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- Title
芳香族硝基化合物自加速分解温度的定量 结构-性质关系.
- Authors
赵东风; 秦传睿; 党梦涛
- Abstract
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 thermodynamic and kinetic parameters of 18 aromatic nitro compounds were obtained through adiabatic accelerated calorimetry experiments and the self-accelerating decomposition temperature of the substance in a standard packaging of 25 kilograms was calculated. 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 ability of the two models were verified and compared. The results show that the correlation coefficients of aromatic nitro compounds corresponding to the MLR model and the ANN model are 0. 893 and 0. 975, respectively. The ANN model is obviously superior to the MLR model in terms of matching degree.
- Subjects
NITRO compounds; AROMATIC compounds; TEMPERATURE
- Publication
Journal of China University of Petroleum, 2023, Vol 47, Issue 6, p171
- ISSN
1673-5005
- Publication type
Article
- DOI
10.3969/j.issn.1673-5005.2023.06.020