YU Bin，TAI Yang，XU Gang，et al. Theory and key technologies for intelligent fully-mechanized top-coal caving faces of annual production of millions of tons[J]. Coal Science and Technology，2024，52（9）：48−67
. DOI: 10.12438/cst.2024-0880| Citation: |
YU Bin，TAI Yang，XU Gang，et al. Theory and key technologies for intelligent fully-mechanized top-coal caving faces of annual production of millions of tons[J]. Coal Science and Technology，2024，52（9）：48−67 . DOI: 10.12438/cst.2024-0880
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YU Bin: 于斌，重庆大学二级教授，中国煤炭学会副理事长。主要从事坚硬顶板特厚煤层安全高效、高回收率开采以及绿色开发方面研究。获国家科技进步一等奖（排名第2）、二等奖3项（排名第2、6、7），省部级奖16项（13项第1），授权发明专利79项（48项第1），发表论文120篇（SCI检索29篇），出版专著6部（均排第1）。获全国杰出工程师奖、孙越崎能源大奖、全国十佳优秀科技工作者提名奖、重庆市优秀科学家，入围全球顶尖科学家“终身科学影响力排行榜”
The research focused on addressing various challenges in intelligent top caving theory, intelligent perception and recognition key technology, intelligent caving comprehensive decision-making technology, and remote caving intelligent control technology of fully mechanized top coal caving face. This is being done under the “13th Five-Year” national key research and development plan, specifically designed for the key technology and demonstration of intelligent fully mechanized top coal caving mining method with annual production of 10 million of tons in extra-thick coal seam. The research has resulted in the following outcomes: ① Comprehensive experiments were conducted to understand the interaction process of crushing and migrating of roof and top coal combination (RTCC) and the fragmentation distribution of RTCC under different roof conditions. A three-dimensional laser goaf space detection technology has been developed, and the arching phenomenon of top coal blocks on contact, at coal discharge process with multiple coal discharge ports, has been validated. In addition, the numerical simulation of multi-port intelligent coal caving in extra-thick coal seam is carried out with the constraints of mining and caving coordination, high recovery rate, and low gangue ratio, and the number of coal caving ports is determined. those provide a reliable theoretical basis for optimizing intelligent coal-caving processes. ②The research has explored the geological information and physical characteristics of coal and gangue in the working face, along with full-cycle sensing elements of the top coal caving process. This has led to the development of a comprehensive sensing technology system, including real time detection of top coal thickness, accurate identification of coal and gangue, and dynamic measurement of coal flow, providing crucial data information support for decision making of intelligent coal top caving technology. ③A multi-source information database has been established for the man-machine-environment interface, and a decision-making model has been developed for fully mechanized top caving in extra-thick coal seams. An intelligent coal top caving decision-making software based on the Q-learning algorithm has been created, utilizing artificial intelligence for coal and gangue identification, top coal thickness detection, and coal quantity monitoring. ④A high-precision inertial navigation monitoring and control technology for intelligent fully mechanized caving faces has been developed, enabling real-time positioning, attitude monitoring, and action control for the shearer, hydraulic support, and scraper conveyor. An intelligent mine-integrated communication scheduling system and a remote-integrated control platform for fully mechanized caving have also been established. Those allow for the successful implementation of intelligent coal caving in remote one-button start mode. ⑤Advanced technologies such as ground-penetrating radar for top coal thickness detection, vibration-audio-hyperspectral for coal-gangue identification, and laser three-dimensional scanning for real-time coal caving monitoring are utilized in the 8222 Working Face of Tashan Mine. Intelligent coal caving decision software is applied in fully mechanized caving operations, leading to a control of errors within 10.71% for top coal thickness detection, 9.32% for mixed gangue rate, and 7.8% for coal caving amount. On average, each coal caving cycle now saves about 30 minutes, leading to intelligent and efficient coal caving operations with an annual output of 15 million tons.
| [1] |
于斌,杨敬轩,刘长友,等. 大空间采场覆岩结构特征及其矿压作用机理[J]. 煤炭学报,2019,44(11):3295−3307.
YU Bin ,YANG Jingxuan ,LIU Zhangyou ,et al. Overburden structure and mechanism of rock pressure in large space stope[J]. Journal of China Coal Society,2019,44(11):3295−3307.
|
| [2] |
于雷,闫少宏. 特厚煤层综放开采顶板岩层控制基本原理[J]. 煤炭学报,2020,45(S1):31−37.
YU Lei,YAN Shaohong. The basic principle of roof strata control in fully mechanized caving mining of extra thick coal seam[J]. Journal of China Coal Society,2020,45(S1):31−37.
|
| [3] |
于斌. 大同矿区综采40 a开采技术研究[J]. 煤炭学报,2010,35(11):1772−1777.
YU Bin. Study on fully mechanized coal mining technology in passed 40 years in Datong mining area[J]. Journal of China Coal Society,2010,35(11):1772−1777.
|
| [4] |
吴兴利,刘大同,张东方. 大同综采40a综合机械化装备的研发[J]. 煤炭学报,2010,35(11):1893−1897.
WU Xingli,LIU Datong,ZHANG Dongfang. Research and development of fully mechanized coal mining equipmentin passed 40 years of Datong fully mechanized coal mining[J]. Journal of China Coal Society,2010,35(11):1893−1897.
|
| [5] |
康红普,徐刚,王彪谋,等. 我国煤炭开采与岩层控制技术发展40a及展望[J]. 采矿与岩层控制工程学报,2019,1(2):7−39.
KANG Hongpu,XU Gang,WANG Biaomou,et al. Forty years development and prospects of underground coal mining and strata control technologies in China[J]. Journal of Mining And Strata Control Engineering,2019,1(2):7−39.
|
| [6] |
周开平. 薄煤层智能化无人工作面成套装备与技术[J]. 煤炭科学技术,2020,48(3):59−67.
ZHOU Kaiping. Complete equipment and technology for intelligent unmanned workingface in thin coal seam[J]. Coal Science and Technology,2020,48(3):59−67.
|
| [7] |
王国法,张德生. 煤炭智能化综采技术创新实践与发展展望[J]. 中国矿业大学学报,2018,47(3):459−467.
WANG Guofa,ZHANG Desheng. Innovation practice and development prospect of intelligent fully mechanized technology for coal mining[J]. Journal of China University of Mining & Technology,2018,47(3):459−467.
|
| [8] |
韩会军,王国法,许永祥,等. 6~10 m厚煤层超大采高液压支架及其工作面系统自适应智能耦合控制[J]. 煤炭科学技术,2024,52(5):276−288. doi: 10.12438/cst.2023-1692
HAN Huijun,WANG Guofa,XU Yongxiang,et al. Adaptive intelligent coupling control of hydraulic support and working face system for 6−10 m super high mining in thick coal seams[J]. Coal Science and Technology,2024,52(5):276−288. doi: 10.12438/cst.2023-1692
|
| [9] |
任怀伟,孟祥军,李政,等. 8 m大采高综采工作面智能控制系统关键技术研究[J]. 煤炭科学技术,2017,45(11):37−44.
REN Huaiwei,MENG Xiangjun,LI Zheng,et al. Study on key technology of intelligent control system applied in 8 m large mining height fully-mechanized face[J]. Coal Science and Technology,2017,45(11):37−44.
|
| [10] |
李首滨,李森,张守祥,等. 综采工作面智能感知与智能控制关键技术与应用[J]. 煤炭科学技术,2021,49(4):28−39.
LI Shoubin,LI Sen,ZHANG Shouxiang,et al. Key technology and application of intelligent perception and intelligent control in fully mechanized mining face[J]. Coal Science and Technology,2021,49(4):28−39.
|
| [11] |
高有进,杨艺,常亚军,等. 综采工作面智能化关键技术现状与展望[J]. 煤炭科学技术,2021,49(8):1−22.
GAO Youjin,YANG Yi,CHANG Yajun,et al. Status and prospect of key technologies of intelligentization of fully-mechanized coal mining face[J]. Coal Science and Technology,2021,49(8):1−22.
|
| [12] |
魏文艳. 综采工作面智能化开采技术发展现状及展望[J]. 煤炭科学技术,2022,50(S2):244−253.
WEI Wenyan. Development status and prospect of intelligent mining technology of longwall mining[J]. Coal Science and Technology,2022,50(S2):244−253.
|
| [13] |
郭金刚,李化敏,王祖洸,等. 综采工作面智能化开采路径及关键技术[J]. 煤炭科学技术,2021,49(1):128−138.
GUO Jingang,LI Huamin,WANG Zuguang,et al. Path and key technologies of intelligent mining in fully-mechanized coal mining face[J]. Coal Science and Technology,2021,49(1):128−138.
|
| [14] |
孟祥军,李明忠,孙计爽,等. 千万吨级矿井智能化综采成套装备及关键技术[J]. 煤炭科学技术,2020,48(7):47−54.
MENG Xiangjun,LI Mingzhong,SUN Jishuang,et al. Complete sets of equipment and key technologies for intelligent fully-mechanized mining of ten-million tonnage level mine[J]. Coal Science and Technology,2020,48(7):47−54.
|
| [15] |
贺海涛. 综采工作面智能化开采系统关键技术[J]. 煤炭科学技术,2021,49(S1):8−15.
HE Haitao. Key technology of intelligent mining system in fully-mechanized mining face[J]. Coal Science and Technology,2021,49(S1):8−15.
|
| [16] |
刘小雄,马宝,陈建华. 综采工作面智能化开采装备与技术研究[J]. 煤炭科学技术,2022,50(S2):271−276.
LIU Xiaoxiong,MA Bao,CHEN Jianhua. Study on coal seam transparency technology of thin coal seam intelligent mining face[J]. Coal Science and Technology,2022,50(S2):271−276.
|
| [17] |
王学文,刘曙光,王雪松,等. AR/VR融合驱动的综采工作面智能监控关键技术研究与试验[J]. 煤炭学报,2022,47(2):969−985.
WANG Xuewen,LIU Shuguang,WANG Xuesong,et al. Research and test on key technologies of intelligent monitoring and controldriven by AR/VR for fully mechanised coal-mining face[J]. Journal of China Coal Society,2022,47(2):969−985.
|
| [18] |
苏杰,王新坤. 寸草塔煤矿综采工作面智能化建设关键技术研究与应用[J]. 煤炭科学技术,2022,50(S1):250−256.
SU Jie,WANG Xinkun. Research and application of key technologies for intelligent constructionof fully mechanised mining face in Cuncaota Coal Mine[J]. Coal Science and Technology,2022,50(S1):250−256.
|
| [19] |
付翔,李浩杰,张锦涛,等. 综采液压支架中部跟机多模态人机协同控制系统[J]. 煤炭学报,2024,49(3):1717−1730.
FU Xiang,LI Haojie,ZHANG Jintao,et al. Multimodal human-machine collaborative control system for hydraulic supports following the shearer in the middle range of fully mechanized mining face[J]. Journal of China Coal Society,2024,49(3):1717−1730.
|
| [20] |
王国法,庞义辉,许永祥,等. 厚煤层智能绿色高效开采技术与装备研发进展[J]. 采矿与安全工程学报,2023,40(5):882−893.
WANG Guofa,PANG Yihui,XU Yongxiang,et al. Development of intelligent green and efficient mining technology and equipment for thick coal seam[J]. Journal of Mining & Safety Engineering,2023,40(5):882−893.
|
| [21] |
庞义辉,刘新华,马英. 千万吨矿井群综放智能化开采设备关键技术[J]. 煤炭科学技术,2015,43(8):97−101.
PANG Yihui,LIU Xinhua,MA Ying. Key technologies of fully-mechanized caving intelligent mining equipment in ten million tons of mines group[J]. Coal Science and Technology,2015,43(8):97−101.
|
| [22] |
于斌,徐刚,黄志增,等. 特厚煤层智能化综放开采理论与关键技术架构[J]. 煤炭学报,2019,44(1):42−53.
YU Bin,XU Gang,HUANG Zhizeng,et al. Theory and its key technology framework of intelligentized fully-mechanized caving mining in extremely thick coal seam[J]. Journal of China Coal Society,2019,44(1):42−53.
|
| [23] |
李伟. 综放开采智能化控制系统研发与应用[J]. 煤炭科学技术,2021,49(10):128−135.
LI WeiLi. Research and application of intelligent control system forfull-mechanized caving mining[J]. Coal Science and Technology,2021,49(10):128−135.
|
| [24] |
宋选民,朱德福,王仲伦,等. 我国煤矿综放开采40年:理论与技术装备研究进展[J]. 煤炭科学技术,2021,49(3):1−29.
SONG Xuanmin,ZHU Defu,WANG Zhonglun,et al. Advances on longwall fully-mechanized top-coal caving mining technology inChina during past 40 years:theory,equipment and approach[J]. Coal Science and Technology,2021,49(3):1−29.
|
| [25] |
路正雄,郭卫,张帆,等. 基于数据驱动的综采装备协同控制系统架构及关键技术[J]. 煤炭科学技术,2020,48(7):195−205.
LU Zhengxiong,GUO Wei,ZHANG Fan,et al. Collaborative control system architecture and key technologies of fully-mechanized mining equipment based on data drive[J]. Coal Science and Technology,2020,48(7):195−205.
|
| [26] |
张德文,张文坦,郝胜峰. 金鸡滩煤矿智能煤流均衡系统研制与应用[J]. 煤炭科学技术,2021,49(S1):142−145.
ZHANG Dewen,ZHANG Wentan,HAO Shengfeng. Application and development on intelligent coal flow balancing system in Jinjitan Coalmine[J]. Coal Science and Technology,2021,49(S1):142−145.
|
| [27] |
陈志军. 灵新煤矿综采工作面视频监控系统应用优化与探索[J]. 煤炭科学技术,2021,49(S1):146−149.
CHEN Zhijun. Optimization and exploration of video monitoring system infully-mechanized mining face of Lingxin coal mine[J]. Coal Science and Technology,2021,49(S1):146−149.
|
| [28] |
王占飞,王耀. 煤矿井下煤流运输系统智能调速研究与应用[J]. 煤炭科学技术,2022,50(S1):283−288.
WANG Zhanfei,WANG Yao. Research and application of intelligent speed regulation of coalflow transportation system in coal mine[J]. Coal Science and Technology,2022,50(S1):283−288.
|
| [29] |
李世银,杨瑞鑫,杨磊,等. 煤矿井下智能超表面非视距无线覆盖技术综述[J]. 中国矿业大学学报,2024,53(3):613−622.
LI Shiyin,YANG Ruixin,YANG Lei,et al. Survey of the non-line-of sight wireless coverage technology by reconfigurable intelligent surfaces in underground coal mines[J]. Journal of China University of Mining & Technology,2024,53(3):613−622.
|
| [30] |
梁卫国,郭凤岐,于永军,等. 煤矸石井下原位智能分选充填技术研究进展[J]. 煤炭科学技术,2024,52(4):12−27.
LIANG Weiguo,GUO Fengqi,YU Yongjun,et al. Research progress on in-situ intelligent sorting and filling technology of coal gangue underground[J]. Coal Science and Technology,2024,52(4):12−27.
|
| [31] |
王家臣,潘卫东,张国英,等. 图像识别智能放煤技术原理与应用[J]. 煤炭学报,2022,47(1):87-101.
WANG Jiachen,PAN Weidong,ZHANG Guoying,et al. Principles and applications of image-based recognition of withdrawn coal and intelligent control of draw opening in longwall top coal caving face[J]. Journal of China Coal Society,2022,47(01):87-101.
|
| [32] |
毛明仓,张孝斌,张玉良. 基于透明地质大数据智能精准开采技术研究[J]. 煤炭科学技术,2021,49(1):286−293.
MAO Mingcang,ZHANG Xiaobin,ZHANG Yuliang. Research on intelligent and precision mining technology based on transparent geological big data[J]. Coal Science and Technology,2021,49(1):286−293.
|
| [33] |
程建远,王保利,范涛,等. 煤矿地质透明化典型应用场景及关键技术[J]. 煤炭科学技术,2022,50(7):1−12.
CHENG Jianyuan,WANG Baoli,FAN Tao,et al. Typical application scenes and key technologies of coal mine geological transparency[J]. Coal Science and Technology,2022,50(7):1−12.
|
| [34] |
李梅,毛善君,赵明军. 煤矿智能地质保障系统研究进展与展望[J]. 煤炭科学技术,2023,51(2):334−348.
LI Mei,MAO Shanjun,ZHAO Mingjun. Research progress and prospects of coal mine intelligent geological guarantee systems[J]. Coal Science and Technology,2023,51(2):334−348.
|
| [35] |
崔亚仲,任艳艳,白明亮. 神东矿区煤炭智能化建设实践[J]. 煤炭科学技术,2022,50(S1):218-226.
CUI Yazhong,REN Yanyan,BAI Ming liang Practice of ntelligent construction of Shendong Coal Mine[J]. Coal Science and Technology,2022,50(S1):218-226.
|
| [36] |
任怀伟,王国法,赵国瑞,等. 智慧煤矿信息逻辑模型及开采系统决策控制方法[J]. 煤炭学报,2019,44(9):2923−2935.
REN Huaiwei,WANG Guofa,ZHAO Guorui,et al. Smart coal mine logic model and decision control method of mining system[J]. Journal of China Coal Society,2019,44(9):2923−2935.
|
| [37] |
方新秋,梁敏富,李爽,等. 智能工作面多参量精准感知与安全决策关键技术[J]. 煤炭学报,2020,45(1):493−508.
FANG Xinqiu,LIANG Minfu,LI Shuang,et al. Key technologies of multi-parameter accurate perception and security decision in intelligent working face[J]. Journal of China Coal Society,2020,45(1):493−508.
|
| [38] |
刘永亮,李艳杰,崔耀. 超大采高工作面智能集成供液系统研究与应用[J]. 煤炭科学技术,2022,50(S2):387−392.
LIU Yongliang,LI Yanjie,CUI Yao. Research and application of intelligent integrated liquid supply system for super high mining face[J]. Coal Science and Technology,2022,50(S2):387−392.
|
| [39] |
尤秀松,葛世荣,郭一楠,等. 智采工作面三机数字孪生驱动控制架构[J]. 煤炭学报,2024,49(7):3265−3275.
YOU Xiusong,GE Shirong,GUO Yinan,et al. Digital twin-driven Control construction for Three machines ofSmart Coal Mining Face[J]. Journal of China Coal Society,2024,49(7):3265−3275.
|
| [40] |
马宏伟,赵英杰,薛旭升,等. 智能采煤机器人关键技术[J]. 煤炭学报,2024,49(2):1174−1182.
MA Hongwei,ZHAO Yingjie,XUE Xusheng,et al. Key technologies of intelligent mining robot[J]. Journal of China Coal Society,2024,49(2):1174−1182.
|
| [41] |
于斌,夏洪春,孟祥斌. 特厚煤层综放开采顶煤成拱机理及除拱对策[J]. 煤炭学报,2016,41(7):1617−1623.
YU Bin,XIA Hongchun,MENG Xiangbin. Top coal arching mechanism and arch removal strategies in fully mechanized top coal caving mining of ultra-thick coal seam[J]. Journal of China Coal Society,2016,41(7):1617−1623.
|
| [42] |
于斌,朱帝杰,陈忠辉. 基于随机介质理论的综放开采顶煤放出规律[J]. 煤炭学报,2017,42(6):1366−1371.
YU Bin,ZHU Dijie,CHEN Zhonghui. Top-coal drawing law of LTCC mining based on stochastic medium theory[J]. Journal of China Coal Society,2017,42(6):1366−1371.
|
| [43] |
左建平,吴根水,孙运江,等. 岩层移动内外“类双曲线”整体模型研究[J]. 煤炭学报,2021,46(2):333−343.
ZUO Jianping,WU Genshui,SUN Yunjiang,et al. Investigation on the inner and outer analogous hyperbola model (AHM)of strata movement[J]. Journal of China Coal Society,2021,46(2):333−343.
|
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