Coal mine fires,which can cause heavy casualties,environmental damages and a waste of coal resources,have become a worldwide problem.Aiming at overcoming the drawbacks,such as a low analysis efficiency,poor stability ...Coal mine fires,which can cause heavy casualties,environmental damages and a waste of coal resources,have become a worldwide problem.Aiming at overcoming the drawbacks,such as a low analysis efficiency,poor stability and large monitoring error,of the existing underground coal fire monitoring technology,a novel monitoring system based on non-dispersive infrared(NDIR)spectroscopy is developed.In this study,first,the measurement principle of NDIR sensor,the gas concentration calculation and its temperature compensation algorithms were expounded.Next,taking CO and CH_(4) as examples,the liner correlation coefficients of absorbance and the temperature correction factors of the two indicator gases were calculated,and then the errors of concentration measurement for CO,CO_(2),CH_(4) and C_(2)H_(4) were further analyzed.The results disclose that the designed NDIR sensors can satisfy the requirements of industrial standards for monitoring the indicator gases for coal fire hazards.For the established NDIR-based monitoring system,the NDIRbased spectrum analyzer and its auxiliary equipment boast intrinsically safe and explosion-proof performances and can achieve real-time and in-situ detection of indicator gases when installed close to the coal fire risk area underground.Furthermore,a field application of the NDIR-based monitoring system in a coal mine shows that the NDIR-based spectrum analyzer has a permissible difference from the chromatography in measuring the concentrations of various indicator gases.Besides,the advantages of high accuracy,quick analysis and excellent security of the NDIR-based monitoring system have promoted its application in many coal mines.展开更多
The coal of Anyuan Mine has the characteristic of easy spontaneous combustion. Conventional method is difficult to predict it. Coal samples from this mine were tested in laboratory. The data obtained from laboratory d...The coal of Anyuan Mine has the characteristic of easy spontaneous combustion. Conventional method is difficult to predict it. Coal samples from this mine were tested in laboratory. The data obtained from laboratory determination were initialized for the value which was defined as "K". The ratio of each index gas and value of "K", and the ratio of combination index gases and value of "K", were analyzed simultaneously. The research results show that for this coal mine, if there is carbon monoxide in the gas sample, the phenomenon of oxidation and temperature rising for coal exists in this mine; if there is C_2H_4 in the gas sample, the temperature of coal perhaps exceeds 130 °C. If the coal temperature is between 35 °C and 130 °C, prediction and forecast for coal spontaneous combustion depend on the value of Φ(CO)/K mainly; if the temperature of coal is between 130 °C and 300 °C, prediction and forecast for coal spontaneous combustion depend on the value of Φ(C_2H_6)/Φ(C_2H_2) and Φ(C_2H_6)/K. The research results provide experimental basis for the prediction of coal spontaneous combustion in Anyuan coal mine, and have better guidance on safe production of this coal mine.展开更多
Forecast is very important for preventing and controlling the disaster of spontaneous combustion （sponcom）. Gaseous products of coal, such as carbon monoxide, ethylene, propane and hydrogen, are commonly used as ind...Forecast is very important for preventing and controlling the disaster of spontaneous combustion （sponcom）. Gaseous products of coal, such as carbon monoxide, ethylene, propane and hydrogen, are commonly used as indicators to reflect its status quo of sponcom in coal mines. Nevertheless, since the corresponding relationship between the temperature and the indicators is non-linear and can＇t be depicted with simple mathematical formula, it is very difficult to diagnose and forecast coal sponcom by monitoring indicator gases＇ distribution. A forward feeding 3-layer artificial neural network （ANN） model is employed to express the corresponding relation between temperature and index gases of coal sponcom more accurately. A large amount of data from programmed temperature oxidation experiments were employed to train the network to gain the connection strength between nerve cells and to accomplish the model. It proved in real coal productions that the ANN model can forecast coal sponcom accurately.展开更多
基金Project(2021MD703848) supported by the China Postdoctoral Science FoundationProjects(52174229, 52174230)supported by the National Natural Science Foundation of China+1 种基金Project(2021-KF-23-04) supported by the Natural Science Foundation of Liaoning Province,ChinaProject(2020CXNL10) supported by the Fundamental Research Funds for the Central Universities,China。
文摘Coal mine fires,which can cause heavy casualties,environmental damages and a waste of coal resources,have become a worldwide problem.Aiming at overcoming the drawbacks,such as a low analysis efficiency,poor stability and large monitoring error,of the existing underground coal fire monitoring technology,a novel monitoring system based on non-dispersive infrared(NDIR)spectroscopy is developed.In this study,first,the measurement principle of NDIR sensor,the gas concentration calculation and its temperature compensation algorithms were expounded.Next,taking CO and CH_(4) as examples,the liner correlation coefficients of absorbance and the temperature correction factors of the two indicator gases were calculated,and then the errors of concentration measurement for CO,CO_(2),CH_(4) and C_(2)H_(4) were further analyzed.The results disclose that the designed NDIR sensors can satisfy the requirements of industrial standards for monitoring the indicator gases for coal fire hazards.For the established NDIR-based monitoring system,the NDIRbased spectrum analyzer and its auxiliary equipment boast intrinsically safe and explosion-proof performances and can achieve real-time and in-situ detection of indicator gases when installed close to the coal fire risk area underground.Furthermore,a field application of the NDIR-based monitoring system in a coal mine shows that the NDIR-based spectrum analyzer has a permissible difference from the chromatography in measuring the concentrations of various indicator gases.Besides,the advantages of high accuracy,quick analysis and excellent security of the NDIR-based monitoring system have promoted its application in many coal mines.
基金Projects(51274099,51474106)supported by the National Natural Science Foundation of China
文摘The coal of Anyuan Mine has the characteristic of easy spontaneous combustion. Conventional method is difficult to predict it. Coal samples from this mine were tested in laboratory. The data obtained from laboratory determination were initialized for the value which was defined as "K". The ratio of each index gas and value of "K", and the ratio of combination index gases and value of "K", were analyzed simultaneously. The research results show that for this coal mine, if there is carbon monoxide in the gas sample, the phenomenon of oxidation and temperature rising for coal exists in this mine; if there is C_2H_4 in the gas sample, the temperature of coal perhaps exceeds 130 °C. If the coal temperature is between 35 °C and 130 °C, prediction and forecast for coal spontaneous combustion depend on the value of Φ(CO)/K mainly; if the temperature of coal is between 130 °C and 300 °C, prediction and forecast for coal spontaneous combustion depend on the value of Φ(C_2H_6)/Φ(C_2H_2) and Φ(C_2H_6)/K. The research results provide experimental basis for the prediction of coal spontaneous combustion in Anyuan coal mine, and have better guidance on safe production of this coal mine.
基金Supported by the National Natural Science Foundation of China （10972178）
文摘Forecast is very important for preventing and controlling the disaster of spontaneous combustion （sponcom）. Gaseous products of coal, such as carbon monoxide, ethylene, propane and hydrogen, are commonly used as indicators to reflect its status quo of sponcom in coal mines. Nevertheless, since the corresponding relationship between the temperature and the indicators is non-linear and can＇t be depicted with simple mathematical formula, it is very difficult to diagnose and forecast coal sponcom by monitoring indicator gases＇ distribution. A forward feeding 3-layer artificial neural network （ANN） model is employed to express the corresponding relation between temperature and index gases of coal sponcom more accurately. A large amount of data from programmed temperature oxidation experiments were employed to train the network to gain the connection strength between nerve cells and to accomplish the model. It proved in real coal productions that the ANN model can forecast coal sponcom accurately.
