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

Key words:  aromatic nitro compounds  self-accelerating decomposition temperature  quantitative structure-property relationship

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.