煤矿采场智能岩层控制原理及方法
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摘要
煤矿采场智能岩层控制是智慧矿山及智能化开采的重要组成部分,是由"试误岩层控制"向"精准岩层控制"、由"静态岩层控制"向"动态岩层控制"发展的关键路径,是当前和今后一个时期采场岩层控制领域的重要发展方向之一。明确了采场智能岩层控制的内涵:即运用现代信息技术、人工智能技术及方法等,以采场智能装备系统为载体,实现开采全过程的采场围岩自动化、智能化控制。采场智能岩层控制分为3个关键环节:开采过程中的环境及设备运行数据的感知与汇集、动态分析与状态判别、实时决策控制与反馈。分析了矿山数据的构成、感知汇集方法及利用方式,矿山数据的主要用途为:岩层控制效果与事故灾害特征评价的大数据关联分析、为人工智能模型提供学习样本及分析对象、作为动态数值计算的反演分析参照对象、作为数据可视化与开采实景虚拟的信息来源。给出了采场智能岩层控制的动态分析与状态判别、实时决策与控制的技术路径,提出了采场智能岩层控制的关键科学问题:(1)环境及设备运行数据的感知汇集方法与技术;(2)矿山数据实时快速分析方法与技术;(3)采场智能岩层控制的关联分析与模型;(4)矿山数据可视化与开采场景虚拟构建;(5)基于大数据的快速动态数值计算原理及算法;(6)采场智能岩层控制"感知-分析-控制-反馈"全过程算法集成与系统构建。结合工程实际介绍了基于支持向量机和动态数值计算的采场智能岩层控制初步应用。
Intelligent ground control(IGC) at longwall working face is the significant and key part in wisdom coal mine and mining intelligentization,and is the crucial link from trial-and-error ground control to precise ground control as well as from static control to dynamic control.IGC is one of the important research fields in ground control.The connotation of IGC at longwall working face was defined,which is the ground control technology using modern information technology and artificial intelligent technology. The composition,perception and col-lection method and the utilization mode of coal mine data were analyzed.The coal mine data is used for big data analysis for evaluating ground control effectiveness and coal mine disaster characteristics,providing learning samples and analysis objects for artificial intelligence models,and used as the reference object for back analysis of dynamic numerical simulations and informationsource for data visualization and virtual reality of mining process. The pathways of dynamic analysis and state distinguish,real-time decision-making and control in IGC were proposed.The key scientific problems of IGC were clarified,that include the methodology and technology of perception and collection of coal mine data,the real-time rapid analysis of coal mine data,the intelligent model and algorithm of IGC,coal mine data visualization,the virtual reality for mining process,the principle and algorithm of rapid numerical simulation based on big data,and the algorithm integration and system construction for the whole process of IGC.Two specific IGC application cases on Support Vector Machine and Dynamic Numerical Simulation were introduced.
Intelligent ground control(IGC) at longwall working face is the significant and key part in wisdom coal mine and mining intelligentization,and is the crucial link from trial-and-error ground control to precise ground control as well as from static control to dynamic control.IGC is one of the important research fields in ground control.The connotation of IGC at longwall working face was defined,which is the ground control technology using modern information technology and artificial intelligent technology. The composition,perception and col-lection method and the utilization mode of coal mine data were analyzed.The coal mine data is used for big data analysis for evaluating ground control effectiveness and coal mine disaster characteristics,providing learning samples and analysis objects for artificial intelligence models,and used as the reference object for back analysis of dynamic numerical simulations and informationsource for data visualization and virtual reality of mining process. The pathways of dynamic analysis and state distinguish,real-time decision-making and control in IGC were proposed.The key scientific problems of IGC were clarified,that include the methodology and technology of perception and collection of coal mine data,the real-time rapid analysis of coal mine data,the intelligent model and algorithm of IGC,coal mine data visualization,the virtual reality for mining process,the principle and algorithm of rapid numerical simulation based on big data,and the algorithm integration and system construction for the whole process of IGC.Two specific IGC application cases on Support Vector Machine and Dynamic Numerical Simulation were introduced.
引文
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[35] MAHDEVARI S,TORABI S R,MONJEZI M.Application of artificial intelligence algorithms in predicting tunnel convergence to avoid TBM jamming phenomenon[J]. International Journal of Rock Mechanics and Mining Sciences,2012,55:33-44.
[36] EBRAHIMABADI A,AZIMIPOUR M,BAHREINI A.Prediction of roadheaders’ performance using artificial neural network approaches(MLP and KOSFM)[J]. Journal of Rock Mechanics and Geotechnical Engineering,2015,7(5):573-583.
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[2] KEET C M,AWRYNOWICZ A,D’AMATO C,et al.The data mining optimization ontology[J]. Web Semantics:Science,Services and Agents on the World Wide Web,2015,32:43-53.
[3] JARRAHI M H. Artificial intelligence and the future of work:Human-AI symbiosis in organizational decision making[J]. Business Horizons,2018,61(4):577-586.
[4] CHAO X,KOU G,LI T,et al.Jie Ke versus AlphaGo:A ranking approach using decision making method for large-scale data with incomplete information[J].European Journal of Operational Research,2018,265(1):239-247.
[5] NI J,HU J.Dynamics control of autonomous vehicle at driving limits and experiment on an autonomous formula racing car[J]. Mechanical Systems and Signal Processing,2017,90:154-174.
[6] WU Z,LU Z,ZHANG W,et al.A data-driven approach of performance evaluation for cache server groups in content delivery network[J].Journal of Parallel and Distributed Computing,2018,119:162-171.
[7] AUGIER M,DEW N,KNUDSEN T,et al.Organizational persistence in the use of war gaming and scenario planning[J]. Long Range Planning,2018,51(4):511-525.
[8] LUXTON D D. Artificial Intelligence in Behavioral and Mental Health Care[M]. Cambridge,Massachusetts:Academic Press,2016:1-26.
[9] HUANG H,LI Q,ZHANG D.Deep learning based image recognition for crack and leakage defects of metro shield tunnel[J]. Tunnelling and Underground Space Technology,2018,77:166-176.
[10] LEE J,DAVARI H,SINGH J,et al.Industrial artificial intelligence for industry 4. 0-based manufacturing systems[J]. Manufacturing Letters,2018:https://doi. org/10. 1016/j. mfglet. 2018. 09.002(In Press).
[11]中华人民共和国工业和信息化部.《智能制造发展规划(2016—2020年)》正式发布[EB/OL]. http://www. miit. gov. cn/n1146290/n4388791/c5405924/content.html.,2018-09-20.
[12]王国法,范京道,徐亚军,等.煤炭智能化开采关键技术创新进展与展望[J].工矿自动化,2018,44(2):5-12.WANG Guofa,FAN Jingdao,XU Yajun,et al. Innovation progress and prospect on key technologies of intelligent coal mining[J]. Industry and Mine Automation,2018,44(2):5-12.
[13]王国法,李占平,张金虎.互联网,大采高工作面智能化升级关键技术[J].煤炭科学技术,2016,44(7):15-21.WANG Guofa,LI Zhanping,ZHANG Jinhu.Key technology of intelligent upgrading reconstruction of internet plus high cutting coal mining face[J].Coal Science and Technology,2016,44(7):15-21.
[14]王国法.工作面支护与液压支架技术理论体系[J].煤炭学报,2014,39(8):1593-1601.WANG Guofa. Theory system of working face support system and hydraulic roof support technology[J].Journal of China Coal Society,2014,39(8):1593-1601.
[15]徐亚军,王国法.液压支架群组支护原理与承载特性[J].岩石力学与工程学报,2017,36(S1):3367-3373.XU Yajun,WANG Guofa.Supporting principle and bearing characteristics of hydraulic powered roof support groups[J].Chinese Journal of Rock Mechanics and Engineering,2017,36(S1):3367-3373.
[16]王国法,张金虎.煤矿高效开采技术与装备的最新发展[J].煤矿开采,2018,23(1):1-4,12.WANG Guofa,ZHANG Jinhu. Recent development of coal mine highly effective mining technology and equipment[J].Coal Mining Technology,2018,23(1):1-4,12.
[17]王国法.煤矿高效开采工作面成套装备技术的创新和发展[A].2010年中国煤炭学会青年科技工作者论坛[C].北京,2010.WANG Guofa.Innovation and development of completed set equipment and technology for high efficient coal mining face in underground coal mine[A].Proceedings of the 2001 China Coal Society Youth Science and Technology Workers Forum[C].Beijing,2010.
[18]王国法.煤矿高效开采工作面成套装备技术创新与发展[J].煤炭科学技术,2010,38(1):63-68,106.WANG Guofa.Innovation and development of completed set equipment and technology for high efficient coal mining face in underground coal mine[J].Coal Science and Technology,2010,38(1):63-68,106.
[19]王国法.综采自动化智能化无人化成套技术与装备发展方向[J].煤炭科学技术,2014,42(9):30-34,39.WANG Guofa. Development orientation of complete fully-mechanized automation,intelligent and unmanned mining technology and equipment[J]. Coal Science and Technology,2014,42(9):30-34,39.
[20]任怀伟,王国法,李首滨,等.7 m大采高综采智能化工作面成套装备研制[J].煤炭科学技术,2015,43(11):116-121.REN Huaiwei,WANG Guofa,LI Shoubin,et al.Development of intelligent sets equipment for fully-mechanized 7 m height mining face[J].Coal Science and Technology,2015,43(11):116-121.
[21]王国法,王虹,任怀伟,等.智慧煤矿2025情景目标和发展路径[J].煤炭学报,2018,43(2):295-305.WANG Guofa,WANG Hong,REN Huaiwei,et al. 2025 scenarios and development path of intelligent coal mine[J].Journal of China Coal Society,2018,43(2):295-305.
[22]袁亮.煤炭精准开采科学构想[J].煤炭学报,2017,42(1):1-7.YUAN Liang. Scientific conception of precision coal mining[J].Journal of China Coal Society,2017,42(1):1-7.
[23]袁亮.开展基于人工智能的煤炭精准开采研究,为深地开发提供科技支撑[J].科技导报,2017,35(14):1.YUAN Liang.Carry out research on precision coal mining based on artificial intelligence,and provide scientific support for deep development[J].Science&Technology Review,2017,35(14):1.
[24]宋振骐.安全高效智能化开采技术现状与展望[J].煤炭与化工,2014,37(1):1-4,9.SONG Zhenqi.Intelligent safe and efficient mining technology present situation and prospect[J]. Coal and Chemical Industry,2014,37(1):1-4,9.
[25]何敏.智慧矿山定义探讨[J].工矿自动化,2017,43(9):12-16.HE Min. Discussion on definition of wisdom mine[J]. Industry and Mine Automation,2017,43(9):12-16.
[26]田成金.煤炭智能化开采模式和关键技术研究[J].工矿自动化,2016,42(11):28-32.TIAN Chengjin. Research of intelligentized coal mining mode and key technologies[J]. Industry and Mine Automation,2016,42(11):28-32.
[27]张昊,葛世荣.无人驾驶采煤机关键技术探讨[J].工矿自动化,2016,42(2):31-33.ZHANG Hao,GE Shirong. Study on key technologies of unmanned driving shearer[J]. Industry and Mine Automation,2016,42(2):31-33.
[28]葛世荣,苏忠水,李昂,等.基于地理信息系统(GIS)的采煤机定位定姿技术研究[J].煤炭学报,2015,40(11):2503-2508.GE Shirong,SU Zhongshui,LI Ang,et al. Study on the positioning and orientation of a shearer based on geographic information system[J].Journal of China Coal Society,2015,40(11):2503-2508.
[29]葛世荣.智能化采煤装备的关键技术[J].煤炭科学技术,2014,42(9):7-11.GE Shirong. Key technology of intelligent coal mining equipment[J].Coal Science and Technology,2014,42(9):7-11.
[30] S'LEZAK D,GRZEGOROWSKI M,JANUSZ A,et al.A framework for learning and embedding multi-sensor forecasting models into a decision support system:A case study of methane concentration in coal mines[J].Information Sciences,2018,451-452:112-133.
[31] RUILIN Z,LOWNDES I S. The application of a coupled artificial neural network and fault tree analysis model to predict coal and gas outbursts[J].International Journal of Coal Geology,2010,84(2):141-152.
[32] JANUSZ A,GRZEGOROWSKI M,MICHALAK M,et al.Predicting seismic events in coal mines based on underground sensor measurements[J].Engineering Applications of Artificial Intelligence,2017,64:83-94.
[33] ZHOU J,LI X B,SHI X Z,et al.Predicting pillar stability for underground mine using Fisher discriminant analysis and SVM methods[J].Transactions of Nonferrous Metals Society of China,2011,21(12):2734-2743.
[34] ZHANG X Q,WANG H B,YU H Z. Neural network based algorithm and simulation of information fusion in the coal mine[J].Journal of China University of Mining and Technology,2007,17(4):595-598.
[35] MAHDEVARI S,TORABI S R,MONJEZI M.Application of artificial intelligence algorithms in predicting tunnel convergence to avoid TBM jamming phenomenon[J]. International Journal of Rock Mechanics and Mining Sciences,2012,55:33-44.
[36] EBRAHIMABADI A,AZIMIPOUR M,BAHREINI A.Prediction of roadheaders’ performance using artificial neural network approaches(MLP and KOSFM)[J]. Journal of Rock Mechanics and Geotechnical Engineering,2015,7(5):573-583.
[37] POOLE D L,MACKWORTH A K,GOEBEL R.Computational intelligence:A logical approach[M]. New York:Oxford University Press,1998.
[38] LANEY D.3D data management:Controlling data volume,velocity and variety[J].META Group Research Note,2001,6(70):1.
[39]彭赐灯.立柱压力可以说明什么问题[J].煤矿开采,1999,(1):41-43.PENG Cideng. The implication of pressure of shield leg[J].Coal Mining Technology,1999,(1):41-43.
[40] CHIANG L H,KOTANCHEK M E,KORDON A K.Fault diagnosis based on Fisher discriminant analysis and support vector machines[J]. Computers&Chemical Engineering,2004,28(8):1389-1401.
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