International Journal of Computational Intelligence Systems

Volume 14, Issue 1, 2021, Pages 376 - 385

Research of Synergy Warning System for Gas Outburst Based on Entropy-Weight Bayesian

Authors
Jiayong Zhang, Zibo Ai*, ORCID, Liwen Guo, Xiao Cui
College of Mining Engineering, North China University of Science and Technology, Tangshan, Hebei, 063210, China
*Corresponding author. Email: 1256681979@qq.com
Corresponding Author
Zibo Ai
Received 29 June 2020, Accepted 9 December 2020, Available Online 18 December 2020.
DOI
10.2991/ijcis.d.201214.001How to use a DOI?
Keywords
Gas outburst; Characteristic values of gas emission; Entropy-Weight Bayesian; Synergy; Early warning
Abstract

Based on the statistical analysis of coal occurrence characteristics, and dynamic phenomena of coal and rock in Qianjiaying coal mine, China, an area–local outburst early warning system based on outburst key factors and early warning indicators was constructed. Statistical analysis of anomaly features of gas emission rate prior to outburst determined that the early warning index of the heading-face featured characteristic values of gas emission rate, including variance, peak difference, and fluctuation slope. Based on the entropy-weight method, the weight of indicators in the early warning process was determined, and the membership degree of each early warning grade under the synergistic effect of multiple indicators was calculated using Bayesian theory to determine the early warning grade. An outburst early warning model for Qianjiaying coal mine was constructed. The application client for an early warning system was developed, including a real-time gas data acquisition system and a visual early warning system. During the application of the early warning system in Qianjiaying Mine, it detected abnormal early warning indicators and issued early warning signals 6 hours in advance, avoiding casualties and equipment losses.

Copyright
© 2021 The Authors. Published by Atlantis Press B.V.
Open Access
This is an open access article distributed under the CC BY-NC 4.0 license (http://creativecommons.org/licenses/by-nc/4.0/).

Download article (PDF)
View full text (HTML)

Journal
International Journal of Computational Intelligence Systems
Volume-Issue
14 - 1
Pages
376 - 385
Publication Date
2020/12/18
ISSN (Online)
1875-6883
ISSN (Print)
1875-6891
DOI
10.2991/ijcis.d.201214.001How to use a DOI?
Copyright
© 2021 The Authors. Published by Atlantis Press B.V.
Open Access
This is an open access article distributed under the CC BY-NC 4.0 license (http://creativecommons.org/licenses/by-nc/4.0/).

Cite this article

TY  - JOUR
AU  - Jiayong Zhang
AU  - Zibo Ai
AU  - Liwen Guo
AU  - Xiao Cui
PY  - 2020
DA  - 2020/12/18
TI  - Research of Synergy Warning System for Gas Outburst Based on Entropy-Weight Bayesian
JO  - International Journal of Computational Intelligence Systems
SP  - 376
EP  - 385
VL  - 14
IS  - 1
SN  - 1875-6883
UR  - https://doi.org/10.2991/ijcis.d.201214.001
DO  - 10.2991/ijcis.d.201214.001
ID  - Zhang2020
ER  -