Advance Search

WANG Xu,YIN Shangxian,XU Bin,et al. Study on height optimization prediction model of overburden water-conducting fracture zone under fully mechanized mining[J]. Coal Science and Technology,2023,51(S1):284−297

. DOI: 10.13199/j.cnki.cst.2022-1530
Citation:

WANG Xu,YIN Shangxian,XU Bin,et al. Study on height optimization prediction model of overburden water-conducting fracture zone under fully mechanized mining[J]. Coal Science and Technology,2023,51(S1):284−297

. DOI: 10.13199/j.cnki.cst.2022-1530

Study on height optimization prediction model of overburden water-conducting fracture zone under fully mechanized mining

Funds: 

Basic Research Funds for Central Universities (3142021004,3142020002); Science and Technology Fund of Colleges and Universities of Hebei Province (QN2022208)

More Information
  • Received Date: September 20, 2022
  • Available Online: August 15, 2023
  • Accurately predicting the height of water-conducting fracture zones is an important issue in coal mine water control and a prerequisite for ensuring mine safety. Currently, there are problems with the accuracy of prediction models for water-conducting fracture zone height. Based on collecting a wide range of measured data on water-conducting fracture zone development height in various coal mining areas and summarizing previous research achievements, a prediction model for water-conducting fracture zone height was established, taking into account five factors: mining thickness, face elongation, dip angle, mining depth, and proportion coefficient of hard rock.Factor analysis was used to analyze the weights of influencing factors and determine the dominant factor. Multiple linear regression model, multiple nonlinear regression model, and backpropagation (BP) neural network prediction model were established. Accuracy tests were conducted on the prediction models to calculate the height of water-conducting fracture zones. Using measured data from the Xishan mining area as an example, the optimal model was selected, and the BP neural network model, as the optimal prediction model, was analyzed and compared with the “Sanxia” empirical formula, multiple linear regression model, and multiple nonlinear regression model in terms of the computed results The results showed that the “San xia” empirical formula had distortions compared to actual data and could no longer guide the prediction of water-conducting fracture zone height under comprehensive mining conditions. On the other hand, the BP neural network prediction model had an error of less than 10%, with stable absolute and relative errors. Additionally, this model reduced the correlation between factors and improved prediction accuracy. Therefore, the BP neural network prediction model demonstrates good accuracy and applicability in predicting water-conducting fracture zone height. The research findings can provide reference suggestions for guiding on-site water hazard prevention and control in mines.

  • [1]
    孙 润,孙 泽,田午子,等. 屯兰矿28120回采工作面导水裂隙带发育高度研究[J]. 华北科技学院学报,2019,16(2):20−24.

    SUN Run,SUN Ze,TIAN Wuzi,et al. Research on developmental height of water flowing fractured zone of 28120 working face in tunlan coal mine[J]. Journal of North China Institute of Science and Technology,2019,16(2):20−24.
    [2]
    邹 海,桂和荣,王桂梁,等. 综放开采导水裂隙带高度预测方法[J]. 煤田地质与勘探,1998(6):44−47.

    ZOU Hai,GUI Heron,WANG Guiliang,et al. Forcast about the helght of water fractured zone under sub level caving method[J]. coal geology & exploration,1998(6):44−47.
    [3]
    国家安全监管总局, 国家煤矿安监局, 国家能源局, 等. 建筑物、水体、铁路及主要井巷煤柱留设与压煤开采规程[M]. 北京: 煤炭工业出版社, 2017.

    State Administration of Work Safety, National Coal Mine Safety Administration, National Energy Administration, et al. Specifications for coal pillar retention and compressed coal mining in buildings water bodies railways and main shafts [M]. Beijing: China Coal Industry Publishing House, 2017.
    [4]
    尹尚先,徐 斌,徐 慧,等. 综采条件下煤层顶板导水裂缝带高度计算研究[J]. 煤炭科学技术,2013,41(9):138−142.

    YIN Shangxian,XU Bin,XU Hui,et al. Study on height calculation of water conducted fractured zone caused by fully mechanized mining[J]. Coal Science and Technology,2013,41(9):138−142.
    [5]
    许延春,李俊成,刘世奇,等. 综放开采覆岩“两带”高度的计算公式及适用性分析[J]. 煤矿开采,2011,16(2):4−7,11.

    XU Yangchun,LI Juncheng,LIU Shiqi et al. Calculation formula of “two-zone” height of overlying strata and its adaptability analysis[J]. Coal Mining Technology,2011,16(2):4−7,11.
    [6]
    胡小娟,李文平,曹丁涛,等. 综采导水裂隙带多因素影响指标研究与高度预计[J]. 煤炭学报,2012,37(4):613−620.

    HU Xiaojuan,LI Wenping,CAO Dingtao et al. Index of multiple factors and expected height of fully mechanized water flowing fractured zone[J]. Journal of China Coal Society,2012,37(4):613−620.
    [7]
    张宏伟,朱志洁,霍丙杰,等. 基于改进的FOA-SVM导水裂隙带高度预测研究[J]. 中国安全科学学报,2013,23(10):9−14.

    ZHANG Hongwei,ZHU Zhijie,HUO Bingjie et al. Water flowing fractured zone height prediction based on improved foa-sum[J]. China Safety Science Journal,2013,23(10):9−14.
    [8]
    施龙青,吴洪斌,李永雷,等. 导水裂隙带发育高度预测的PCA-GA-Elman优化模型[J]. 河南理工大学学报(自然科学版),2021,40(4):10−18.

    SHI Longqing,WU Hongbin,LI Yongle et al. Optimization model of PCA-GA-Elman for development height prediction of water-conducting fissure zone[J]. Journal of Henan Polytechnic University (Natural Science),2021,40(4):10−18.
    [9]
    赵忠明,刘永良,李 祎,等. 基于ANN导水裂隙带高度预测的过程优化[J]. 矿业安全与环保,2015,42(3):47−49, 53.

    ZHAO Zhongming,LIU Yongliang,LI Yi,et al. Process optimization of height prediction of water flowing fractured zone based on ann[J]. Mining Safety & Environmental Protection,2015,42(3):47−49, 53.
    [10]
    王秋宝. 基于改进ACO优化BP神经网络的软件缺陷预测模型[D]. 青岛: 中国石油大学(华东), 2017.

    WANG Qiubao. A software defect prediction model based on BP neural network using improved ACO optimization [D]. Qingdao: China University of Petroleum (East China), 2017.
    [11]
    邓伟康,刘 锋,朱二周. 基于新型PSO算法优化BP神经网络的软件缺陷预测方法研究[J]. 微电子学与计算机,2017,34(4):39−43, 48.

    DENG Weikang,LIU Feng,ZHU Erzhou. Software defect prediction model based on IVPSO-BP[J]. Microelectronics & Computer,2017,34(4):39−43, 48.
    [12]
    吴广竹, 徐智敏. 基于BP神经网络的导水裂隙带高度预测研究[J]. 能源技术与管理, 2008(1): 90-92.

    WU Guangzhu, XU Zhimin. Research on predicting the height of water-carrying fracture zone based on bp neural network. [J]. Energy Technology and Management, 2008(1): 90-92.
    [13]
    盛奉天,段玉清. 彬长矿区巨厚砂岩含水层下综放开采导水裂隙带高度研究[J]. 煤炭工程,2022,54(3):84−89.

    SHENG Fengtian,DUAN Yuqing. Height of water-conducting fracture zone in fully mechanized caving mining under extra-thick sandstone aquifer in Binchang mining area[J]. Coal Engineering,2022,54(3):84−89.
    [14]
    王君卿. 综采工作面导水裂隙带高度预测[J]. 煤矿现代化,2018(5):151−152,156.

    WANG Junqing. Study on height calculation of water conducted fractured zone caused by fully mechanized mining[J]. Coal Mine Modernization,2018(5):151−152,156.
    [15]
    徐树媛,张永波,孙灏东,等. 基于RBF核ε-SVR的导水裂隙带高度预测模型研究[J]. 安全与环境学报,2021,21(5):2022−2029.

    XU Shuyuan,ZHANG Yongbo,SUN Haodong,et al. Predictable testing and determination of the height of the fractured waterconducting zone based on the ε-SVR model via the RBF kernel function[J]. Journal of Safety and Environment,2021,21(5):2022−2029.
    [16]
    娄高中,谭 毅. 基于PSO-BP神经网络的导水裂隙带高度预测[J]. 煤田地质与勘探,2021,49(4):198−204. doi: 10.3969/j.issn.1001-1986.2021.04.024

    LOU Gaozhong,TAN Yi,et al. Prediction of the height of water flowing fractured zone based on PSO-BP neural network[J]. Coal Geology & Exploration,2021,49(4):198−204. doi: 10.3969/j.issn.1001-1986.2021.04.024
    [17]
    王捞捞. 保德矿综放开采导水裂隙与地表沉陷规律研究[D]. 徐州: 中国矿业大学, 2021.

    WANG Laolao. Study on water flowing fracture and surface subsidence law of fully-mechanized caving mining in Baode [D]. Xuzhou: China University of Mining and Technology, 2021.
    [18]
    李 博,吴 煌,李 腾. 基于加权的综采导水裂隙带高度多元非线性回归预测方法研究[J]. 采矿与安全工程学报,2022,39(3):536−545.

    LI Bo,WU Huang,LI Teng. Height prediction of water-conducting fractured zone under fully mechanized mining based on weighted multivariate nonlinear regression[J]. Journal of Mining & Safety Engineering,2022,39(3):536−545.
    [19]
    秦 晋,智荣腾. BP神经网络在软件质量评价中的应用研究[J]. 软件导刊,2016,15(9):1−3.

    QIN Jin,Zhi Rongteng. Research on application of BP neural network in software quality evaluation[J]. Ware Guide,2016,15(9):1−3.
    [20]
    曾一凡,武 强,赵苏启,等. 我国煤矿水害事故特征、致因与防治对策[J]. 煤炭科学技术,2023,51(7):1−14.

    ZENG Yifan,WU Qiang,ZHAO Suqi,et al. Characteristics, causes, and prevention measures of coal mine water hazard accidents in China[J]. Coal Science and Technology,2023,51(7):1−14.
    [21]
    曾一凡,刘晓秀,武强,等. 双碳背景下“煤-水-热”正效协同共采理论与技术构想[J]. 煤炭学报,2023,48(2):538−550.

    ZENG Yifan,LIU Xiaoxiu,WU Qiang,et al. Theory and technical conception of coal-water-thermal positive synergistic co-extraction under the dual carbon background[J]. Journal of China Coal Society,2023,48(2):538−550.
  • Related Articles

    [1]LI Chao, LU Yiqiang, CHEN Zhao, YI Haiyang, WANG Mingyao. Research status and prospect of rock breaking form in mechanized excavation of mining hard rock roadway[J]. COAL SCIENCE AND TECHNOLOGY, 2024, 52(S1): 259-268. DOI: 10.13199/j.cnki.cst.2022-0211
    [2]CUI Menghao, JI Huifu, HUI Yanbo, ZHANG Zhongwei, CUI Yuming, SONG Dan. Research on multi-boom coordinated drilling technology for hard rock tunneling[J]. COAL SCIENCE AND TECHNOLOGY, 2023, 51(9): 261-273. DOI: 10.12438/cst.2022-1203
    [3]LUO Yong, GONG Fengqiang. Research progress and prospect of laboratory test of rock spalling  in deep hard rock roadway[J]. COAL SCIENCE AND TECHNOLOGY, 2022, 50(6): 46-60.
    [4]LI Shugang, LIU Lidong, ZHAO Pengxiang, LIN Haifei, XU Peiyun, ZHUO Risheng. Analysis and application of fracture evolution law of overburden compacted area on fully-mechanized mining face under multiple factors[J]. COAL SCIENCE AND TECHNOLOGY, 2022, 50(1): 95-104.
    [5]LI Qi, QIN Yujin, GAO Zhongning. Research on height prediction of “two zones” of overburdcn based on BP neural network in Wuyang Mine[J]. COAL SCIENCE AND TECHNOLOGY, 2021, 49(8): 53-59.
    [6]OUYANG Zhenhua, PENG Rui, ZHANG Tongjun, TANG Zhongyi, ZHAO Qifeng, ZHU Jianming. Analysis on mechanical model and impact factors of unloading failure of rock surrounding in soft rock roadway[J]. COAL SCIENCE AND TECHNOLOGY, 2020, 48(2).
    [7]LI Gang, NIU Lei, LI Wenlong. Study on rapid excavation technology for hard rock roadway in coal mine[J]. COAL SCIENCE AND TECHNOLOGY, 2018, (11).
    [8]Guo Wanzhong. Analysis on mine pressure bump law of fully-mechanized coal mining face in thin seam with hard roof and hard coal[J]. COAL SCIENCE AND TECHNOLOGY, 2016, (10).
    [9]MA Qi-hua WANG Peng FAN Wen-chang, . Rapid Heading Technology of Mine Hard Rock Large Cross Section Roadway in Deep Mine[J]. COAL SCIENCE AND TECHNOLOGY, 2014, (4).
    [10]Prediction on Width of Fault Water Prevention Coal Pillar Based on Multiple Regression Analysis Method[J]. COAL SCIENCE AND TECHNOLOGY, 2013, (6).

Catalog

    Article views (104) PDF downloads (42) Cited by()
    Related

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return
    Return