TY - JOUR
T1 - Thermal decomposition mechanism of 1,3,5-trinitroperhydro-1,3,5-triazine
T2 - Experiments and reaction kinetic modeling
AU - Xu, Yabei
AU - Chu, Qingzhao
AU - Chang, Xiaoya
AU - Wang, He
AU - Wang, Shengkai
AU - Xu, Shengliang
AU - Chen, Dongping
N1 - Publisher Copyright:
© 2023 Elsevier Ltd
PY - 2023/12/5
Y1 - 2023/12/5
N2 - This study investigates the thermal decomposition of 1,3,5-trinitroperhydro-1,3,5-triazine (RDX) based on combined thermogravimetric (TG) and chemical reaction neural network (CRNN). Two compact kinetic models for RDX are introduced, with one consisting of three substances and a single global reaction (one-step model) and the other consisting of three substances and four reactions (3–4 model). In the one-step model, the calculated activation energy is 193.67 kJ mol−1, which agrees with the experimental value. As for the 3–4 model, the substances and reactions are assigned based on a skeleton mechanism involving reactions of N-N and C-N bond rupture and HONO elimination. This work presents the first application of the CRNN to obtain a reaction mechanism of RDX decomposition, which is further validated against experiments. Furthermore, effects of sublimation and vaporization phenomena are also included in the model. Extension of the CRNN to kinetic modeling of other energetic materials is anticipated in future studies.
AB - This study investigates the thermal decomposition of 1,3,5-trinitroperhydro-1,3,5-triazine (RDX) based on combined thermogravimetric (TG) and chemical reaction neural network (CRNN). Two compact kinetic models for RDX are introduced, with one consisting of three substances and a single global reaction (one-step model) and the other consisting of three substances and four reactions (3–4 model). In the one-step model, the calculated activation energy is 193.67 kJ mol−1, which agrees with the experimental value. As for the 3–4 model, the substances and reactions are assigned based on a skeleton mechanism involving reactions of N-N and C-N bond rupture and HONO elimination. This work presents the first application of the CRNN to obtain a reaction mechanism of RDX decomposition, which is further validated against experiments. Furthermore, effects of sublimation and vaporization phenomena are also included in the model. Extension of the CRNN to kinetic modeling of other energetic materials is anticipated in future studies.
KW - 1,3,5-Trinitroperhydro-1,3,5-triazine
KW - Chemical reaction neural network
KW - Kinetic modeling
KW - Thermal decomposition
KW - Thermogravimetric
UR - http://www.scopus.com/inward/record.url?scp=85170071712&partnerID=8YFLogxK
U2 - 10.1016/j.ces.2023.119234
DO - 10.1016/j.ces.2023.119234
M3 - Article
AN - SCOPUS:85170071712
SN - 0009-2509
VL - 282
JO - Chemical Engineering Science
JF - Chemical Engineering Science
M1 - 119234
ER -