芳香族硝基化合物自加速分解温度的定量结构-性质关系  

Quantitative structure-property relationship of self-accelerating decomposition temperature of aromatic nitro compounds

作　　者：赵东风[1] 秦传睿 党梦涛 ZHAO Dongfeng;QIN Chuanrui;DANG Mengtao(College of Chemistry and Chemical Engineering in China University of Petroleum(East China),Qingdao 266580,China;College of Mechanical and Electrical Engineering in China University of Petroleum(East China),Qingdao 266580,China)

机构地区：[1]中国石油大学(华东)化学化工学院,山东青岛266580 [2]中国石油大学(华东)机电工程学院,山东青岛266580

出　　处：《中国石油大学学报（自然科学版）》2023年第6期171-177,共7页Journal of China University of Petroleum(Edition of Natural Science)

基　　金：山东省重点研发计划重大科技创新工程(2019JZZY020502);青岛市民生科技计划项目(21-1-4-sf-4-nsh);青岛西海岸新区科技项目(2020-42)。

摘　　要：针对芳香族硝基化合物生产、运输以及储存过程中引发的重特大燃爆事故,采用试验及模型计算等方式对其自加速分解温度(SADT)进行获取,并提出一种基于定量结构-性质关系(QSPR)的理论预测方法。通过绝热加速量热试验获取18种芳香族硝基化合物的热力学和动力学参数,以此计算得到25 kg标准包装下物质的自加速分解温度。应用多元线性回归(MLR)和人工神经网络(ANN)等机器学习方法分别构建相应的预测模型,最终验证并比较两种模型的拟合能力、鲁棒性和预测能力。结果表明:芳香族硝基化合物对应MLR模型和ANN模型的相关系数分别为0.893和0.975,ANN模型在匹配度方面明显优于MLR模型。Aiming at the serious explosion accidents caused by aromatic nitro compounds in production,transportation,and storage,the self-accelerating decomposition temperature(SADT)was obtained by experiments and model calculations,and a theoretical prediction method based on the quantitative structure-property relationship(QSPR)was proposed.The thermody-namic and kinetic parameters of 18 aromatic nitro compounds were obtained through adiabatic accelerated calorimetry experi-ments and the self-accelerating decomposition temperature of the substance in a standard packaging of 25 kilograms was cal-culated.In addition,machine learning methods such as multiple linear regression(MLR)and artificial neural network(ANN)were applied to construct corresponding prediction models.Finally,the fitting ability,robustness,and prediction a-bility of the two models were verified and compared.The results show that the correlation coefficients of aromatic nitro com-pounds corresponding to the MLR model and the ANN model are 0.893 and 0.975,respectively.The ANN model is obvious-ly superior to the MLR model in terms of matching degree.

关 键 词：芳香族硝基化合物 自加速分解温度 定量结构-性质关系 

分 类 号：X937[环境科学与工程—安全科学]