Evolutionary analysis of reaction products and chain reaction processes of coal combustion with different degrees of metamorphism based on ML potential MD

The spontaneous combustion process of coal bodies is characterized by complex chain reaction pathways, which have caused some interference in the mechanistic study of coal spontaneous combustion. This study utilized thermogravimetric-Fourier infrared spectroscopy (TG-FTIR) coupling and characterization experiments based on the machine learning (ML) potential molecular dynamics (MD) method to investigate the molecular dynamics evolution mechanism and typical reaction paths of the spontaneous combustion microchemical structures of coals with different degrees of metamorphism. The results demonstrate that the nature of the oxidative decomposition of coal bodies is the oxidative dehydrogenation effect at high temperatures, which leads to the desorption and separation of alkyl side chains and reactive functional groups, and the gradual decomposition into gas-phase products and carbon-containing small-molecule fragments such as H 2 O, CO 2 , CO, or hydrocarbons. Free radicals have been identified as the initiators of the chain reaction, thereby accelerating the oxidation reaction throughout the process. Furthermore, it has been determined that coal with a low degree of metamorphism has more reactive functional group structures, the chain reaction occurs more frequently, and spontaneous combustion is more likely to occur. Conversely, higher metamorphic coals exhibit a higher percentage of carbon and form significantly more typical small molecule fragment structures and reactions, resulting in higher gas-phase conversion and higher calorific values. The results of this study provide a new perspective on the mechanism of the spontaneous coal combustion chain reaction from the molecular level.