摘要
针对综采工作面液压支架阻力监测的大数据分析、来压预测预警及来压影响因素等问题,提出了基于大数据的矿压预测模型,依据FLAC^(3D)数值模拟及刘庄煤矿实际监测结果,将推采划分为稳定推采、停采检修、快速推采三个阶段,得出了以下结论:大数据来压值预测准确率约在65.5%,来压范围预测准确率约在51.7%;单工作日内推采距离越大,顶板下沉量越大,支架来压值越大;在工作面稳定推采阶段来压次数少,来压步距大;停采阶段工作面整体来压;快速推采阶段来压频繁来压步距小,根据开采相应阶段来压变化,刘庄矿预测修正值K取±1.5。
Aiming at the needs for big data analysis of hydraulic support resistance monitoring,and for the prediction,early warning and influencing factors analysis of weighting in fully mechanized mining face,a mining pressure prediction model based on big data is proposed.According to the FLAC^(3D)numerical simulation and the actual monitoring results of Liuzhuang Coal Mine,the advancing mining is divided into three stages:stable advancing mining,pausing maintenance,and rapid advancing mining.The following conclusions are drawn:the big data prediction accuracy of the weighting intensity is about 65.5%,and that of the weighting range is about 51.7%.The larger the mining distance in a single working day,the greater the subsidence of the roof,and the greater the weighting value of the support;in the stable mining stage,less weighting events occur,and the weighting interval is larger;in the pausing stage,the weighting occurs in the whole working face;in the rapid mining stage,the weighting is frequent and the interval is small.According to the change of the weighting in the corresponding stage of mining,the prediction correction value K of Liuzhuang Mine is±1.5.
作者
路建军
周宏范
冯明
王泽雨
夏方迁
LU Jian-jun;ZHOU Hong-fan;FENG Ming;WANG Ze-yu;XIA Fang-qian(China Coal Xinji Energy Co.,Ltd.,Huainan 232001,China;Beijing Anke Xingye Technology Co.,Ltd.,Beijing 102200,China)
出处
《煤炭工程》
北大核心
2022年第11期118-123,共6页
Coal Engineering
关键词
支架阻力
矿压显现
周期来压
来压预测
support resistance
mine pressure behavior
periodical weighting
weighting prediction
