背景:由芳族硝基化合物的结构不稳定性引起的反应的热危害是化学过程安全领域的主要关注点,也是诸如火灾和爆炸之类的重大热失控(TR)事故的主要原因之一。其中,自加速分解温度(SADT),作为一个重要的参数,已被广泛用于评估芳族硝基化合物在实际储存和运输过程中的热危害。然而,控制温度(CT)和紧急温度(ET),依赖于SADT并与之相关,在以前的研究中很少报道。在这项工作中,基于与27个单/二元芳族硝基化合物的稳定结构相对应的分子描述符,构建了CT和ET的多元线性回归(MLR)和人工神经网络(ANN)模型,结合先进的绝热加速量热实验和定量结构-性质关系(QSPR)。筛选出具有显著贡献的最优描述符子集,预测能力,并利用内部和外部验证参数评估了四类模型的稳健性,最后,选取两类参数(R2和ARE)作为主要指标进行综合比较分析。结果表明,4种模型均能较好地拟合实验数据。在此期间,人工神经网络模型的精度略高于MLR模型,两种模式(线性和非线性)下的QSPR模型在预测能力上更倾向于ET。在简化计算过程、实现参数快速预测的基础上,本研究有望为安全运行等工程应用提供技术支持,物质的安全储存和运输,和化学工业的应急响应。
方法:在这项工作中,我们通过ARC和AKTS测试和计算了27个单/二元芳族硝基化合物的热安全参数,并进一步使用PubChem数据库和Gaussian09软件程序来获得并优化其相应的分子结构。几何优化过程在B3LYP级和6-31+G(d,P)基础设置,而相同的功能和基础集用于振动分析。OpenBabel工具箱和ChemDES平台用于转换编码和描述符计算。最后,使用IBMSPSSStatistics24和MATLAB软件构建MLR模型和ANN模型,分别。
BACKGROUND: The thermal hazard of reactions caused by the structural instability of aromatic nitro compounds is a major concern in the field of chemical process safety and one of the main causes of major thermal runaway (TR) accidents such as fire and explosion. Among them, the self-accelerating decomposition temperature (SADT), as an important parameter, has been widely used to evaluate the thermal hazards of aromatic nitro compounds in actual storage and transportation processes. However, the control temperature (CT) and emergency temperature (ET), which depend on and are associated with SADT, have been rarely reported in previous studies. In this work, multiple linear regression (MLR) and artificial neural network (ANN) models for CT and ET were constructed based on the molecular descriptors corresponding to the stable structures of 27 monadic/binary aromatic nitro compounds, combined with advanced adiabatic accelerating calorimetric experiments and quantitative structure-property relationship (QSPR). The optimal subset of descriptors with significant contributions was screened out while the fit, predictive ability, and robustness of the four types of models were evaluated with internal and external validation parameters, and finally, two types of parameters (R2 and ARE) were selected as the main indicators for a comprehensive comparative analysis. The results show that the four models fit the experimental data well. During this period, the accuracy of ANN models is slightly higher than that of MLR models, and the QSPR models under the two modes (linear and nonlinear) are more inclined toward ET in prediction ability. Based on simplifying the calculation process and realizing rapid parameter prediction, this study is expected to provide technical support for engineering applications such as safe operation, safe storage and transportation of substances, and emergency response in the chemical industry.
METHODS: In this work, we tested and calculated the thermal safety parameters of 27 monadic/binary aromatic nitro compounds by ARC and AKTS and further used the PubChem database and Gaussian 09 software program to obtain and optimize their corresponding molecular structures. The geometric optimization process adopts DFT on the B3LYP level and the 6-31 + G(d, p) basis set, while the same functional and basis set was used for vibration analysis. The OpenBabel toolbox and ChemDES platform were used for transformation coding and descriptor calculation. Finally, IBM SPSS Statistics 24 and MATLAB software were used to construct MLR models and ANN models, respectively.