Thermal decomposition mechanism of nitroguanidine via a chemical reaction neural network and experiments

Nitroguanidine (NQ) plays a central role in aerospace and industrial fields, and a deep understanding of its dynamic behavior and reaction mechanisms is crucial for accurately predicting its combustion and explosive properties. To this end, this work proposes a novel kinetic modelling approach that combines a chemical reaction neural network (CRNN) with thermogravimetric (TG) experiments to conduct an in-depth study of NQ reaction kinetics. The results demonstrate that the kinetic model constructed via the CRNN accurately fits the experimental data, revealing the main reaction pathways of NQ and extracting kinetic parameters. Two simplified models have been developed: a single-step reaction model and a multistep reaction model. The single-step model, regarded as the global reaction of NQ, effectively predicts the pyrolysis process of NQ and the formation of its solid products, with activation energy values that are consistent with the experimental results. The multistep reaction model successfully reproduces the TG curve and offers a detailed depiction of the NQ reaction mechanism, covering the initial decomposition pathway, intermediate products, and interconversion reactions among gases. Compared with other data-driven modelling techniques, the CRNN modelling approach incorporates constraints from both experimental and numerical results, making the derived kinetic models more physically reasonable.