Research On Early Warning System Of Methane Concentration In Fully Mechanized Mining Face Based On IPSO-GRU

Gas accidents have long restricted the safe production of coal mines and the sustainable development of the coal industry in my country.The main reason for their triggering is the over-limit of methane concentration,which brings serious accident hazards to the underground production of mines.As the first site of coal mining,the general mining working face is the area where gas accidents occur frequently.At this stage,the prevention and control of methane concentration in coal mines is mainly achieved by monitoring methane concentration data through various sensors,and the early warning capability needs to be improved.Therefore,it is of great practical significance for the development of coal enterprises and safety of underground personnel to study the change trend of methane concentration,to achieve accurate prediction of methane concentration and carry out graded early warning of over-limit methane concentration.This thesis takes themethane concentration monitoring data of the comprehensive mining working face as the research object.Firstly,by analyzing the influencing factors of the methane concentration of the working face,the grey correlation method is used to calculate the weights of each factor and determine the six main factors affecting the methane concentration.And by improving the wavelet threshold method to remove the noise interference in the sensor acquisition process,and using principal component analysis(PCA)to downscale the 6dimensional input parameters of the gated recirculation unit(GRU)model to 4-dimensional principal component parameters to reduce the data redundancy of the network model.Secondly,the methane concentration prediction model is based on the GRU model.In order to solve the problem that the GRU network is prone to fall into the gradient,the improved particle swarm optimization algorithm(IPSO)is used to find the optimal values of the implied layer weights of the GRU network and to predict the methane concentration.Experiments show that the root mean square error of the IPSO-GRU methane concentration prediction model is 0.017,and the mean absolute error is 0.019,which shows that the model predicts with high accuracy and good fit.Finally,the methane concentration early warning system is design and developed on the.NET framework.The monitoring data and the prediction data of the IPSO-GRU model are divided into warning levels and warning intervals by the gray correlation analysis.The system is composed of three layers:client presentation layer,technical packaging application layer and database integration layer.which realizes the functions of predicting the methane concentration of the fully mechanized mining face,graded early warning and visual display of the data curve.In this thesis,the research on the early warning method of methane concentration data in the mechanized mining face,it can realize the prediction of methane concentration in the mechanized mining face and the grading early warning function of over-limit methane concentration,which can guaranteee the safety of mine personnel,and provide support and decision for coal mine safety production,and has certain theoretical research value and engineering application value.