Fusion calorimetric experiments and QSPR modeling to inversely explore the accuracy of thermodynamic methods: A case study of the apparent activation energies of 18 aromatic nitro compounds

Abstract

The choice of thermodynamic analysis method will directly affect the accuracy of thermal hazard assessment. In this work, two advanced calorimetric experiments were used to obtain the pyrolysis parameters of 18 aromatic nitro compounds under dynamic and adiabatic conditions, aiming at the inherent thermal risk and explosion characteristics of aromatic nitro compounds in important chemical processes. The multiple linear regression (MLR) and artificial neural network (ANN) models corresponding to six typical thermodynamic analysis methods (FWO, Kissinger, Starink, Friedman, Townsend, and TMRad) were constructed by using the apparent activation energy (Ea) calculated by fitting as the basic sample data and combining with quantitative structure-property relationship (QSPR). Two kinds of parameters in internal and external validation were used as the main indexes to judge the accuracy and reliability of different kinetic analysis methods applied to aromatic nitro compounds. This study is expected to provide theoretical support and technical guidance for the chemical industry in the thermal hazard assessment process of nitro compounds.