Volume 49 Issue 10
Oct.  2023
Turn off MathJax
Article Contents
SHI Xiangyu, SI Lei, WANG Zhongbin, et al. Forward simulation of electromagnetic waves in coal gangue model based on improved bidirectional peak-valley search algorithm[J]. Journal of Mine Automation,2023,49(10):87-95.  doi: 10.13272/j.issn.1671-251x.18090
Citation: SHI Xiangyu, SI Lei, WANG Zhongbin, et al. Forward simulation of electromagnetic waves in coal gangue model based on improved bidirectional peak-valley search algorithm[J]. Journal of Mine Automation,2023,49(10):87-95.  doi: 10.13272/j.issn.1671-251x.18090

Forward simulation of electromagnetic waves in coal gangue model based on improved bidirectional peak-valley search algorithm

doi: 10.13272/j.issn.1671-251x.18090
  • Received Date: 2023-03-20
  • Rev Recd Date: 2023-10-12
  • Available Online: 2023-10-24
  • Realizing automatic recognition of coal gangue content during the top coal caving process is an important goal of fully mechanized mining automation. The existing methods for automatic recognition of coal gangue content have problems such as low accuracy and real-time performance. The coal gangue mixture generated during the top coal caving process is a three-phase medium formed by coal, gangue, and air. The electrical parameters of each phase medium are different. The propagation features of electromagnetic waves are also different in different components of the mixed three-phase medium. There is a significant difference in the dielectric constant between coal blocks and gangue. By studying the electrical parameters of coal gangue mixtures with different gangue contents, new ideas and methods can be provided for automatic recognition of gangue content in top coal caving working faces. In order to explore the electrical differences of coal gangue mixtures with different gangue contents, a bidirectional peak-valley search algorithm based on the divide and conquer strategy is proposed. Based on this algorithm, a multiphase discrete random medium model of coal gangue is established. Based on the Maxwell equations and their constitutive relationship equations, the electromagnetic wave forward simulation of the established model is performed using the finite difference time domain method. The analysis shows that after improving the bidirectional peak-valley search algorithm based on the divide and conquer strategy, there is a clear phase interface between the coal, gangue, and air phases in the coal gangue multiphase discrete random medium model. Moreover, there is a greater degree of dispersion of each phase and no aggregation phenomenon. Therefore, the local medium can also reflect the overall electrical parameters, which can meet the requirements of the medium model for electromagnetic wave forward modeling. The forward simulation results indicate the following points. ① The frequency of the excitation signal will affect the amplitude of the transmitted wave. In the 12 GHz range, the higher the frequency of the excitation signal, the greater the amplitude of the transmitted wave. Low frequency will reduce the robustness of the signal, and the excitation frequency should be higher than 2 GHz. ② The gangue content of the coal gangue mixture is positively correlated with the overall equivalent dielectric constant of the medium. The higher the gangue content, the greater the propagation loss of the electromagnetic wave signal. The smaller the amplitude of the signal received by the receiving plane, the longer the time it takes for the electromagnetic wave signal to penetrate the medium. There is a significant difference between different gangue contents, which can be used as a basis for the gangue content recognition of fully mechanized top coal caving.

     

  • loading
  • [1]
    王家臣. 我国综放开采40年及展望[J]. 煤炭学报,2023,48(1):83-99.

    WANG Jiachen. 40 years development and prospect of longwall top coal caving in China[J]. Journal of China Coal Society,2023,48(1):83-99.
    [2]
    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.
    [3]
    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(1):87-101.
    [4]
    王家臣,李良晖,杨胜利. 不同照度下煤矸图像灰度及纹理特征提取的实验研究[J]. 煤炭学报,2018,43(11):3051-3061.

    WANG Jiachen,LI Lianghui,YANG Shengli. Experimental study on gray and texture features extraction of coal and gangue image under different illuminance[J]. Journal of China Coal Society,2018,43(11):3051-3061.
    [5]
    窦希杰,王世博,谢洋,等. 基于IMF能量矩和SVM的煤矸识别[J]. 振动与冲击,2020,39(24):39-45.

    DOU Xijie,WANG Shibo,XIE Yang,et al. Coal and gangue identification based on IMF energy moment and SVM[J]. Journal of Vibration and Shock,2020,39(24):39-45.
    [6]
    窦希杰,王世博,刘后广,等. 基于EMD特征提取与随机森林的煤矸识别方法[J]. 工矿自动化,2021,47(3):60-65.

    DOU Xijie,WANG Shibo,LIU Houguang,et al. Coal and gangue identification method based on EMD feature extraction and random forest[J]. Industry and Mine Automation,2021,47(3):60-65.
    [7]
    马瑞,王增才,王保平. 基于声波信号小波包变换的煤矸界面识别研究[J]. 煤矿机械,2010,31(5):44-46.

    MA Rui,WANG Zengcai,WANG Baoping. Coal-rock interface recognition based on wavelet packet transform of acoustic signal[J]. Coal Mine Machinery,2010,31(5):44-46.
    [8]
    宋庆军,肖兴明,张天顺,等. 基于声波的放顶煤过程自动控制系统[J]. 计算机工程与设计,2015,36(11):3123-3127.

    SONG Qingjun,XIAO Xingming,ZHANG Tianshun,et al. Automatic control systems in top-coal caving based on acoustic wave[J]. Computer Engineering and Design,2015,36(11):3123-3127.
    [9]
    李瑞,李博,王学文,等. 基于XGBoost与可见-近红外光谱的煤矸识别方法[J]. 光谱学与光谱分析,2022,42(9):2947-2955.

    LI Rui,LI Bo,WANG Xuewen,et al. A classification method of coal and gangue based on XGBoost and visible-near infrared spectroscopy[J]. Spectroscopy and Spectral Analysis,2022,42(9):2947-2955.
    [10]
    丁震,常博深. 面向煤矸识别的近红外反射光谱数据预处理方法[J]. 工矿自动化,2021,47(12):93-97.

    DING Zhen,CHANG Boshen. Near-infrared reflectance spectrum data preprocessing method for coal gangue identification[J]. Industry and Mine Automation,2021,47(12):93-97.
    [11]
    张宁波,鲁岩,刘长友,等. 综放开采煤矸自动识别基础研究[J]. 采矿与安全工程学报,2014,31(4):532-536.

    ZHANG Ningbo,LU Yan,LIU Changyou,et al. Basic study on automatic detection of coal and gangue in the fully mechanized top coal caving mining[J]. Journal of Mining & Safety Engineering,2014,31(4):532-536.
    [12]
    王增才. 综采放顶煤开采过程煤矸识别研究[J]. 煤矿机械,2002,23(8):13-14.

    WANG Zengcai. Study on distributing coal and rock in the process of fully-mechanized coal winning sublevel caving coal technology[J]. Coal Mining Machinery,2002,23(8):13-14.
    [13]
    殷学鑫,刘洋. 二维随机介质模型正演模拟及其波场分析[J]. 石油地球物理勘探,2011,46(6):862-872,1012,830.

    YIN Xuexin,LIU Yang. Random medium 2-D modeling and its wavefield analysis[J]. Oil Geophysical Prospecting,2011,46(6):862-872,1012,830.
    [14]
    董珍一,林莉,雷明凯,等. 基于BPNN的封严涂层孔隙分布均匀性超声表征[J]. 航空学报,2022,43(5):570-579.

    DONG Zhenyi,LIN Li,LEI Mingkai,et al. Ultrasonic quantitative characterization of pore distribution uniformity of seal coating based on BPNN[J]. Acta Aeronautica et Astronautica Sinica,2022,43(5):570-579.
    [15]
    雍凡,刘子龙,欧洋,等. 基于随机介质模型的统计分析在深反射地震中的应用[J]. 地球物理学进展,2021,36(3):993-1007.

    YONG Fan,LIU Zilong,OU Yang,et al. Application of statistical analysis based on random medium model in deep seismic reflection data[J]. Progress in Geophysics,2021,36(3):993-1007.
    [16]
    LIN Li,ZHANG Wei,MA Zhiyuan,et al. Random multi-phase medium model and its application in analysis of ultrasonic propagation characteristics for AlSi-polyester abradable seal coating[J]. NDT & E International,2019,108(12). DOI: 10.1016/j.ndteint.2019.102173.
    [17]
    王鑫泉. 基于介电特征及几何约束的煤矸识别技术研究[D]. 淮南:安徽理工大学,2022.

    WANG Xinquan. Research on coal gangue identification technology based on dielectric characteristics and geometric constraints[D]. Huainan:Anhui University of Science and Technology,2022.
    [18]
    王湘云,郭华东,王超,等. 干燥岩石的相对介电常数研究[J]. 科学通报,1999,44(13):1384-1391. doi: 10.1360/csb1999-44-13-1384

    WANG Xiangyun,GUO Huadong,WANG Chao,et al. Study on the relative dielectric constant of dry rocks[J]. Chinese Science Bulletin,1999,44(13):1384-1391. doi: 10.1360/csb1999-44-13-1384
    [19]
    乔鸣忠,张晓锋,单志超. 大功率永磁电机中永磁体布置的研究[J]. 中小型电机,2003,30(5):21-25.

    QIAO Mingzhong,ZHANG Xiaofeng,SHAN Zhichao. Research on array of permanent magnet in large PMSM[J]. S & M Electric Machines,2003,30(5):21-25.
    [20]
    ZHAO Anping. Analysis of the numerical dispersion of the 2D alternating-direction implicit FDTD method[J]. IEEE Transactions on Microwave Theory and Techniques,2002,50(4):1156-1164. doi: 10.1109/22.993419
    [21]
    JUNG K Y,TEIXEIRA F L. Multispecies ADI-FDTD algorithm for nanoscale three-dimensional photonic metallic structures[J]. IEEE Photonics Technology Letters,2007,19(8):586-588. doi: 10.1109/LPT.2007.894282
  • 加载中

Catalog

    通讯作者: 陈斌, bchen63@163.com
    • 1. 

      沈阳化工大学材料科学与工程学院 沈阳 110142

    1. 本站搜索
    2. 百度学术搜索
    3. 万方数据库搜索
    4. CNKI搜索

    Figures(9)

    Article Metrics

    Article views (194) PDF downloads(18) Cited by()
    Proportional views
    Related

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return
    Return