Application of computational intelligence platform in coal and gas outburst prediction

The present situation of lacking fast and effective coal and gas outburst prediction techniques will lead to long out- burst prevention cycles and poor accurate prediction effects and slows down coal roadway drive speed seriously. Also, due to historical and economic reasons, some coal mines in China are equipped with poor safety equipment, and the staff professional capability is low. What＇s worse, artificial and mine geological conditions have great influences on the traditional technologies of coal and gas outburst prediction. Therefore, seeking a new fast and efficient coal and gas outburst prediction method is nec- essary. By using system engineering theory, combined with the current mine production conditions and based on the coal and gas outburst composite hypothesis, a coal and gas outburst spatiotemporal forecasting system was established. This system can guide forecasting work schedule, optimize prediction technologies, carry out step-by-step prediction and eliminate hazard hier- archically. From the point of view of application, the proposed system improves the prediction efficiency and accuracy. On this basis, computational intelligence methods to construct disaster information analysis platform were used. Feed-back results pro- vide decision support to mine safety supervisors.

Prediction method for risks of coal and gas outbursts based on spatial chaos theory using gas desorption index of drill cuttings

Based on the evolution of geological dynamics and spatial chaos theory, we proposed the advanced prediction an advanced prediction method of a gas desorption index of drill cuttings to predict coal and gas outbursts. We investigated and verified the prediction method by a spatial series data of a gas desorption index of drill cuttings obtained from the 113112 coal roadway at the Shitai Mine. Our experimental results show that the spatial distribution of the gas desorption index of drill cuttings has some chaotic charac- teristics, which implies that the risk of coal and gas outbursts can be predicted by spatial chaos theory. We also found that a proper amount of sample data needs to be chosen in order to ensure the accuracy and practical maneuverability of prediction. The relative prediction error is small when the prediction pace is chosen carefully. In our experiments, it turned out that the optimum number of sample points is 80 and the optimum prediction pace 30. The corresponding advanced prediction pace basically meets the requirements of engineering applications.

Advances in gas content based on outburst control technology in Huainan, China

The sudden and violent nature of coal and gas outbursts continues to pose a serious threat to coal mine safety in China. One of the key issues is to predict the occurrence of outbursts. Current methods that are used for predicting the outbursts in China are considered to be inadequate, inappropriate or impractical in some seam conditions. In recent years, Huainan Mining Industry Group(Huainan) in China and the Commonwealth Scientific and Industrial Research Organisation(CSIRO) in Australia have been jointly developing technology based on gas content in coal seams to predict the occurrence of outbursts in Huainan. Significant progresses in the technology development have been made, including the development of a more rapid and accurate system in determining gas content in coal seams, the invention of a sampling-while-drilling unit for fast and pointed coal sampling, and the coupling of DEM and LBM codes for advanced numerical simulation of outburst initiation and propagation. These advances are described in this paper.