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煤矿坑道钻进过程智能优化与控制技术

姚宁平 吴敏 陈略峰 陆承达 范海鹏 张幼振 李旺年 姚克

姚宁平,吴敏,陈略峰,等. 煤矿坑道钻进过程智能优化与控制技术[J]. 煤田地质与勘探,2023,51(9):1−9. doi: 10.12363/issn.1001-1986.23.06.0377
引用本文: 姚宁平,吴敏,陈略峰,等. 煤矿坑道钻进过程智能优化与控制技术[J]. 煤田地质与勘探,2023,51(9):1−9. doi: 10.12363/issn.1001-1986.23.06.0377
YAO Ningping,WU Min,CHEN Luefeng,et al. Intelligent optimization and control technology for drilling process of coal mine tunnels[J]. Coal Geology & Exploration,2023,51(9):1−9. doi: 10.12363/issn.1001-1986.23.06.0377
Citation: YAO Ningping,WU Min,CHEN Luefeng,et al. Intelligent optimization and control technology for drilling process of coal mine tunnels[J]. Coal Geology & Exploration,2023,51(9):1−9. doi: 10.12363/issn.1001-1986.23.06.0377

煤矿坑道钻进过程智能优化与控制技术

doi: 10.12363/issn.1001-1986.23.06.0377
基金项目: 国家自然科学基金面上项目(62273317);教育部高等学校学科创新引智计划项目(B17040)
详细信息
    第一作者:

    姚宁平,1970 年生,男,甘肃泾川人,博士,研究员,博士生导师,从事煤矿井下智能钻探技术与装备的研究工作.E-mail:yaoningping@cctegxian.com

    通信作者:

    吴敏,1963年生,男,广东化州人,博士,教授,从事过程控制、鲁棒控制和智能系统的研究. E-mail:wumin@cug.edu.cn

  • 中图分类号: P634

Intelligent optimization and control technology for drilling process of coal mine tunnels

  • 摘要: 煤矿坑道钻进施工环境恶劣、复杂的煤层结构和繁杂的操作工序造成钻进效率低、钻进成本高。开展煤矿坑道钻进过程优化与控制技术的研究势在必行。围绕煤矿坑道钻进控制过程关键技术,从含煤地层岩性智能识别、钻进参数智能优化和智能控制3方面展开。首先,为了准确判断含煤地层类型,建立基于BP-Adaboost的含煤地层岩性智能识别模型。然后,在不同含煤地层条件下,建立基于机械比能和钻速的智能优化模型,为司钻人员提供最优给进压力和转速参考值。进而,提出一种基于模糊PID的给进压力控制策略,实现给进压力的有效控制。最后,基于煤矿坑道钻机智能钻进系统在淮南矿区某煤矿井下进行了现场试验。试验结果表明:所提含煤地层岩性智能识别方法的识别准确率达到96.75%;智能优化方法显著提升现场钻速,消耗的机械比能降低,在提高钻进效率的同时降低了钻进成本;给进压力控制策略使给进压力稳定运行在最优值附近,减小给进系统超调的同时,提升系统的响应速度,使给进压力的动态响应更加平稳。煤矿坑道钻进过程智能优化与控制技术能够保障钻进过程安全高效运行,促进煤矿坑道钻进技术智能化发展。

     

  • 图  煤矿坑道钻进过程智能优化与控制方案

    Fig. 1  Intelligent optimization and control scheme for drilling process of coal mine tunnel

    图  典型含煤地层岩性智能识别方案

    Fig. 2  Intelligent lithology identification scheme of typical coal-bearing formation

    图  钻进参数智能优化方案

    Fig. 3  Intelligent optimization scheme of drilling parameters

    图  回转系统控制回路

    Fig. 4  Rotary system control circuit

    图  给进系统控制回路

    Fig. 5  Feed system control circuit

    图  基于模糊PID给进系统液压控制

    注:$\Delta k_{\rm{p}}$为比例系数调节量;$\Delta k_{\rm{i}}$为积分系数调节量;$\Delta k_{\rm{d}} $为微分系数调节量。

    Fig. 6  Hydraulic control of feed system based on fuzzy PID

    图  隶属度函数

    Fig. 7  Membership function

    图  系统数据流

    Fig. 8  System data flow

    图  岩性识别混淆矩阵

    Fig. 9  Lithology identification confusion matrix

    图  10  钻速优化结果

    Fig. 10  Optimization results of drilling rate

    图  11  机械比能优化结果

    Fig. 11  Optimization results of mechanical specific energy

    图  12  给进压力响应曲线

    Fig. 12  Feed pressure response curve

    图  13  现场施工与控制软件界面

    Fig. 13  Field construction and control software interface

    图  14  钻机回转速度对比曲线

    Fig. 14  Drilling rig rotation speed vs time

    表  1  分类模型性能评价

    Table  1  Classification model performance evaluation

    岩层类型 查准率 召回率 F-measure
    软煤层 96.86% 96.70% 96.78
    硬煤层 96.17% 96.02% 96.09
    岩层 97.22% 97.48% 97.35
    下载: 导出CSV

    表  2  控制性能对比

    Table  2  Control performance comparison

    控制算法调节时间/s超调量/%稳态误差/%
    模糊PID0.600.01
    PID2.08.30.003
    下载: 导出CSV
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出版历程
  • 收稿日期:  2023-06-26
  • 修回日期:  2023-08-10
  • 刊出日期:  2023-09-15
  • 网络出版日期:  2023-09-04

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