Experimental study on effects of load damage precursor information and response characteristic of gas-containing coal for mining safety based on acoustic emission

声发射 煤矿开采 环境科学 计算机科学 材料科学 工程类 废物管理 复合材料
作者
Ming‐Tian Zhang,Jinyang Fan,Junsheng Du,Deyi Jiang,Jie Chen,Qiang Yuan,Lei Hao,Ya Wang
出处
期刊:Chemical Engineering Research & Design [Elsevier BV]
卷期号:184: 993-1010 被引量:5
标识
DOI:10.1016/j.psep.2024.01.057
摘要

Acoustic emission (AE) of coal is an accompanying phenomenon closely related to the internal damage of coal. Studying its information response characteristics can reveal the damage destruction mechanism of coal and improve the effectiveness and reliability of mining disaster warnings. In order to further study the response characteristics of load damage precursor information for gas-containing coals, the latest self-developed triaxial experimental system was used to conduct cyclic load damage tests on gas-containing coal under different adsorption pressures. The evolution of the AE signals and gas outflow during the load damage process of coal samples was analyzed. Normal distribution theory was innovatively introduced to construct the relationship index between AE parameters and damage precursor information of gas-containing coal under loads. Moreover, a damage identification model for coal samples was established. Results show that: (1) Under different gas pressure conditions, the damage form of gas-containing coal is mainly "X" type shear damage; (2) In terms of the temporal characteristics of the AE and gas outflow, the characteristic parameters of the AE ringing counts can be described in four phases, with an overall gas outflow transient trend of a slight decrease followed by a rapid increase; (3) The data fitting results show that the absolute energy of AE b value has the characteristics of normal distribution. Furthermore, the right end of the interval corresponding to each probability condition of the normal distribution (98%, 95%, and 90%) can be used as the threshold to identify the precursor information of each damage stage of the coal samples, thus quantitatively and accurately predicting the damage stage of the coal samples. The findings of this study not only provide scientific references for the prediction and early warning of coal and gas outburst in coal mines but also contribute to the enhancement of process safety risk control.
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