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 ›› 2021, Vol. 40 ›› Issue (3): 303-307.

• • 上一篇    下一篇

烃类物质自燃点的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. 常州大学环境与安全工程学院,江苏常州213164
  • 出版日期:2021-03-15 发布日期:2021-03-15
  • 通讯作者: 时静洁（1987-），女，常州大学环境与安全工程学院讲师。
  • 作者简介:朱红亚（1985－）,女,江苏昆山人,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、消防应急救援方面的研究，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 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. School of Environmental and Safety Engineering, Changzhou University, Jiangsu Changzhou 213164, China
  • Online:2021-03-15 Published:2021-03-15

摘要： 应用定量构效关系（QSPR）方法对烃类物质的自燃点开展了预测研究。选取国际电工委员会数据库中的39 种烃类物质作为样本集，随机选择34 种作为训练集，5 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5 种烃类物质的自燃点。结果表明，2 个预测模型均比较稳定，理论预测值与实验值均较为相符，GA-SVM 模型预测结果较GA-MLR 模型更接近于实验值，这表明自燃点与其分子结构间具有更强的非线性关系。

关键词: 烃类物质, 自燃点预测, QSPR

Abstract: The auto- ignition temperature (AIT) were predicted by Quantitative Structure- Pharmacokinetics Relationship (QSPR). Thirty-nine kinds of hydrocarbons in the International Electrotechnical Commission (IEC) database were selected as sample sets, 34 kinds were randomly selected as training sets and 5 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 spontaneous ignition points of 5 hydrocarbon substances were predicted theoretically. Finally, the performance and application fields of the model were evaluated. The results showed that the two prediction models are stable and have strong prediction ability and generalization performance. The theoretical predicted values are consistent with the experimental values, and the predicted results of GA-SVM model are closer to the experimental values than GA-MLR model, which indicates that the relationship between auto- ignition temperature and its molecular structure is more nonlinear.  

Key words: hydrocarbon substances, prediction of auto-ignition temperature, QSPR