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Aiming at the complexity of coal structure and reaction, based on the analysis of active group species, structure and its transformation characteristics in coal reaction, the paper comprehensively constructed the active structural unit in the process of coal spontaneous combustion for the first time. Using the frontier orbital theory and quantum chemical calculation to analysis the complete reaction path, activation energy and the heat release of electron transfer in reactions of the active site, the 13 elementary reaction were obtained. The order and relationship of the reactions in coal spontaneous combustion was also built. Based on the research results, the coal oxidation dynamics theory was presented in the paper, which is a chemical chain cycle caused by oxygen with low activation energy, and converts the coal native structure to carbon free radicals releasing the gaseous products. The reaction mechanism of heat and products was illustrated in the oxidation dynamics theory of coal spontaneous combustion. The theory was successfully applicated in categorizing the spontaneous combustion tendency of coal and developing highly-effective chemical inhibition technology to prevent coal spontaneous combustion. [ABSTRACT FROM AUTHOR]

For the purpose of achieving more accurate and reliable gas emission dynamic prediction through effective analysis of gas measuring data in mines, this paper put forward a method that use ant colony clustering to optimize El-man neural network. Ant colony clustering algorithm was merged with Elman neural network to optimize weight and threshold. The model of gas emission quantity prediction was established by ACC-ENN algorithm, with the historical data of mine actual monitoring to experiment and analysis. The results show that the Elman neural network model optimized by ant colony clustering than other prediction model has better generalization ability and higher precision of prediction, to realize the dynamic forecast of gas emission effectively. [ABSTRACT FROM AUTHOR]