基于海量矿压监测数据的采场支架与顶板状态智能感知技术  

Technology of intelligent sensing of longwall shield supports status and roof strata based on massive shield pressure monitoring data

作　　者：程敬义[1,2] 万志军[1,2] PENG Syd S 张洪伟 邢轲轲 闫万梓 刘泗斐[1] CHENG Jingyi;WAN Zhijun;PENG Syd S;ZHANG Hongwei;XING Keke;YAN Wanzi;LIU Sifei(School of Mines,China University of Mining and Technology,Xuzhou 221116,China;State Key Laboratory of Coal Resources and Safe Mining,China University of Mining and Technology,Xuzhou 221116,China;Department of Mining Engineering,West Virginia University,Morgantown 26505,USA;School of Energy and Mining Engineering,China University of Mining and Technology (Beijing),Beijing 100083,China)

机构地区：[1]中国矿业大学矿业工程学院,江苏徐州221116 [2]中国矿业大学深部煤炭资源开采教育部重点实验室,江苏徐州221116 [3]西弗吉尼亚大学采矿工程系,西弗吉尼亚州摩根顿26505 [4]中国矿业大学(北京)能源与矿业学院,北京100083

出　　处：《煤炭学报》2020年第6期2090-2103,共14页Journal of China Coal Society

基　　金：中央高校基本科研业务费专项资金资助项目(2018QNA24);国家自然科学基金青年基金资助项目(51604267);国家重点基础研究发展计划(973)资助项目(2014CB046300)。

摘　　要：超前感知综采工作面顶板来压并预判冒顶事故、自主评价支护参数的适应性,是提高综采工作面安全高效及智能化水平的基础。围绕综采工作面支架与顶板状态智能感知的核心问题,基于综采工作面电液控制液压支架海量监测数据,开发了综采工作面支架与顶板状态智能感知系统(SSRI);结合大数据挖掘及工作循环分析技术,提出了用于支架压力分析的多因次工作循环特征参数;研究了安全阀开启、割煤及邻架移架、地质等多种因素影响下的单台支架承载特征及支架群组载荷转移分布规律;在深入解读支架阻力及活柱下缩时序曲线所蕴含的支架与围岩相互作用关系的基础上,构建了支架与顶板状态智能感知模型,实现了对顶板来压的预测、冒顶预警、支架适应性及支护质量评价,初步建立了基于海量矿压监测数据的采场支架与顶板状态智能感知技术体系。结果表明:①额定工作阻力不应作为评价支架承载能力的惟一或主要指标,初撑力、额定工作阻力及安全阀开启特性等参数共同决定了支架的承载特性及承载能力;②充分挖掘分析海量矿压监测数据,可以实现采场顶板灾害智能预警、支护质量评价及故障诊断等研究目标;③对海量监测数据的深入分析与利用是实现综采工作面支架围岩耦合自适应控制、支护参数自适应调整等智能化开采目标的前提与基础。It is of great importance to sense the roof weighting and roof caving in advance and self-evaluate initial resistance and working resistance for promoting the safety,efficiency and intelligent mining level in longwall penal.Based on the scientific principles of intelligent sensing of supports-roof status,this paper used the massive data collected by the electro-hydraulic controlled two-leg shield supports,data mining and working cycle analysis technique to extract the characteristic parameters.Then the supporting characteristics of a single support and the load transformation and distribution on the longwall panel considering setting pressure,yielding,and shearer’s cutting,as well as neighboring shields’advance were investigated.On the basis of in-depth explanation on the temporal-sequence curve of supporting resistance and the indicating relationship between support and surrounding rocks,the intelligent sensing model was constructed.The study concludes that①the rated working pressure should not be employed as the main indicator for evaluating the loading capacity of hydraulic support.Parameters,such as initial support pressure,rated working resistance and safety valve opening characteristics,would co-determine the bearing characteristics and capacity of hydraulic support.②By analyzing the massive data of support pressure,the goals of intelligent roof disaster warning,support quality evaluation and fault diagnosis can be achieved.③The in-depth analysis and utilization of massive monitored pressure data is the foundation to realize intelligent mining goals such as the self-control of hydraulic support pressure and adaptive adjustment of support parameters.

关 键 词：综采工作面 液压支架 矿压大数据 多因次工作循环特征参数 矿压智能分析 智能感知 

分 类 号：TD32[矿业工程—矿井建设] TD355.4