Design Of Intelligent Monitoring System For Gas Extraction

Gas extraction is one of the most effective measures to control gas disasters,and is also beneficial to reduce energy waste.The monitoring level of gas extraction is directly related to the safe production and sustainable utilization of resources of coal enterprises.With the state's attention to energy conservation and consumption reduction and the rapid development of coal industry,how to intelligently predict underground gas concentration and extract gas according to demand has become an important subject while ensuring safe production of coal enterprises.This paper conducts an in-depth study on intelligent monitoring of gas extraction,mainly in the following aspects:First of all,the intelligent monitoring system for gas extraction researched and designed in this paper is mainly aimed at the intelligent monitoring of gas in underground gas extraction pipelines.Through extraction from main line and branch pipe individual monitoring-station of sensor installation of gas concentration,gas temperature of extraction pipeline,pipeline pressure,mixed real-time monitoring of main parameters such as flow rate and CO concentration,by PLC as the core controller to deal with the analysis of relevant data,adjust the downhole electric regulating valve opening position,thus controlling the gas pipeline extraction concentration always stay within the scope of the extraction requirements,ground monitoring center by the configuration software to create PC real-time display the current gas extraction parameters,finally realizes the gas extraction intelligent monitoring.Secondly,combining the characteristics of actual gas extraction parameters in Guqiao Coal Mine(Southern district),Huainan City,Anhui Province,a gas concentration prediction model based on particle swarm optimization(PSO)algorithm and ANN structure was proposed.The control variable method was used to explore the influence of the number of network layers and neurons on the effect of the prediction model,and a neural network containing 14 neurons in each layer of 7 layers was designed.In order to build the neural network automatically,the PSO optimized ANN structure was proposed,and then the gas data prediction simulation experiment was carried out.The performance of the two network structures was compared in detail,and finally the effectiveness of the proposed algorithm was verified.The proposed gas concentration prediction model can effectively predict gas extraction concentration and make preventive alarm in advance.Finally,according to gas extraction in our country some of the major problems existing in the monitoring system and deficiency,the paper has carried on the corresponding research and analysis,put forward the concentration of the gas extraction design ideas of automatic optimization control,high efficiency,high concentrations of gas extraction to meet the requirements of ground gas extraction,and to achieve the requirements of different concentration of the gas extraction.Figure [42] table [8] reference [57]...