煤矿井下空气质量革命技术现状与展望

袁亮, 薛生, 郑晓亮, 江丙友

袁亮,薛生,郑晓亮,等. 煤矿井下空气质量革命技术现状与展望[J]. 工矿自动化,2023,49(6):32-40. DOI: 10.13272/j.issn.1671-251x.18130
引用本文: 袁亮,薛生,郑晓亮,等. 煤矿井下空气质量革命技术现状与展望[J]. 工矿自动化,2023,49(6):32-40. DOI: 10.13272/j.issn.1671-251x.18130
YUAN Liang, XUE Sheng, ZHENG Xiaoliang, et al. Current situation and prospects of air quality revolution technology in coal mines[J]. Journal of Mine Automation,2023,49(6):32-40. DOI: 10.13272/j.issn.1671-251x.18130
Citation: YUAN Liang, XUE Sheng, ZHENG Xiaoliang, et al. Current situation and prospects of air quality revolution technology in coal mines[J]. Journal of Mine Automation,2023,49(6):32-40. DOI: 10.13272/j.issn.1671-251x.18130

煤矿井下空气质量革命技术现状与展望

基金项目: 国家自然科学基金项目(52074012,52204191);安徽省高校杰出青年基金项目(2022AHO20057);安徽省高校协同创新项目(GXXT-2020-059);陕西省创新能力支撑计划项目(2021TD-28);陕煤集团神木红柳林矿业有限公司委托课题(SMHLL-JS-YJ-202006)。
详细信息
    作者简介:

    袁亮(1960—),男,安徽金寨人,中国工程院院士,教授,博士研究生导师,主要研究方向为煤炭安全精准开采、煤矿瓦斯治理与职业安全健康,E-mail:yuanl_1960@sina.com

  • 中图分类号: TD714.3

Current situation and prospects of air quality revolution technology in coal mines

  • 摘要: 我国煤矿安全高效开采技术处于世界领先水平,但以粉尘为主的毒害物质、高温、高湿、噪声等多种职业危害因素诱发的职业疾病长期困扰煤矿从业人员,目前职业病危害已超过安全事故对职工的伤害,严重制约行业未来发展。提出了井下空气质量革命理念,全面总结了井下控降尘技术(包括煤层注水减尘技术、喷雾降尘技术、通风排尘技术、化学试剂抑尘技术、井下空气质量革命控降尘体系)、燃油车辆尾气治理、井下空气质量监测预警系统的现状。为进一步推进井下空气质量革命理论与技术突破,立足于“分源−分区−分级−分策”的粉尘高效治理理念,寻求建立多源多相多场粉尘协同降尘与综合治理的理论体系;指明了采掘区域粉尘高效智能防控技术及装备、矿井粉尘环境多参量同步智能监测技术及装备等关键技术装备的发展方向;指出必须充分融合理工医管学科建立煤矿粉尘防控多主体协同体系,为井下空气质量革命由目前的起步阶段向中高级阶段发展奠定理论与技术基础,分步实现煤矿从业人员生命全周期职业健康的目标,最终做到煤矿职业病少发病或零发病,助力健康中国战略。
    Abstract: China's coal mine safe and efficient mining technology is at the leading level in the world. But the occupational diseases induced by dust-based toxic substances, high temperature, high humidity, noise and other occupational hazard have long plagued coal mine employees. At present, occupational disease hazards have exceeded the harm of safety accidents to employees, seriously restricting the industry's future development. The concept of underground air quality revolution is put forward. The status of underground dust control and reduction technology (including coal seam water infusion dust reduction technology, spray dust reduction technology, ventilation dust removal technology, chemical reagent dust suppression technology, underground air quality revolution dust control and reduction system), fuel vehicle emission treatment, underground air quality monitoring and early warning system is comprehensively summarized. In order to further promote the theoretical and technological breakthroughs of the underground air quality revolution, based on the concept of "source-division-grading-strategy" for efficient dust control, we seek to establish a theoretical system for collaborative dust reduction and comprehensive management of multi-source, multiphase, and multi-field dust. The development direction of key technical equipment such as efficient intelligent dust prevention and control technology and equipment in mining areas, and synchronous intelligent monitoring technology and equipment for multiple parameters of mine dust environment has been pointed out. It is pointed out that it is necessary to fully integrate the disciplines of science, engineering, medical management, and establish a multi-agent collaborative system for coal mine dust prevention and control. It will lay a theoretical and technical foundation for the development of the underground air quality revolution from the current initial stage to the intermediate and advanced stages. Step by step, it will achieve the goal of full life cycle occupational health for coal mine employees. Ultimately, it will achieve the goal of reducing or zero incidences of occupational diseases in coal mines, supporting the Healthy China Initiative.
  • 带式输送机是煤炭运输体系中的关键设备[1-3],对带式输送机输送的物料流量进行实时监测不仅对生产自动化控制具有重要意义,而且在产量统计、生产成本控制及能源节约等方面也发挥着积极作用[4-6]。当前,煤矿带式输送机上物料流量的测定主要依赖于电子胶带秤与核子胶带秤[7-8]。电子胶带秤主要基于压力传感器,通过测量输送带压力计算物料质量[9]。该方法属于接触式测量,存在机械结构复杂、测量精度较低、故障率较高,维护工作繁琐等缺陷[10-11]。核子胶带秤为非接触式测量,克服了电子胶带秤的部分缺陷,但仍存在测量精度有限、放射源安全管理要求严格、标定过程复杂等问题[12]。此外,射线的使用对环境和人体健康可能带来潜在风险,进一步限制了其应用范围[13-14]

    随着图像处理和智能识别技术的发展,基于机器视觉的带式输送机异常监测和物料流量测量方法受到关注[15-17]。该方法用激光标尺在物料表面投射一条轮廓线,用相机采集包含轮廓线的输送带表面图像,运用图像处理方法估算流量,但测量精度易受水雾、烟雾、粉尘和干扰光源的干扰,且受维护周期等因素影响,多用于辅助流量测量[18-19]。文献[20]提出利用激光雷达测量运输物料流量,根据扇形−三角形面积公式,直接采用激光雷达输出的极坐标数据计算运输物料的截面积,存在受异常点云数据影响和难以准确描述物料表面状态的问题,误差较大。针对该问题,本文提出了一种基于Akima插值的带式输送机物料流量激光检测方法。采用Akima插值法获取带式输送机上物料的截面积,结合输送带运行速度和激光雷达扫描频率,计算单个扫描周期内的物料体积。通过对任意时间段的测量数据进行积分,获得该时间段内的物料总体积。

    本文使用的激光雷达采用飞行时间(Time of Flight,TOF)测量原理,能够对周围360°环境进行二维扫描探测,通过计算调制激光的发射、返回时间差测量物体与传感器的相对距离,如图1所示。

    激光雷达测距公式为

    $$ D = \frac{1}{2}C({t_2} - {t_1}) $$ (1)

    式中:$ D $为激光雷达与物体间的距离;$ C $为光速;$ {t}_{2} $为雷达接收到返回激光信号的时刻;$ {t}_{1} $为激光射出时内部定时器开始计时时刻。

    图  1  TOF测量原理
    Figure  1.  TOF measurement principle

    经激光雷达内嵌的信号处理单元实时解算,得到探测物体的距离,结合高精度自适应角度测量模块输出的角度信息,得到周围360°环境的二维平面信息[21]

    将激光雷达安装在带式输送机的上方,使其扫描平面垂直于输送带的对称轴线,并与输送带底部切面正交,如图2所示。

    图  2  激光雷达安装位置
    Figure  2.  Installation position of LiDAR

    带式输送机在输送物料的过程中始终处于连续运动状态,因此从输送带的俯视视角看,输送带沿输送方向运动的同时,激光雷达按照设定的扫描频率从左向右进行扫描,如图3所示,v为带式输送机速度。激光雷达所获取的输送带表面点云数据并非严格沿输送带垂直方向分布,而是呈现出一定的倾斜特征,即点云扫描轨迹表现为一条斜线,而非理想情况下的直线。

    图  3  激光雷达获取的输送带点云轨迹
    Figure  3.  Point cloud trajectory of the conveyor belt obtained by LiDAR

    在每个扫描周期内,激光雷达获取的点云数据由大量离散测量点组成。由于相邻点云的采集时间间隔极短,相应的空间距离亦较小,所以相邻点云之间物料截面轮廓在水平面上的变化幅度较小。基于此特性,可假设相邻点云之间的物料截面轮廓基本保持不变,并利用该截面轮廓乘以激光雷达扫描周期内输送带的运动距离,计算该区间内输送物料的体积。进一步地,将同一扫描周期内所有点云按照相同方法进行处理,即可准确描述该扫描周期内输送带上物料的总体积,如图4所示。

    图  4  每个扫描周期内物料体积计算
    Figure  4.  Calculation of material volume within each scanning cycle

    图4中输送带向下运动,激光雷达从左向右扫描,当激光雷达完成1次完整扫描时,输送带移动距离为d,蓝色平行四边形代表由相邻点云数据计算得到的部分物料,该部分的面积与红色矩形的面积相等。因此通过累加激光雷达单次扫描过程中所有红色矩形的体积,可以得到输送带在距离d内输送的物料体积。随着输送带的持续运动与激光雷达的连续扫描,可以获得输送带上物料的总体积,如图5所示。

    图  5  带式输送机输送物料总体积计算
    Figure  5.  Calculation of total volume of materials conveyed by belt conveyor

    不同激光雷达输出的数据形式各异,需要对数据进行预处理以满足计算需求。激光雷达输出的数据为360°的点云。因此,需要对点云进行直通滤波,通过限制扫描角度对部分点云进行截取,仅保留所需数据。

    在实际点云数据获取过程中,由于异物遮挡、激光雷达未接收到回波、物料表面条件复杂等因素,激光雷达扫描物料表面输出的点云往往会出现异常点。为此,需要对点云进行离群点去噪。具体方法是计算相邻点之间的欧几里得距离,并结合基于中位数绝对偏差(Median Absolute Deviation,MAD)的修改Z分数方法来检测异常点。先计算相邻点的距离序列并求出中位数,再计算这些距离与中位数差的绝对值,通过MAD估计偏差,计算每个点的修改Z分数。如果连续2个点的修改Z分数均超过设定的阈值,则将第2个点标记为异常点。该方法主要利用统计学中的鲁棒指标(如中位数和MAD)识别偏离正常分布的异常值。

    其他插值算法使用过程中,在多项式阶次较高时常出现龙格现象,其最直观的表现为导数变化剧烈。为避免该问题,本文采用效果较好的Akima插值法[22]

    基于Akima插值的运输物料截面积计算法需要使用直角坐标形式的点云数据,而激光雷达输出的点云坐标是在以激光雷达为原点ox轴为极轴的二维极坐标系中表示的,如图6所示。因此,本文利用坐标变换公式将每个点云的极坐标变为直角坐标形式。

    图  6  带式输送机空载时的激光扫描截面
    Figure  6.  Laser scanning cross-section of belt conveyor under no-load condition

    图6中,$ \alpha $为激光扫描起始线与x轴的夹角,则激光所扫描的角度区间为$ {180}^{\circ }-2\alpha $;$\alpha_i $为第i条与第i+1条激光扫描线之间的夹角;$l_i$为激光雷达输出的第i个距离信息;n为1个扫描周期内输出的角度、距离信息总数。激光扫描截面任意点的二维坐标公式为

    $$ \left\{ {\begin{array}{*{20}{l}} {{y_i} = {l_i}\cos ({{90}^{\circ }} - \alpha - {\alpha _i})}&{} \\ {{x_i} = {l_i}\sin ({{90}^{\circ }} - \alpha - {\alpha _i})}&{} \end{array}} \right. $$ (2)

    插值后可得到带式输送机空载和负载时的点云曲线$ {f}_{1}\left(x\right) $和$ {f}_{2}\left(x\right) $,如图7所示。将这2条曲线分别对$ x $轴积分并将积分绝对值相减,可得到空载和负载时的截面积:

    $$ {S_1} = \int_a^b {{f_1}} (x){\mathrm{d}}x $$ (3)
    $$ {S_2} = \int_a^b {{f_2}} (x){\mathrm{d}}x $$ (4)

    式中ab分别为激光雷达扫描输送带第1个点和最后一个点的横坐标。

    图  7  插值前后输送带点云分布
    Figure  7.  Point cloud distribution of the conveyor belt before and after interpolation

    物料截面积为

    $$ {{S}} = \left| {{{{S}}_1}} \right| - \left| {{{{S}}_2}} \right| $$ (5)

    带式输送机运输物料截面如图8所示,瞬时流量为激光雷达1个扫描周期内通过激光雷达扫描截面的物料体积,计算公式为

    $$ {V_k} = \nu \frac{1}{f}{s_k} $$ (6)

    式中:$ {V}_{k} $为激光雷达第k个激光扫描周期内输送物料的体积;$ f $为激光雷达扫描频率;$ {s}_{k} $为第k个周期物料的截面积。

    图  8  带式输送机运输物料截面
    Figure  8.  Cross-section of belt conveyor transporting materials

    对一定时间段内式(6)计算的所有数据求和,可得到输送物料的总体积:

    $$ V = \sum\limits_{k = 1}^n \nu \frac{1}{f}{s_k} $$ (7)

    仿真平台基于PyCharm搭建,模拟一条宽1 m、长10 m的带式输送机。该输送机上物料最大高度设定为0.5 m,激光雷达安装在输送机上方1 m处,使雷达扫描面垂直于带式输送机对称轴线和底部切面,创建一条开口向上的抛物线,并在其基础上添加了随机扰动,以模拟表面不规则的物料形状,物料截面如图9所示。由系统生成的物料截面积为0.429 339 m2,在物料表面随机取99个点表示激光雷达扫描物料表面输出的点云信息。

    图  9  带式输送机输送物料仿真截面
    Figure  9.  Simulated cross-section of materials on belt conveyor

    实际获得的点云数据往往包含某些噪声,如图10所示。如果不对这些异常点进行处理,将导致较大的计算误差。由于物料表面情况复杂,某些物料块中的缝隙过大,导致点云数据落在物料内部,如图10(a)所示,通过扇形−三角形计算法得到此时物料截面积为0.414 144 m2;另外,由于异物遮挡和激光雷达未收到回波等,点云数据可能未能准确落在物料表面,如图10(b)所示,此时计算得到物料截面积为0.441 076 m2。这与真实截面积存在较大误差。因此,对点云数据中的噪声进行有效处理尤为重要。

    图  10  点云异常
    Figure  10.  Point cloud anomaly

    基于Akima插值的运输物料截面积计算法对激光雷达输出的点云进行离群点去噪处理,能够有效识别异常的点云数据并对其进行修正。经过修正后计算得到的物料截面积分别为0.432 357 m2和0.431 852 m2,较未修正时的结果更接近真实的物料截面积,修正后的点云分布如图11所示。

    图  11  离群点去噪后点云分布
    Figure  11.  Distribution of point cloud after outlier denoising

    实验平台如图12所示。实验使用的激光雷达是一款室内外通用的TOF测距雷达,测量精度为$ \pm 3\;\mathrm{c}\mathrm{m} $,角度分辨率为0.4°,最大量程为12 m。激光雷达置于带式输送机上方0.5 m处。带式输送机运行速度测试采用滚轮式线速度测试仪,编码器将脉冲信号传送给可编程控制器,可编程控制器通过程序计算出带式输送机的运行速度。考虑到实验室环境中带式输送机极限载物和极限速度的情况,在带速为5,10,15,20 m/min下分别测量体积为1.8,2.4,3.0 dm3的物料,用石英砂作为带式输送机输送的物料。

    图  12  实验平台
    Figure  12.  Experimental platform

    分别采用扇形−三角形计算法和Akima插值法对不同体积和带速的情况进行实验,每组5次实验,数据见表1表2。可看出扇形−三角形计算法的精度较低且不稳定,而Akima插值法的精度全部达90%以上。

    表  1  扇形−三角形计算法实验数据
    Table  1.  Experimental data of sector-triangle calculation method
    实际体积/dm3带速/(m·min−1实验1实验2实验3实验4实验5
    检测值/dm3精度/%检测值/dm3精度/%检测值/dm3精度/%检测值/dm3精度/%检测值/dm3精度/%
    1.852.01188.261.73196.181.59188.411.68993.831.75697.61
    101.94591.971.92493.112.05385.961.92393.171.80699.67
    151.81299.341.80199.922.00288.781.88495.321.86996.17
    202.20277.632.04886.232.08184.382.04386.522.09683.54
    2.452.73785.962.59292.02.58192.442.44698.082.57792.61
    102.77684.352.58992.132.66489.002.85980.872.74185.79
    152.79983.372.88879.682.69787.612.88379.892.63990.05
    202.66788.872.78583.972.70887.162.71087.072.81082.92
    3.053.16594.493.17594.183.26991.043.39186.983.23392.22
    103.21292.943.15194.973.27590.833.31889.413.42086.01
    153.30089.983.35288.283.38187.283.47484.183.35288.28
    203.23092.343.40286.593.47084.343.17594.163.50183.31
    下载: 导出CSV 
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    表  2  Akima插值法实验数据
    Table  2.  Experimental data of Akima interpolation method
    实际体积/dm3带速/(m·min−1实验1实验2实验3实验4实验5
    检测值/dm3精度/%检测值/dm3精度/%检测值/dm3精度/%检测值/dm3精度/%检测值/dm3精度/%
    1.851.77098.361.73996.631.67192.811.68793.721.86796.29
    101.89494.781.81998.941.85397.041.62190.031.74596.97
    151.73496.361.95491.441.69594.171.79599.741.73396.27
    201.72896.011.58387.931.62290.111.74096.671.75797.61
    2.452.63790.132.40799.712.54793.862.20691.912.33597.29
    102.19791.552.33297.182.43398.612.38399.312.56393.2
    152.39999.942.58192.452.52394.872.61691.022.32696.93
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    3.053.00299.942.96598.853.07197.633.26491.223.10196.65
    102.98699.522.87595.853.05098.343.05598.183.07697.46
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    202.89596.512.93697.872.94798.232.84294.733.14995.05
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    分别绘制带速为5,10,15,20 m/min时2种方法的测量值与标准值曲线,如图13所示。可看出Akima插值法的运用提高了测量精度,测量体积更接近真实体积。实验过程中的误差包括激光雷达测量误差、输送带速度测量误差、瞬时煤流量近似计算误差、物料内气隙导致的误差等。

    1) 提出了一种基于Akima插值的带式输送机物料流量激光检测方法。采用激光雷达获取输送带表面点云数据,通过Akima插值法计算带式输送机运输物料截面积。结合带式输送机的运行速度和激光雷达的扫描频率,计算物料的瞬时体积及在一定时间间隔内输送的物料总体积。

    2) 实验结果表明,该方法精度达到90%以上,可靠性高,可以准确得到带式输送机输送物料的瞬时流量和总流量。Akima插值法有效克服了由于过拟合导致的偏离实际曲线的问题,相比于扇形−三角形计算法,更能准确反映物料表面的轮廓形状。

    图  13  不同带速时检测结果
    Figure  13.  Test results under different belt speed
  • 图  1   煤层注水[9]

    Figure  1.   Coal seam water infusion[9]

    图  2   采煤工作面粉尘综合防控系统[8]

    1—支架封闭控尘装置;2—机载除尘装置;3—远射程气水喷雾装置;4—负压除尘微雾净化装置。

    Figure  2.   Dust integrated control system in coal working face[8]

    图  3   掘进工作面喷雾降尘系统[14]

    Figure  3.   Spray dust removal system in heading face[14]

    图  4   长压短抽通风排尘系统

    Figure  4.   Long-pressure short-suction ventilation dust removal system

    图  5   采煤区域粉尘高效净化设备体系

    Figure  5.   High efficiency dust purification equipment system in coal working area

    图  6   掘进区域粉尘高效净化设备体系

    Figure  6.   High efficiency dust purification equipment system in heading area

    图  7   矿井燃油车排放量和最优运行条件预测系统

    Figure  7.   Prediction system of mine fuel vehicle emission and optimal operating conditions

    图  8   井下空气质量在线监测与智能预警平台

    Figure  8.   Underground air quality online monitoring and intelligent early warning platform

    图  9   井下空气质量革命发展目标

    Figure  9.   The development goals of underground air quality revolution

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出版历程
  • 收稿日期:  2023-05-23
  • 修回日期:  2023-06-18
  • 网络出版日期:  2023-06-29
  • 刊出日期:  2023-06-24

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