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Analyze on Models and Application of Dynamica Overall the Dangerous of Gas Explosion on Full Mechanized Coalface

Author: YanMin
Tutor: LiShuGang
School: Xi'an University of Science and Technology
Course: Safety Technology and Engineering
Keywords: Gas explosion hazard Nonlinear analysis model Grey Relational Theory Analytic Hierarchy Process Artificial Neural Networks Forecast
CLC: TD712.7
Type: Master's thesis
Year: 2009
Downloads: 105
Quote: 0
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Abstract


Fully mechanized caving face gas emission rate by the amount of coal mining , mining strength , mining depth increase and other factors , showing obvious instability , seriously affecting the safety production of fully mechanized caving face , to grasp fully mechanized caving face gas explosion disaster the degree of risk , prevention of gas explosion accidents this thesis is based on the theory of accident causes , man - machine - the ring system analysis and hierarchical analysis , the establishment of a fully mechanized caving face gas explosion accident analysis model , from the gas state , people machine , management Quartet conduct a comprehensive analysis of the face of the fully mechanized caving face gas explosion impact factors , establish a set of fully mechanized caving face gas explosion could reflect general relationship between various factors and forecast gas explosion risk index system . In view of the non-linear characteristics of the gas explosion disaster , the use of gray relational analysis method to determine the nonlinear theory of gas explosion accidents analyze the degree of association between the second layer of the three-tier structure of the factors , and then use AHP to determine the second layer target layer right weight , associate degree weight combined to draw comprehensive evaluation of training samples of BP neural network as a nonlinear artificial neural network , combined with fully mechanized caving face gas explosion disaster characteristics to determine the fully mechanized caving face gas explosion hazard sexual BP neural network evaluation model , specific mechanized caving face gas explosion four major categories of influencing factors were evaluated , and finally established a BP neural network prediction model for a mine 102 fully mechanized caving face gas explosion risk associated largest gas Emission . This article aims to dangerous argument , mine fully mechanized caving scientific assessment of the ability of the surface gas explosion disaster prevention and the establishment of the security system and other aspects of the gas disaster closely linked , and provide the basis for the establishment of the control system of the gas explosion , as the progressive realization to the prevention of accidents provide a theoretical basis for the center of modern scientific management methods .

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CLC: > Industrial Technology > Mining Engineering > Mine safety and labor protection > Mine atmosphere > Mine Gas > Prevention and treatment of gas explosion
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