Volume 50 Issue 5
May  2024
Turn off MathJax
Article Contents
ZHENG Chuang, LI Danning, FENG Yinhui. Intelligent shearer cutting control based on process driven technology[J]. Journal of Mine Automation,2024,50(5):23-27, 150.  doi: 10.13272/j.issn.1671-251x.2023090017
Citation: ZHENG Chuang, LI Danning, FENG Yinhui. Intelligent shearer cutting control based on process driven technology[J]. Journal of Mine Automation,2024,50(5):23-27, 150.  doi: 10.13272/j.issn.1671-251x.2023090017

Intelligent shearer cutting control based on process driven technology

doi: 10.13272/j.issn.1671-251x.2023090017
  • Received Date: 2023-09-05
  • Rev Recd Date: 2024-06-05
  • Available Online: 2024-06-13
  • The traditional shearer cutting control lacks analysis of the state of the shearer drum, resulting in low quality of cutting template generation. It does not fully consider the undulation of the working face and geological environmental conditions, which makes it impossible to obtain the optimal cutting path. Relying on the control unit of the shearer itself cannot adjust the height of the drum in a timely manner. In order to solve the above problems, a process driven intelligent shearer cutting control scheme is proposed. According to the hydraulic support number of the working face, real-time collection of corresponding drum cutting height data is carried out. Combined with historical data of drum cutting height, real-time data is processed to generate a shearer cutting template that conforms to the trend of the working face roof and floor curve. Based on realistic data from the roof and floor of the working face and manual coal cutting experience, the method plans the cutting path of the shearer and performs real-time intervention to achieve adaptive coupling between the cutting height of the shearer drum and the curve of the roof and floor of the working face. By editing the coal mining process and setting the cutting template data, a coal mining process table file is formed. The cutting height of the shearer drum is adjusted accordingly to achieve adaptive height adjustment control of the shearer. The intelligent shearer cutting control scheme based on process driven technology is applied to the 43207 working face of Yujialiang Coal Mine in Shendong Coal Group. It achieves unmanned and normalized coal mining operations. The number of personnel in the production team working face is reduced from 3 to unmanned in the middle of the working face, and the automatic coal cutting rate of the shearer is over 97%.

     

  • loading
  • [1]
    王国法,张良,李首滨,等. 煤矿无人化智能开采系统理论与技术研发进展[J]. 煤炭学报,2023,48(1):34-53.

    WANG Guofa,ZHANG Liang,LI Shoubin,et al. Progress in theory and technological development of unmanned smart mining system[J]. Journal of China Coal Society,2023,48(1):34-53.
    [2]
    王国法,富佳兴,孟令宇. 煤矿智能化创新团队建设与关键技术研发进展[J]. 工矿自动化,2022,48(12):1-15.

    WANG Guofa,FU Jiaxing,MENG Lingyu. Development of innovation team construction and key technology research in coal mine intelligence[J]. Journal of Mine Automation,2022,48(12):1-15.
    [3]
    康红普,任世华,王保强,等. 煤炭工业数字化发展战略研究[J]. 中国工程科学,2023,25(6):170-178. doi: 10.15302/J-SSCAE-2023.06.021

    KANG Hongpu,REN Shihua,WANG Baoqiang,et al. Digital development strategy of coal industry[J]. Strategic Study of CAE,2023,25(6):170-178. doi: 10.15302/J-SSCAE-2023.06.021
    [4]
    葛世荣,郝雪弟,田凯,等. 采煤机自主导航截割原理及关键技术[J]. 煤炭学报,2021,46(3):774-788.

    GE Shirong,HAO Xuedi,TIAN Kai,et al. Principle and key technology of autonomous navigation cutting for deep coal seam[J] Journal of China Coal Society,2021,46(3):774-788.
    [5]
    李森,李重重,刘清. 基于透明地质的综采工作面规划截割协同控制系统[J]. 煤炭科学技术,2023,51(4):175-184.

    LI Sen,LI Zhongzhong,LIU Qing. Planned cutting and collaborative control system for fully-mechanized mining face based on transparent geology[J]. Coal Science and Technology,2023,51(4):175-184.
    [6]
    侯运炳,张弘,毛善君,等. 基于高精度三维动态地质模型的采煤机自适应智能截割技术研究[J]. 矿业科学学报,2023,8(1):26-38.

    HOU Yunbing,ZHANG Hong,MAO Shanjun,et al. Adaptive intelligent cutting technology of the shearer based on the high-precision three-dimensional dynamic geological model[J]. Journal of Mining Science and Technology,2023,8(1):26-38.
    [7]
    李旭,吴雪菲,田野,等. 基于数字煤层的综采工作面精准开采系统[J]. 工矿自动化,2021,47(11):16-21.

    LI Xu,WU Xuefei,TIAN Ye,et al. Digital coal seam-based precision mining system for fully mechanized working face[J]. Industry and Mine Automation,2021,47(11):16-21.
    [8]
    司垒,王忠宾,刘新华,等. 基于煤层分布预测的采煤机截割路径规划[J]. 中国矿业大学学报,2014,43(3):464-471.

    SI Lei,WANG Zhongbin,LIU Xinhua,et al. Cutting path planning of coal mining machine based on prediction of coal seam distribution[J]. Journal of China University of Mining & Technology,2014,43(3):464-471.
    [9]
    董刚,马宏伟,聂真. 基于虚拟煤岩界面的采煤机上滚筒路径规划[J]. 工矿自动化,2016,42(10):22-26.

    DONG Gang,MA Hongwei,NIE Zhen. Path planning of shearer up-drum based on virtual coal-rock interface[J]. Industry and Mine Automation,2016,42(10):22-26.
    [10]
    符大利. 透明工作面采煤机规划调高策略研究[J]. 煤矿安全,2023,54(4):226-231.

    FU Dali. Research on planning height adjustment strategy of shearer in transparent working face[J]. Safety in Coal Mines,2023,54(4):226-231.
    [11]
    钟立雯. 基于极限学习机的采煤机记忆截割调高控制算法研究[D]. 西安:西安科技大学,2015.

    ZHONG Liwen. Research towards adjustment control algorithm of shearer's memorial cutting on the basis of extreme learning machine[D]. Xi'an:Xi'an University of Science and Technology,2015.
    [12]
    王焕文,陶福贵,康俊霞. 基于单向示范刀的采煤机记忆截割模型构建及模拟分析[J]. 煤矿机械,2014,35(10):105-107.

    WANG Huanwen,TAO Fugui,KANG Junxia. Memory cutting model building and simulation analysis of shearer based on one-way demonstrate cutter[J]. Coal Mine Machinery,2014,35(10):105-107.
    [13]
    李旭东. 采煤机自动调高控制系统的研究[J]. 自动化应用,2020(6):8-10.

    LI Xudong. Research on the automatic height control system of the Shearer[J]. Automation on Application,2020(6):8-10.
    [14]
    刘送永,程诚,吴洪状,等. 基于煤岩界面识别的采煤机智能调高控制方法研究[J/OL]. 煤炭科学技术:1-14[2023-08-17]. https://doi.org/10.13199/j.cnki.cst.2022-0004.

    LIU Songyong,CHENG Cheng,WU Hongzhuang,et al. Study on intelligent height adjustment control method of shearer based on coal-rock interface recognition[J/OL]. Coal Science and Technology:1-14[2023-08-17]. https://doi.org/10.13199/j.cnki.cst.2022-0004.
    [15]
    许连丙. 基于Elman神经网络的采煤机智能调高控制算法研究[J]. 机电工程技术,2021,50(6):163-164,177. doi: 10.3969/j.issn.1009-9492.2021.06.044

    XU Lianbing. Research on intelligent height adjustment control algorithm of shearer based on Elman neural network[J]. Mechanical & Electrical Engineering Technology,2021,50(6):163-164,177. doi: 10.3969/j.issn.1009-9492.2021.06.044
    [16]
    卢春锋. 采煤机自适应截割及跟踪控制技术研究[J]. 能源与节能,2024(5):122-125.

    LU Chunfeng. Adaptive cutting and tracking control technology of shearers[J]. Energy and Energy Conservation,2024(5):122-125.
    [17]
    申伟,晋瑜. 基于记忆原理的采煤机自动割煤系统研究及应用[J]. 自动化应用,2019(6):19-20.

    SHEN Wei,JIN Yu. Research and application of automatic coal cutting system of shearer based on memory principle[J]. Automation Application,2019(6):19-20.
    [18]
    王云龙. 综采工作面采煤机记忆割煤技术的实现[J]. 煤炭与化工,2017,40(12):69-71.

    WANG Yunlong. Implement of memory coal slicing technology in fully mechanized working face[J]. Coal and Chemical Industry,2017,40(12):69-71.
    [19]
    刘春生,刘延婷,刘若涵,等. 采煤机截割状态与煤岩识别的关联载荷特征模型[J]. 煤炭学报,2022,47(1):527-540.

    LIU Chunsheng,LIU Yanting,LIU Ruohan,et al. Correlation load characteristic model between shearer cutting state and coal-rock recognition[J]. Journal of China Coal Society,2022,47(1):527-540.
    [20]
    黄曾华. 综采工作面自动化控制系统在王坡煤矿的应用[J]. 煤矿开采,2013,18(4):52-54. doi: 10.3969/j.issn.1006-6225.2013.04.019

    HUANG Zenghua. Application of automatic control system for full-mechanized mining face in Wangpo Colliery[J]. Coal Mining Technology,2013,18(4):52-54. doi: 10.3969/j.issn.1006-6225.2013.04.019
    [21]
    宋焘,董博,党恩辉. 基于智能感知的综采三机远程智能协同控制技术[J]. 煤矿机械,2022,43(6):172-175.

    SONG Tao,DONG Bo,DANG Enhui. Remote intelligent cooperative control technology of three machines in fully mechanized mining face based on intelligent perception[J]. Coal Mine Machinery,2022,43(6):172-175.
  • 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(4)

    Article Metrics

    Article views (82) PDF downloads(18) Cited by()
    Proportional views
    Related

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return
    Return