Research and Application of Mine Roadway Deformation Monitoring by Ultrasonic Technology

2014 ◽  
Vol 962-965 ◽  
pp. 1070-1074 ◽  
Author(s):  
Sheng Li ◽  
Xin Lin Jin ◽  
Ming Kun Luo ◽  
Chao Jun Fan
Keyword(s):  
Data Storage ◽  
Coal Mine ◽  
Deformation Monitoring ◽  
Mine Production ◽  
Ultrasonic Technology ◽  
Mine Roadway ◽  
Distance Sensor ◽  
Arduino Microcontroller ◽  
Roadway Deformation ◽  
Sd Card

Coal mine roadway deformation have a significant impact on coal mine production. Based on the Arduino microcontroller system and ultrasonic distance sensor, through temperature compensation and system fixes, designed a roadway deformation monitoring system which is high accuracy, low price and simple operation. Monitoring results through the LCD display, and SD card for data storage, the system achieved real-time monitoring of roadway deformation. The system was actually tested in coal mine, and achieved good monitoring results.

Study on Coal Mine Gas Monitoring System Based on Arduino

2014 ◽  
Vol 1073-1076 ◽  
pp. 2173-2176 ◽  
Author(s):  
Hui Chun Gao ◽  
Chao Jun Fan ◽  
Jun Wen Li ◽  
Ming Kun Luo
Keyword(s):  
Monitoring System ◽  
Coal Mine ◽  
Low Cost ◽  
Gas Monitoring ◽  
Underground Coal Mine ◽  
Gas Concentration ◽  
Coal Mine Gas ◽  
Good Market ◽  
Mine Gas ◽  
Sd Card

Aimed at the frequency gas accident of coal mine, we designed a coal mine gas monitoring system based on Arduino microcontroller. The MQ-4 gas sensor was used to collect gas concentration, wireless ZigBee was used to transfer data of gas concentration to PC. The system can display gas concentration real-timely by LCD and use SD card to store the data. The system will send out sound and light alarm when the gas concentration overruns. Industrial tests have been carried out in Wuyang coal mine. Results show that gas monitoring system can well adapt to environment of underground coal mine and the measurement is accurate. The system is real-time monitoring and early warning. It has the characteristics of low power consumption, low cost, wireless, good market prospect.

scholarly journals Research on Deformation Mechanisms of a High Geostress Soft Rock Roadway and Double-Shell Grouting Technology

Geofluids ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Fengnian Wang ◽  
Shizhuang Chen ◽  
Pan Gao ◽  
Zhibiao Guo ◽  
Zhigang Tao
Keyword(s):  
Mechanical Properties ◽  
Rock Mass ◽  
Large Deformation ◽  
Coal Mine ◽  
Soft Rock ◽  
In Situ Stress ◽  
Deformation Monitoring ◽  
Deformation And Failure ◽  
Working Face ◽  
High Geostress

In this study, the deformation characteristics and mechanical properties of coal and rock mass in the S2N5 working face of the Xiaokang coal mine are analyzed to address the problem of large deformation of soft rocks with high in situ stress surrounding roadways. Through a newly developed grouting pipe, a double-shell grouting technology, consisting of low-pressure grouting and high-pressure split grouting, is proposed for the Xiaokang coal mine. In addition, the effect of grouting is evaluated by borehole peeping and deformation monitoring. The results show that the double-shell grouting technology can effectively improve the overall mechanical properties of the surrounding coal and rock mass, preventing the large deformation and failure of the roadway. This technology can be useful when analyzing and preventing large deformation of soft rock roadways.

Characterisation of the Tunnel-Channel Wave around a Coal Mine Roadway Based on Synthetic and Real Data

2020 ◽  
Author(s):  
R. Czarny ◽  
M. Malinowski ◽  
M. Ćwiękała ◽  
S. Olechowski ◽  
Z. Isakow ◽  
...  
Keyword(s):  
Coal Mine ◽  
Real Data ◽  
Mine Roadway ◽  
Channel Wave ◽  
Tunnel Channel

scholarly journals Prediction of the First Weighting from the Working Face Roof in a Coal Mine Based on a GA-BP Neural Network

2019 ◽  
Vol 9 (19) ◽  
pp. 4159
Author(s):  
Tan ◽  
Yang ◽  
Chang ◽  
Zhao
Keyword(s):  
Neural Network ◽  
Coal Mine ◽  
Bp Neural Network ◽  
Prediction Models ◽  
Initial Pressure ◽  
Mining Area ◽  
Mine Pressure ◽  
Mine Production ◽  
Production Safety ◽  
Matlab Programming

The accidents caused by roof pressure seriously restrict the improvement of mines and threaten production safety. At present, most coal mine pressure forecasting methods still rely on expert experience and engineering analogies. Artificial neural network prediction technology has been widely used in coal mines. This new approach can predict the surface pressure on the roof, which is of great significance in coal mine production safety. In this paper, the mining pressure mechanism of coal seam roofs is summarized and studied, and 60 sets of initial pressure data from multiple working surfaces in the Datong mining area are collected for gray correlation analysis. Finally, 12 parameters are selected as the input parameters of the model. Suitable back propagation (BP) and GA(genetic algorithm)-BP initial roof pressure prediction models are established for the Datong mining area and trained with MATLAB programming. By comparing the training results, we found that the optimized GA-BP model has a larger determination coefficient, smaller error, and greater stability. The research shows that the prediction method based on the GA-BP neural network model is relatively reliable and has broad engineering application prospects as an auxiliary decision-making tool for coal mine production safety.

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