Study On Gas Distribution And Its Distribution Field Reconstruction Technology In Mine Stope

Gas accident is the most serious accidents in the coal mine which causes a largenumber of casualties and economic losses. Most of gas explosion accidents occur inthe mining and tunneling area in mine. Mining and tunneling tunnel is the main gasemission area, where it is easy to come up with the gas accumulation. Usingcomputer numerical simulation of the gas distribution of airflow within the miningtunnel, which is able to know the regularity of gas distribution and optimize thedesign of ventilation. But this method can not achieve the quantification grasp andprediction of gas emission and gas accumulation, and also can not make an effectiveanalysis of gas distribution in the stope with the measured data of the gasconcentration. The reconstruction of the gas distribution field use of gasconcentration monitoring data for, can realize the quantitative understanding for thestope gas distribution, and can realize the fast determine of the gas emission andaccumulation area. So this dissertation mainly researches that the theory and testanalysis of stope gas distribution rules, gas distribution field reconstructiontechnique.Main works in this dissertation are as follows:(1) To establish the ventilation physical model and airflow turbulencemathematical model of the mining face tunnel. Under the existing turbulence theory,the numerical simulation analysis of air migration process in the underground tunnelis made. The general rules of stope tunnel airflow field and gas distribution ismastered to provide a theoretical basis for the reconstruction of gas distribution field.It is shown that the areas of the cutting unit of the shearer head and the upper cornerare most prone to gas accumulation, so the gas concentration monitoring in theseareas must be reinforced.(2) Basic requirements of the mining face gas monitoring are introduced, and toachieve the stope gas monitoring network is expounded. Improving element methodis proposed to measure the distribution of the stope gas concentration, and the trendsurface fitting and mathematical interpolation analysis are completed. Through theresearch of onsite gas monitoring data, the result shows that the reality rules of thestope gas distribution is in accord with the results of the theoretical simulationanalysis, and to provide a realistic basis for the reconstruction of gas distributionfield. (3) A reconstruction technique of stope gas distributed field based on the spatialinformation statistics is presented. Because of its own shortcomings of traditionalinterpolation methods, they are difficult to achieve the reconstruction of gasdistribution under the sparse data. Spatial information statistics methods based on theregionalized variable theory and the variogram tool, it can realize the best non-biasedinterpolation estimation to study on the randomness and structure data. Through theexperimental variogram calculation of the measurement data and comparativeanalysis with the cross-validation of a variety of model fitting results, the powerfunction model fitting is determined to be used for Kriging interpolation of gasdistribution field reconstruction methods.(4) Proposed a reconstruction techniques based on the neural network algorithmfor gas distribution field. The neural network interpolation has good nonlinearapproximation capability. The basic theory of the neural networks is introduced.Through the neural network training to the measured data with the BF networkalgorithm and the RBF network algorithm, the results show that the generalizedregression neural network algorithm has the best performance for reconstruction ofgas distribution field. The reconstructed gas distribution field has smooth continuityand has best the fitting precision.(5) Completed a comparative analysis of their practice performance and dataadaptation with these two reconstruction methods. Both of the gas reconstructionmethods have good theoretical basis and application cases. In dissertation,throughthe reconstruction practice and comparative analysis of a group of measured gasconcentration monitoring data, and by gradually reducing the reconstruction sampledata, their data adaptive and predictive performance of two reconstruction methodswere tested. The results show that the reconstruction technique based on the neuralnetwork algorithm requires more sample data to ensure the reconstruction accuracy,the reconstruction technique based on spatial information statistics method has goodreconstruction accuracy and stability under in the case of large changes of the samplequantity.