氧衍生物的自燃点，最后对所构建模型的性能及应用域进行了评价。结果表明，经GA筛选得出MATS2e、nCOH、Dv、BEHv2、nCHR、GATS1v、IDE、Du等8种特征分子描述符，GA-MLR和GA-SVM模型的理论预测值与实验值均较为相符且后者更优，两个预测模型均比较稳定，且具备较强的预测能力和泛化推广性能。

关键词: 烃的含氧衍生物, 自燃点, 定量构效关系, 遗传算法, 多元线性回归, 支持向量机

Abstract: The auto-ignition temperature (AIT) and its intrinsic quantitative relationship with molecular structure of hydrocarbons were studied by Quantitative Structure-Pharmacokinetics Relationship (QSPR). 76 kinds of oxygen-containing derivative of hydrocarbons in the International Electrotechnical Commission (IEC) database were selected as sample sets, 65 kinds were randomly selected as training sets to set up prediction model and 11 kinds as test sets. Genetic algorithm (GA) was used to screen variables, multiple linear regression (MLR) model and support vector machine (SVM) model were established by combining linear and nonlinear methods respectively, and the auto-ignition temperature of 11 oxygen-containing derivative of hydrocarbons were predicted theoretically. Finally, the performance and application fields of the model were evaluated. The results show that eight characteristic molecular descriptors, such as MATS2e, nCOH, Dv, BEHv2, nCHR, GATS1v, IDE and Du, were obtained by GA. The theoretical predicted values of GA-MLR and GA-SVM models were consistent with the experimental values，and the latter model was better. The two prediction models are stable and have strong prediction ability and generalization performance.

Key words: oxygen-containing derivative of hydrocarbons, auto-ignition temperature, quantitative structure-pharmacokinetics relationship, genetic algorithm, multiple linear regression, support vector machine