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

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.

Keywords

• Gas outburst
• Characteristic values of gas emission
• Entropy-Weight Bayesian
• Synergy
• Early warning