Study On The Evaluation Technology Of Potential Outburst Risk In Coal Seam

Coal is the foundation energy of our national economic development. In theindustry field, coal mines exist the most major hazards and the greatest risks. Coal andgas outburst occurred in coal mines is a dynamic phenomenon whose mechanism isextremely complicate. Coal and gas outburst is one of the most serious naturaldisasters to hamper the safety states during mining. The mechanism of the disaster isnot clear yet, however, the accidents caused by it become much more serious. Thearticle put forward a guideline, which was based on the outburst mechanism's"comprehensive hypothesis'' and applied artificial neural network models to evaluatethe potential outburst risk of coal seam.Firstly, this paper discussed the need and significance of evaluating the potentialoutburst risk of coal seam, analyzed the current situation and subsistent problems, andestablished research programs and technical routes, which were based on outburstmechanism's "comprehensive hypothesis" in theory and applied the artificial neuralnetwork model for the evaluation.Secondly, the paper comprehensively analyzed and reviewed the factors whichhad impact on coal and gas outburst, such as gas, geological conditions and so on inthe round. According to the factors, the qualitative and quantitative indicators weregot. Through the comprehensive analysis and review, the paper establishedcomprehensive indices system of coal seam potential outburst risk which consisted ofthree stair indices: gas, coal body structure features and geological conformation, andclassified the eight indicators to reflect three factors.Thirdly, the basic principles, frameworks, features and capacities of the ArtificialNeural Network (ANN) were briefly introduced. The adaptability combining thepotential outburst risk evaluation of coal seam with the non-linear artificial neuralnetwork technology was discussed and ANN was believed to be a reasonable andpowerful approach to evaluate the potential outburst risk of coal seam. BackPropagation Neural Network (BPNN) was found to be the most feasible networkmodel in the assessment.Lastly, the paper emphatically and deeply studied the structure design and trainingof the BPNN which was widely applied in the function simulation and classreorganization field. On the basis of data collected from the Hongling coal mine inShenyang, Liaoning province, by applying neural network toolbox (NNT) onMATLAB software the author established BP neural network model to evaluate thepotential outburst risk of coal seam. The model considered the effect of the followingfactors: gas content, gas pressure, the destruction type of coal, the solidity modulus ofcoal, gas early speed of diffusion, soft coal ratio, fold, and fault. The test resultsindicated that the model can accurately evaluate the potential outburst risk, thereforethis paper found a new technical mean to evaluate the potential outburst risk of coalseam.