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[1]毕慧杰,任延平,张浩浩,等.基于多因素模式识别的煤与瓦斯突出预测研究[J].中国安全生产科学技术,2017,13(6):98-103.[doi:10.11731/j.issn.1673-193x.2017.06.016]
　BI Huijie,REN Yanping,ZHANG Haohao,et al.Dynamic prediction of coal and gas outburst based on multi-factor pattern recognition[J].JOURNAL OF SAFETY SCIENCE AND TECHNOLOGY,2017,13(6):98-103.[doi:10.11731/j.issn.1673-193x.2017.06.016]
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基于多因素模式识别的煤与瓦斯突出预测研究
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《中国安全生产科学技术》[ISSN:1673-193X/CN:11-5335/TB]

卷:
13
期数:
2017年6期
页码:
98-103
栏目:
现代职业安全卫生管理与技术
出版日期:
2017-06-30

文章信息/Info

Title:
Dynamic prediction of coal and gas outburst based on multi-factor pattern recognition
文章编号:
1673-193X(2017)-06-0098-06
作者:
毕慧杰; 任延平; 张浩浩; 杨鸿智
辽宁工程技术大学 矿业学院，辽宁 阜新 123000
Author(s):
BI Huijie;  REN Yanping;  ZHANG Haohao;  YANG Hongzhi
College of Mining, Liaoning Technical University, Fuxin Liaoning 123000, China
关键词:
模式识别; 煤与瓦斯突出; 工程扰动; VBA; 动态预测
Keywords:
pattern recognition;  coal and gas outburst;  engineering disturbance;  VBA;  dynamic prediction
分类号:
X936
DOI:
10.11731/j.issn.1673-193x.2017.06.016
文献标志码:
A
摘要:
采煤工作面煤与瓦斯突出是由煤层自然条件和工程扰动共同作用决定的，充分考虑煤层原始赋存条件和人类工程活动对煤与瓦斯突出的影响，建立多因素模式识别准则和方法，应用VBA技术完成了工作面煤与瓦斯突出危险性动态预测系统开发。以平顶山十矿己15-24080工作面为研究对象，将瓦斯含量、瓦斯压力、采动应力等因素作为工作面煤与瓦斯突出的主要影响因素，运用多因素模式识别方法实现了对工作面煤与瓦斯突出危险性分单元概率预测，且能够随着工作面不断推进进行动态预测和分级管理。研究结果表明：突出危险性预测结果与现场实况有较好的一致性，对煤矿安全开采具有良好的指导作用。
Abstract:
The coal and gas outburst in coal mining face is decided by the combined effect of natural conditions and engineering disturbance of coal seam. Fully considering the influence of the original occurrence conditions of coal seam and the human engineering activities on coal and gas outburst, the criteria and method of multi-factor pattern recognition were established, and the dynamic prediction system of coal and gas outburst risk in coal mining face was developed by using VBA technology. Taking Ji 15-24080 working face in Pingdingshan No.10 coal mine as the research object, the factors such as gas content, gas pressure, and mining stress etc., were taken as the main influence factors of coal and gas outburst. The unit probability prediction on coal and gas outburst risk in coal mining face was realized by using the multi-factor pattern recognition method, and the dynamic prediction and classification management can be carried out with the continuous advance of the working face. It showed that the results of outburst risk prediction were in good agreement with the field actual situation, and the method has a good guiding function to the safety mining of coal mine.

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备注/Memo

备注/Memo:
国家重点研发计划项目(2016YFC0801407)；国家自然科学基金项目(51674132)；深部岩土力学与地下工程国家重点实验室开放基金项目(SKLGDUEK1510)
更新日期/Last Update: 2017-07-11