Proceedings of the 2015 4th International Conference on Sensors, Measurement and Intelligent Materials

Study on laws of gas occurrence and emission of deep coal seam in Yaoqiao Coal Mine

Authors
Genyin Cheng, Yifei Zhou, Liming Qi, Shan Feng, Jian Cao
Corresponding Author
Genyin Cheng
Available Online January 2016.
DOI
https://doi.org/10.2991/icsmim-15.2016.148How to use a DOI?
Keywords
Gas content; Gas Emission; Laws of gas occurrence and emission
Abstract

To grasp the gas occurrence and emission laws of Yaoqiao deep coal mining area (West ten mining area and the second central mining area) in No.7 Coal Seam, the neural network is used to analyze the relationship and degree of influence factors between gas occurrence and gas content. We construct a prediction model of gas content to predict the gas content of the corresponding position and draw contour map of the gas content. We combine gas geological data of Yaoqiao coal mine and summarize the law of gas occurrence and emission to guide the work of gas prevention and control and enhance the prevention level of gas disaster.

Copyright
© 2016, the Authors. Published by Atlantis Press.
Open Access
This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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Volume Title
Proceedings of the 2015 4th International Conference on Sensors, Measurement and Intelligent Materials
Series
Advances in Computer Science Research
Publication Date
January 2016
ISBN
978-94-6252-157-5
ISSN
2352-538X
DOI
https://doi.org/10.2991/icsmim-15.2016.148How to use a DOI?
Copyright
© 2016, the Authors. Published by Atlantis Press.
Open Access
This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

Cite this article

TY  - CONF
AU  - Genyin Cheng
AU  - Yifei Zhou
AU  - Liming Qi
AU  - Shan Feng
AU  - Jian Cao
PY  - 2016/01
DA  - 2016/01
TI  - Study on laws of gas occurrence and emission of deep coal seam in Yaoqiao Coal Mine
BT  - Proceedings of the 2015 4th International Conference on Sensors, Measurement and Intelligent Materials
PB  - Atlantis Press
SP  - 791
EP  - 796
SN  - 2352-538X
UR  - https://doi.org/10.2991/icsmim-15.2016.148
DO  - https://doi.org/10.2991/icsmim-15.2016.148
ID  - Cheng2016/01
ER  -