Citation: | ZHANG Fan,LI Yuxue,LI Yuhan,et al. SSA–RF: A novel prediction method for two-column supports in coal mines based on digital twins[J]. Coal Science and Technology,2025,53(1):312−325. DOI: 10.12438/cst.2024-0131 |
Existing methods for predicting hydraulic support loads in mine roadways usually assume a static spatio-temporal mining arrangement, which ignores the dynamic loads of the far-field surrounding rocks and affects the accuracy of rockburst prediction. In order to ensure safe mining, real-time and accurate predictive assessment of potential rockburst is necessary. In this paper, a Sparrow Search Algorithm-Random Forest (SSA–RF) prediction method based on digital twin and machine learning is proposed. By analyzing the interaction between the support system and the surrounding rock, a digital twin model of the two-column support is established, and the interaction mapping and synchronous feedback between the physical entity and its digital twin are realized based on data driving. By comparing and analyzing the calculated and real values of the attitude variables during the column lifting process of the two-column support, it is found that compared with the physical entity of the support, the digital twin model has an average error of 0.14° in angle and 6.15 mm in length, which is in line with the accuracy requirements. In addition, the Sparrow Search Algorithm was used to optimize the number of decision trees and node features in the Random Forest. Compared with using a single prediction model, the SSA–RF prediction modeling improves the convergence speed and optimization ability. The experimental results show that the SSA–RF method proposed in this paper performs optimally compared with prediction algorithms such as Long Short-Term Memory (LSTM), Random Forest (RF) and Support Vector Machine (SVM), and its prediction accuracy reaches 85.89% and 91.09% on the central support and end support data sets, respectively. In addition, it is found that the roof in the area of the central support is prone to fracture instability, which will destroy the vertical stress support conditions in the central area of the working face, thus leading to a larger range of load variations in the central support with a slightly lower prediction accuracy than that of the end support. The above results provide some theoretical reference for further research on the occurrence mechanism of rockburst in coal mine and accurate prediction of potential rockburst.
[1] |
中国煤炭工业协会,中国煤炭报社. 2023年煤炭科技十大新闻[J]. 中国煤炭工业,2024(1):40−41.
China National Coal Association,China Coal News. 2023 top 10 news on coal technology[J]. China Coal Industry,2024(1):40−41.
|
[2] |
WEN J L,LI H S,JIANG F X,et al. Rock burst risk evaluation based on equivalent surrounding rock strength[J]. International Journal of Mining Science and Technology,2019,29(4):571−576. doi: 10.1016/j.ijmst.2019.06.005
|
[3] |
潘一山,高学鹏,王伟,等. 冲击地压矿井综采工作面两巷超前支护液压支架研究[J]. 煤炭科学技术,2021,49(6):1−12.
PAN Yishan,GAO Xuepeng,WANG Wei,et al. Research of hydraulic powered supports for entries' advanced support in fully-mechanized working face of rock burst mine[J]. Coal Science and Technology,2021,49(6):1−12.
|
[4] |
WU X Y,JIANG L S,XU X G,et al. Numerical analysis of deformation and failure characteristics of deep roadway surrounding rock under static-dynamic coupling stress[J]. Journal of Central South University,2021,28(2):543−555. doi: 10.1007/s11771-021-4620-2
|
[5] |
徐连满,马柳,姜笑楠,等. 冲击地压载荷下O型棚支架动力响应规律研究[J]. 煤炭科学技术,2022,50(4):49−57.
XU Lianman,MA Liu,JIANG Xiaonan,et al. Study on dynamic response law of O-shaped support under rockburst loading[J]. Coal Science and Technology,2022,50(4):49−57.
|
[6] |
潘俊锋. 煤矿冲击地压启动理论及其成套技术体系研究[J]. 煤炭学报,2019,44(1):173−182.
PAN Junfeng. Theory of rockburst start-up and its complete technology system[J]. Journal of China Coal Society,2019,44(1):173−182.
|
[7] |
姜志刚. 综采工作面液压支架载荷预测算法研究[J]. 煤矿机械,2023,44(8):32−35.
JIANG Zhigang. Research on load prediction algorithm of hydraulic support in fully mechanized mining face[J]. Coal Mine Machinery,2023,44(8):32−35.
|
[8] |
曾庆良,李兆基,万丽荣,等. 不同加载条件下液压支架承载及铰接点载荷研究[J]. 煤炭工程,2022,54(8):168−173.
ZENG Qingliang,LI Zhaoji,WAN Lirong,et al. Load on bearing and hinge point of hydraulic support under different loading conditions[J]. Coal Engineering,2022,54(8):168−173.
|
[9] |
张云朝,吴士良,王建行. 大采高综放工作面支架适应性评价[J]. 煤炭工程,2016,48(6):12−14,18.
ZHANG Yunzhao,WU Shiliang,WANG Jianhang. Assessment of adaptability of support for high cutting fully mechanized top coal caving face[J]. Coal Engineering,2016,48(6):12−14,18.
|
[10] |
LI T D,WANG J R,ZHANG K,et al. Mechanical analysis of the structure of longwall mining hydraulic support[J]. Science Progress,2020,103(3):36850420936479. doi: 10.1177/0036850420936479
|
[11] |
尹希文,朱拴成,安泽,等. 浅埋深综放工作面矿压规律及支架工作阻力确定[J]. 煤炭科学技术,2013,41(5):50−54.
YIN Xiwen,ZHU Shuancheng,AN Ze,et al. Mine strata pressure law of fully mechanized top coal caving mining face in shallow depth and determination of working resistance for powered support[J]. Coal Science and Technology,2013,41(5):50−54.
|
[12] |
王国法,庞义辉. 液压支架与围岩耦合关系及应用[J]. 煤炭学报,2015,40(1):30−34.
WANG Guofa,PANG Yihui. Relationship between hydraulic support and surrounding rock coupling and its application[J]. Journal of China Coal Society,2015,40(1):30−34.
|
[13] |
王国法,庞义辉. 基于支架与围岩耦合关系的支架适应性评价方法[J]. 煤炭学报,2016,41(6):1348−1353.
WANG Guofa,PANG Yihui. Shield-roof adaptability evaluation method based on coupling of parameters between shield and roof strata[J]. Journal of China Coal Society,2016,41(6):1348−1353.
|
[14] |
LI Y,SHI X H. Mine pressure prediction study based on fuzzy cognitive maps[J]. International Journal of Computational Intelligence and Applications,2020,19(3):2050023. doi: 10.1142/S1469026820500236
|
[15] |
贾思锋,付翔,王然风,等. 液压支架时空区域支护质量动态评价[J]. 工矿自动化,2022,48(10):26−33,81.
JIA Sifeng,FU Xiang,WANG Ranfeng,et al. Dynamic evaluation of support quality of hydraulic support in space-time region[J]. Journal of Mine Automation,2022,48(10):26−33,81.
|
[16] |
CAI W,DOU L M,ZHANG M,et al. A fuzzy comprehensive evaluation methodology for rock burst forecasting using microseismic monitoring[J]. Tunnelling and Underground Space Technology,2018,80:232−245. doi: 10.1016/j.tust.2018.06.029
|
[17] |
葛世荣,王世博,管增伦,等. 数字孪生−应对智能化综采工作面技术挑战[J]. 工矿自动化,2022,48(7):1−12.
GE Shirong,WANG Shibo,GUAN Zenglun,et al. Digital twin:Meeting the technical challenges of intelligent fully mechanized working face[J]. Journal of Mine Automation,2022,48(7):1−12.
|
[18] |
TAO F,XIAO B,QI Q L,et al. Digital twin modeling[J]. Journal of Manufacturing Systems,2022,64:372−389. doi: 10.1016/j.jmsy.2022.06.015
|
[19] |
张帆,葛世荣. 矿山数字孪生构建方法与演化机理[J]. 煤炭学报,2023,48(1):510−522.
ZHANG Fan,GE Shirong. Construction method and evolution mechanism of mine digital twins[J]. Journal of China Coal Society,2023,48(1):510−522.
|
[20] |
马宏伟,王鹏,张旭辉,等. 煤矿巷道智能掘进机器人系统关键技术研究[J]. 西安科技大学学报,2020,40(5):751−759.
MA Hongwei,WANG Peng,ZHANG Xuhui,et al. Research on key technology of intelligent tunneling robotic system in coal mine[J]. Journal of Xi’an University of Science and Technology,2020,40(5):751−759.
|
[21] |
张旭辉,吕欣媛,王甜,等. 数字孪生驱动的掘进机器人决策控制系统研究[J]. 煤炭科学技术,2022,50(7):36−49.
ZHANG Xuhui,LYU Xinyuan,WANG Tian,et al. Research on decision control system of tunneling robot driven by digital twin[J]. Coal Science and Technology,2022,50(7):36−49.
|
[22] |
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 control driven by AR/VR for fully mechanized coal-mining face[J]. Journal of China Coal Society,2022,47(2):969−985.
|
[23] |
丁恩杰,金雷,陈迪. 互联网+感知矿山安全监控系统研究[J]. 煤炭科学技术,2017,45(1):129−134.
DING Enjie,JIN Lei,CHEN Di. Study on safety monitoring and control system of Internet+” perception mine[J]. Coal Science and Technology,2017,45(1):129−134.
|
[24] |
袁亮,俞啸,丁恩杰,等. 矿山物联网人-机-环状态感知关键技术研究[J]. 通信学报,2020,41(2):1−12.
YUAN Liang,YU Xiao,DING Enjie,et al. Research on key technologies of human-machine-environment states perception in mine Internet of Things[J]. Journal on Communications,2020,41(2):1−12.
|
[25] |
丁恩杰,俞啸,夏冰,等. 矿山信息化发展及以数字孪生为核心的智慧矿山关键技术[J]. 煤炭学报,2022,47(1):564−578.
DING Enjie,YU Xiao,XIA Bing,et al. Development of mine informatization and key technologies of intelligent mines[J]. Journal of China Coal Society,2022,47(1):564−578.
|
[26] |
CHEN L,HU X M,WANG G,et al. Parallel mining operating systems:From digital twins to mining intelligence[C]//2021 IEEE 1st International Conference on Digital Twins and Parallel Intelligence (DTPI). Piscataway,NJ:IEEE,2021:469-473.
|
[27] |
ZHAO W T,ZHANG C,FAN B,et al. Research on rolling bearing virtual-real fusion life prediction with digital twin[J]. Mechanical Systems and Signal Processing,2023,198:110434. doi: 10.1016/j.ymssp.2023.110434
|
[28] |
ZHANG H X,FENG Q Q,WU M D,et al. HIDT:A Digital Twin modeling approach through hierarchical integration for industrial Internet[J]. Computers & Industrial Engineering,2023,181:109306.
|
[29] |
HOCHREITER S,SCHMIDHUBER J. Long short-term memory[J]. Neural Computation,1997,9(8):1735−1780. doi: 10.1162/neco.1997.9.8.1735
|
[30] |
KUMAR I,TRIPATHI B K,SINGH A. Attention-based LSTM network-assisted time series forecasting models for petroleum production[J]. Engineering Applications of Artificial Intelligence,2023,123:106440. doi: 10.1016/j.engappai.2023.106440
|
[31] |
DONG L J,LI X B,PENG K. Prediction of rockburst classification using random forest[J]. Transactions of Nonferrous Metals Society of China,2013,23(2):472−477. doi: 10.1016/S1003-6326(13)62487-5
|
[32] |
CORTES C,VAPNIK V. Support-vector networks[J]. Machine Learning,1995,20(3):273−297.
|
[33] |
WU R,HUANG H S,WEI J N,et al. An improved sparrow search algorithm based on quantum computations and multi-strategy enhancement[J]. Expert Systems with Applications,2023,215:119421. doi: 10.1016/j.eswa.2022.119421
|
[34] |
国家质量监督检验检疫总局,国家标准化管理委员会. 煤矿用液压支架 第1部分:通用技术条件:GB 25974.1—2010[S]. 北京:中国标准出版社,2011.
|
[35] |
MA Z S,YU S H,HAN Y,et al. Zeroing neural network for bound-constrained time-varying nonlinear equation solving and its application to mobile robot manipulators[J]. Neural Computing and Applications,2021,33(21):14231−14245. doi: 10.1007/s00521-021-06068-6
|
[36] |
刘斌慧,杨军,田锋,等. 巷旁切顶对采场下位岩层垮落特征的影响机制[J]. 岩土力学,2023,44(1):289−302.
LIU Binhui,YANG Jun,TIAN Feng,et al. Influence mechanism of roadside roof cutting on collapse characteristics of lower roof in a stope[J]. Rock and Soil Mechanics,2023,44(1):289−302.
|
[37] |
汪伟,崔欣超,祁云,等. 基于SSA-RF的采空区煤自燃温度回归分析模型[J]. 中国安全科学学报,2023,33(9):136−141.
WANG Wei,CUI Xinchao,QI Yun,et al. Regression analysis model of coal spontaneous combustion temperature in goaf based on SSA-RF[J]. China Safety Science Journal,2023,33(9):136−141.
|