Study On The Application Of AE Techniques In Risk Prediction Of Coal And Gas Outburst

Coal and gas outburst is a kind of extremely complicated dynamic phenomenon. It is the comprehensive action results of ground stress, gas pressure, and mechanical properties of coal structure and so on. Because of its heavy destroys for roadway, equipments, facilities, and production system, furthermore gas explosion, and coal and gas outburst seriously threatened the safety production, therefore, it is great significant for coal mine safety production to study on risk prediction of coal and gas outburst.There is AE (Acoustic Emission) together with the deformation and failure in solid materials, such as coal, rocks and so on, therefore AE techniques can be used for real-time detection for inner material. It is the premise of coal and gas outburst perdiction for dynamic damage and failure and instability prediction of coal-lane way. Based on choosing 6222 driving face in Hai Shiwan Coal Mine, Yaojie Coal Electricity Group, analyzed the burst risk by the ways of theory analysis, numerical simulation and spot monitoring etc.Study shows that there exists inherent vice in coal-rock mess, and inherent vice is important AE sources. Caiser effect and AE signal during coal samples loading process is theoretical basis of risk prediction of coal and gas outburst, and discriminated the risk prediction via 2σcriteria. If the large events or low events are between (μ-2σ,μ+2σ), there is no burst risk in workface, then dangerous. The key point of AE prediction is noise filtering, and it will be better via the improvement of soft, hard and field installation in sensor. The range of plastic circle at the sides of the coal-lane way, therefore, combined with the decaying characters of AE signal then across the loose zone, we should install the wave-guiding rod to the original coal-rock mess beyond the loose zone for monitoring, and chose the length in 1.9～2.0m. Spot data displayed that AE sensor can better collected the fractures of the driving face with burst risk, and captured the precursor information of coal and gas outburst. Compared with the spot situation in the workface, verified the effectiveness of 2σcriteria for the risk prediction of driving face.