Traditional coal mine safety prediction methods are off-line and do not have dynamic prediction functions.The Support Vector Machine(SVM) is a new machine learning algorithm that has excellent properties.The least squ...Traditional coal mine safety prediction methods are off-line and do not have dynamic prediction functions.The Support Vector Machine(SVM) is a new machine learning algorithm that has excellent properties.The least squares support vector machine(LS-SVM) algorithm is an improved algorithm of SVM.But the common LS-SVM algorithm,used directly in safety predictions,has some problems.We have first studied gas prediction problems and the basic theory of LS-SVM.Given these problems,we have investigated the affect of the time factor about safety prediction and present an on-line prediction algorithm,based on LS-SVM.Finally,given our observed data,we used the on-line algorithm to predict gas emissions and used other related algorithm to compare its performance.The simulation results have verified the validity of the new algorithm.展开更多
Based on the nowadays＇ condition, it is urgent that the gas detection cable communication system must be replaced by the wireless communication systems. The wireless sensors distributed in the environment can achieve...Based on the nowadays＇ condition, it is urgent that the gas detection cable communication system must be replaced by the wireless communication systems. The wireless sensors distributed in the environment can achieve the intelligent gas monitoring system. Apply with multilayer data fuse to design working tactics, and import the artificial neural networks to analyze detecting result. The wireless sensors system communicates with the control center through the optical fiber cable. All the gas sensor nodes distributed in coal mine are combined into an intelligent, flexible structure wireless network system, forming coal mine gas monitoring system based on wireless sensor network.展开更多
In view of the difficulty of automatic adjustment, the recovery lag and the major accident potential of the mine ventilation system, an experimental model of the pipe net was established according to the typical one m...In view of the difficulty of automatic adjustment, the recovery lag and the major accident potential of the mine ventilation system, an experimental model of the pipe net was established according to the typical one mine and one working face ventilation system of Daliuta coal mine. Using the best uniform approximation method of Chebyshev interpolation to fit the fan performance curve, we experimentally determined fan characteristics with different frequencies and establish the data base for the curves. Based on ventilation network monitoring theory, we designed a monitoring system for ventilation network parameter monitoring and fan operating frequency automatic control. Using the absolute methane emission quantity to predict the air quantity requirement of branch and fan frequency, we established a f-ω regulation model based on fan frequency and absolute methane emission quantity. After analysing methane emission and distribution characteristics, using CO_2 to simulate the methane emission characteristics from a working face, we verified the correctness and rationality of the f-ω regulation model. The fan operation frequency is adjusted by the method of air adjustment change with methane emission quantity and the curve searching method after determining air quantity requirements. The results show that the air quantity in a branch strictly changes according to the f-ω regulation model, in the airincreasing dilution by fan frequency regulation, the CO_2 concentration is limited to the set threshold value. The paper verifies the practicability of a frequency regulation system and the feasibility of the frequency adjustment scheme and provides guidance for the construction of automatic frequency conversion control system in coal mine ventilation networks.展开更多
In the procedure of coal industry production, the losses of the persons and economy caused by the gas explosion accidents are most serious, therefore, prevention and control of the gas explosion accident of the coal m...In the procedure of coal industry production, the losses of the persons and economy caused by the gas explosion accidents are most serious, therefore, prevention and control of the gas explosion accident of the coal mines is an important issue needed to be solved urgently in the safety production work of our coal mines. The characteristic of time structure variation index characteristic was analyzed about gas concentration sequence of three measure points in the NO. 1I 1024 working face. It was found that the value of time variation about three measure points was mostly 1〈δ≤1.5, and gas emission presented consistently strong-clustering state twice, and the value of time variation presented continuous variation state in the active stage of gas concentration. Complex characteristics of the value indicated gas emission was continuously variable in time or space and presented the complex nonlinear characteristics. So the characteristic about gas emission system was correctly depicted and analyzed to gas emission system according to the relation of its state variation and essential of nonlinear system. The result also provided reliable warranty for its continued nonlinear research on gas emission.展开更多
文摘Traditional coal mine safety prediction methods are off-line and do not have dynamic prediction functions.The Support Vector Machine(SVM) is a new machine learning algorithm that has excellent properties.The least squares support vector machine(LS-SVM) algorithm is an improved algorithm of SVM.But the common LS-SVM algorithm,used directly in safety predictions,has some problems.We have first studied gas prediction problems and the basic theory of LS-SVM.Given these problems,we have investigated the affect of the time factor about safety prediction and present an on-line prediction algorithm,based on LS-SVM.Finally,given our observed data,we used the on-line algorithm to predict gas emissions and used other related algorithm to compare its performance.The simulation results have verified the validity of the new algorithm.
基金Supported by the National Natural Science Foundation of China（50534060）
文摘Based on the nowadays＇ condition, it is urgent that the gas detection cable communication system must be replaced by the wireless communication systems. The wireless sensors distributed in the environment can achieve the intelligent gas monitoring system. Apply with multilayer data fuse to design working tactics, and import the artificial neural networks to analyze detecting result. The wireless sensors system communicates with the control center through the optical fiber cable. All the gas sensor nodes distributed in coal mine are combined into an intelligent, flexible structure wireless network system, forming coal mine gas monitoring system based on wireless sensor network.
基金support from the National Key Research and Development Plan (No.2016YFC0801800)the National Natural Science Foundation of China (No.51404263)+2 种基金the National Natural Science Foundation of Jiangsu (No.BK20130203)the Project Funded by the Priority Academic Program Development (PAPD) of Jiangsu Higher Education Institutionsthe Fundamental Research Funds for the Central Universities (Nos.2014XT02 and 2014ZDPY03)
文摘In view of the difficulty of automatic adjustment, the recovery lag and the major accident potential of the mine ventilation system, an experimental model of the pipe net was established according to the typical one mine and one working face ventilation system of Daliuta coal mine. Using the best uniform approximation method of Chebyshev interpolation to fit the fan performance curve, we experimentally determined fan characteristics with different frequencies and establish the data base for the curves. Based on ventilation network monitoring theory, we designed a monitoring system for ventilation network parameter monitoring and fan operating frequency automatic control. Using the absolute methane emission quantity to predict the air quantity requirement of branch and fan frequency, we established a f-ω regulation model based on fan frequency and absolute methane emission quantity. After analysing methane emission and distribution characteristics, using CO_2 to simulate the methane emission characteristics from a working face, we verified the correctness and rationality of the f-ω regulation model. The fan operation frequency is adjusted by the method of air adjustment change with methane emission quantity and the curve searching method after determining air quantity requirements. The results show that the air quantity in a branch strictly changes according to the f-ω regulation model, in the airincreasing dilution by fan frequency regulation, the CO_2 concentration is limited to the set threshold value. The paper verifies the practicability of a frequency regulation system and the feasibility of the frequency adjustment scheme and provides guidance for the construction of automatic frequency conversion control system in coal mine ventilation networks.
基金Supported by Project Provincial Natural Science Foundation of Hunan （09J J3126） The Doctoral Research Activating Fund of Xiangtan University （09QDZ13, 10QDZ04）
文摘In the procedure of coal industry production, the losses of the persons and economy caused by the gas explosion accidents are most serious, therefore, prevention and control of the gas explosion accident of the coal mines is an important issue needed to be solved urgently in the safety production work of our coal mines. The characteristic of time structure variation index characteristic was analyzed about gas concentration sequence of three measure points in the NO. 1I 1024 working face. It was found that the value of time variation about three measure points was mostly 1〈δ≤1.5, and gas emission presented consistently strong-clustering state twice, and the value of time variation presented continuous variation state in the active stage of gas concentration. Complex characteristics of the value indicated gas emission was continuously variable in time or space and presented the complex nonlinear characteristics. So the characteristic about gas emission system was correctly depicted and analyzed to gas emission system according to the relation of its state variation and essential of nonlinear system. The result also provided reliable warranty for its continued nonlinear research on gas emission.
