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Prediction of control temperature and emergency temperature of monadic/binary aromatic nitro compounds by quantitative structure-property relationship: correlation study of self-accelerating decomposition temperature in thermal hazard assessment

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Abstract

Context

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.

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Funding

This work was partly supported by the 2019 Major Scientific and Technological Innovation Projects of Provincial Key R&D Plan (2019JZZY020502), Qingdao Minsheng Science and Technology Plan Project (21-1-4-SF-4-NSH), and the 2020 Science and Technology Project of Qingdao West Coast New Area (2020-42).

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Authors and Affiliations

  1. College of Mechanical and Electrical Engineering, China University of Petroleum (East China), Qingdao, Shandong, China

    Chuanrui Qin & Dongfeng Zhao

  2. Wanhua Chemical Group Co., Ltd, No.59 Chongqing Rd., YEDA, Yantai, Shandong, China

    Mengtao Dang

  3. College of Chemical Engineering, China University of Petroleum (East China), Qingdao, 266580, Shandong, China

    Yifei Meng & Dongfeng Zhao

  4. The Center For Chemical Process Safety-China Section, China University of Petroleum (East China), Qingdao, Shandong, China

    Dongfeng Zhao

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All authors contributed to the study conception, design data collection, and analysis. The first draft of the manuscript was written by Chuanrui Qin, and all authors have made revisions to the first few versions of the manuscript. All authors reviewed and approved the manuscript.

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Correspondence to Dongfeng Zhao.

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Qin, C., Dang, M., Meng, Y. et al. Prediction of control temperature and emergency temperature of monadic/binary aromatic nitro compounds by quantitative structure-property relationship: correlation study of self-accelerating decomposition temperature in thermal hazard assessment. J Mol Model 29, 322 (2023). https://doi.org/10.1007/s00894-023-05719-w

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