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细粒煤分级溢流颗粒粒度在线检测研究

孙豪智 马娇 史长亮 王函露

孙豪智,马娇,史长亮,等. 细粒煤分级溢流颗粒粒度在线检测研究[J]. 工矿自动化,2024,50(5):44-51, 59.  doi: 10.13272/j.issn.1671-251x.2024040010
引用本文: 孙豪智,马娇,史长亮,等. 细粒煤分级溢流颗粒粒度在线检测研究[J]. 工矿自动化,2024,50(5):44-51, 59.  doi: 10.13272/j.issn.1671-251x.2024040010
SUN Haozhi, MA Jiao, SHI Changliang, et al. Research on online detection of particle size in fine-grained coal classification overflow[J]. Journal of Mine Automation,2024,50(5):44-51, 59.  doi: 10.13272/j.issn.1671-251x.2024040010
Citation: SUN Haozhi, MA Jiao, SHI Changliang, et al. Research on online detection of particle size in fine-grained coal classification overflow[J]. Journal of Mine Automation,2024,50(5):44-51, 59.  doi: 10.13272/j.issn.1671-251x.2024040010

细粒煤分级溢流颗粒粒度在线检测研究

doi: 10.13272/j.issn.1671-251x.2024040010
基金项目: 河南省科技攻关计划项目(232102231028);河南理工大学博士基金项目(2022-50)。
详细信息
    作者简介:

    孙豪智(1997—),男,河南郑州人,硕士研究生,研究方向为煤炭洗选工艺及自动化,E-mail:2284259886@qq.com

  • 中图分类号: TD94

Research on online detection of particle size in fine-grained coal classification overflow

  • 摘要: 对细粒煤分选中分级溢流颗粒粒度进行实时在线检测,进而调控分级参数,可减少溢流中粗颗粒含量,提高总精煤回收率。现有研究对溢流颗粒粒度的检测上限普遍在180 μm左右,矿浆体积浓度上限为10%,无法满足粒度较粗、粒级较宽且体积浓度较高的细粒煤分级旋流器溢流颗粒粒度检测要求。为提高煤颗粒粒度和矿浆体积浓度检测上限,开发了一套超声波在线颗粒粒度检测系统。基于超声波声衰减模型,构建了适用于煤颗粒粒度为44.5~600 μm、矿浆体积浓度为0~40%的细粒煤分级现场工况的煤颗粒粒度检测模型。采用粒子群优化算法优化的BP神经网络建立了煤颗粒粒度分布预测模型,实现对细粒煤分级旋流器溢流矿浆粒度分布预测。基于煤颗粒粒度检测模型的模拟结果表明,超声波衰减值随煤颗粒粒度增大而先减小后增大,随超声波频率和矿浆体积浓度增大而增大。分别使用超声波在线颗粒粒度检测系统和煤颗粒粒度分布预测模型对某矿水力分级旋流器溢流颗粒粒度(实际值为150.0,215.0,315.0 μm)分布进行检测,结果表明检测系统测量值相对误差为10.87%,9.81%,8.48%,预测模型的预测值相对误差为9.27%,6.05%,6.92%,均实现了细粒煤分级溢流颗粒粒度的准确检测。

     

  • 图  1  超声波在线颗粒粒度检测系统组成

    Figure  1.  Composition of ultrasonic online particle size detection system

    图  2  超声波在线颗粒粒度检测系统工作流程

    Figure  2.  Work flow of ultrasonic online particle size detection system

    图  3  煤颗粒正态分布等距分组结构

    Figure  3.  Equidistant grouping structure of normal distribution of coal particles

    图  4  体积浓度为10%时,不同频率下超声波衰减谱

    Figure  4.  Ultrasonic attenuation spectra at different frequencies when the volume concentration is 10%

    图  5  超声波频率为1 MHz时,不同体积浓度下超声波衰减谱

    Figure  5.  Ultrasonic attenuation spectra at different volume concentrations with ultrasonic frequency of 1 MHz

    图  6  超声波频率为2 MHz时,不同体积浓度下超声波衰减谱

    Figure  6.  Ultrasonic attenuation spectra at different volume concentrations with ultrasonic frequency of 2 MHz

    图  7  煤颗粒粒度分布预测结果

    Figure  7.  Prediction results of coal particle size distribution

    图  8  不同频率下超声波衰减值−煤颗粒粒度曲线

    Figure  8.  Ultrasonic attenuation value-coal particle size curve at different frequencies

    图  9  超声波频率为1 MHz时,不同体积浓度下超声波衰减值−煤颗粒粒度曲线

    Figure  9.  Ultrasonic attenuation value-particle size curve at different volume concentrations when ultrasonic frequency is 1 MHz

    图  10  超声波频率为2 MHz时,不同体积浓度下超声波衰减值−煤颗粒粒度曲线

    Figure  10.  Ultrasonic attenuation value-particle size curve at different volume concentrations when ultrasonic frequency is 2 MHz

    表  1  煤颗粒粒度分布区间划分

    Table  1.   Division of coal particle size distribution interval

    组号 粒度 组号 粒度
    区间/目 平均值/μm 区间/目 平均值/μm
    1 20~40 600.0 5 120~150 113.0
    2 40~60 315.0 6 150~200 87.0
    3 60~80 215.0 7 200~320 59.5
    4 80~120 150.0 8 320~325 44.5
    下载: 导出CSV

    表  2  PSO算法参数

    Table  2.   PSO algorithm parameters

    参数 参数
    粒子维数 50 最小速度 0.001
    最大权值 0.9 加速度因子 2
    最小权值 0.3 粒子上界 1
    最大速度 0.2 粒子下界 0
    下载: 导出CSV

    表  3  不同粒度的煤颗粒含量占比

    Table  3.   Proportion of coal particle content with different particle sizes

    粒度/μm 44.5 59.5 87.0 113.0
    占比/% 2.61 3.14 3.08 2.29
    粒度/μm 150.0 215.0 315.0 605.0
    占比/% 19.08 41.83 23.88 4.03
    下载: 导出CSV

    表  4  不同粒度的煤颗粒对应的超声波衰减值

    Table  4.   Ultrasonic attenuation values corresponding to coal particle with different particle sizes

    煤颗粒粒度/μm 超声波衰减值/ (Np·m−1
    1 MHz 2 MHz
    44.5 36.51 51.35
    59.5 27.33 40.76
    87.0 18.10 29.08
    113.0 14.99 28.43
    150.0 12.74 33.58
    215.0 13.55 64.07
    315.0 16.25 175.99
    605.0 74.10 500.80
    下载: 导出CSV

    表  5  超声波频率为1 MHz时,不同体积浓度下不同粒度的煤颗粒对应的超声波衰减值

    Table  5.   Ultrasonic attenuation values corresponding to coal particles of different diameters at different volume concentrations when the ultrasonic frequency is 1 MHz

    煤颗粒粒度/μm 不同体积浓度下超声波衰减值/(Np·m−1
    5% 10% 15% 20% 30% 40%
    44.5 22.23 36.51 52.32 69.27 94.46 253.56
    59.5 16.64 27.33 39.16 51.85 70.71 189.80
    87.0 11.02 18.1 25.93 34.34 46.83 125.69
    113.0 9.13 14.99 21.49 28.45 38.79 104.13
    150.0 7.76 12.74 18.27 24.19 32.99 88.55
    215.0 8.26 13.55 19.44 25.74 35.10 94.22
    315.0 9.90 16.25 23.31 30.86 42.08 112.97
    605.0 45.16 74.10 106.29 140.73 191.91 515.12
    下载: 导出CSV

    表  6  超声波频率为 2 MHz时,不同体积浓度下不同粒度的煤颗粒对应的超声波衰减值

    Table  6.   Ultrasonic attenuation values corresponding to coal particles of different diameters at different volume concentrations when the ultrasonic frequency is 2 MHz

    煤颗粒粒度/μm 不同体积浓度下超声波衰减值/(Np·m−1
    5% 10% 15% 20% 30% 40%
    44.5 45.02 51.35 58.47 66.95 77.31 110.33
    59.5 35.73 40.76 46.41 52.42 61.36 87.56
    87.0 25.49 29.08 33.10 37.39 43.76 62.46
    113.0 24.91 28.43 32.36 36.55 42.78 61.05
    150.0 29.42 33.58 38.21 43.17 50.52 72.10
    215.0 56.14 64.07 72.91 82.36 96.40 137.57
    315.0 154.22 175.99 200.28 226.24 264.81 377.90
    605.0 438.90 500.80 570.00 643.89 753.65 1 075.52
    下载: 导出CSV

    表  7  煤颗粒粒度预测值、测量值与实际值对比

    Table  7.   Comparison of coal particle size prediction values, measurement values and actual values

    实际值/μm 测量值/μm 测量相对误差/% 预测值/μm 预测相对误差/%
    150.0 166.3 10.87 163.9 9.27
    215.0 236.1 9.81 228.0 6.05
    315.0 341.7 8.48 336.8 6.92
    下载: 导出CSV
  • [1] 谢苗,朱昀,张保国. 水力分级旋流器工艺参数匹配优化研究[J/OL]. 机械科学与技术:1-9[2024-03-27]. https://doi.org/10.13433/j.cnki.1003-8728.20230033.

    XIE Miao,ZHU Yun,ZHANG Baoguo. Study on process parameter matching optimization of hydrocyclone[J]. Mechanical Science and Technology for Aerospace Engineering:1-9[2024-03-27]. https://doi.org/10.13433/j.cnki.1003-8728.20230033.
    [2] 郭伟. 水力分级旋流器分离粒度的选择与控制[J]. 煤炭加工与综合利用,2023(8):62-65.

    GUO Wei. Selection and control of hydraulic classification cyclone separation size[J]. Coal Processing & Comprehensive Utilization,2023(8):62-65.
    [3] 李波. 矿产资源在现代经济发展中的作用[J]. 有色金属工程,2024,14(3):205. doi: 10.3969/j.issn.2095-1744.2024.03.026

    LI Bo. The role of mineral resources in modern economic development[J]. Nonferrous Metals Engineering,2024,14(3):205. doi: 10.3969/j.issn.2095-1744.2024.03.026
    [4] 钱刚. 浅议低品位矿产资源的开发与利用[J]. 中国金属通报,2020(1):49,51.

    QIAN Gang. A brief discussion on the development and utilization of low-grade mineral resources[J]. China Metal Bulletin,2020(1):49,51.
    [5] 丛日红,赵瑞. 在线粒度检测在煤泥水系统中的应用[J]. 中国矿业,2021,30(增刊1):134-137,142.

    CONG Rihong,ZHAO Rui. Application of online particle size detection in coal slurry system[J]. China Mining Magazine,2021,30(S1):134-137,142.
    [6] 黄细聪,周峰,吴建,等. 机器视觉检测技术在圆筒造球机粒度检测中的应用[J]. 矿业工程,2023,21(3):67-69.

    HUANG Xicong,ZHOU Feng,WU Jian,et al. Application of machine vision detection technology in particle size detection of drum pelletizer[J]. Mining Engineering,2023,21(3):67-69.
    [7] 何桂春,倪文. 非线性方法在超声波粒度检测建模中的应用[M]. 北京:冶金工业出版社,2021.

    HE Guichun,NI Wen. Application of nonlinear method in ultrasonic particle size detection modeling[M]. Beijing:Metallurgical Industry Press,2021.
    [8] 胡志平. PSI−200粒度仪的简介与应用[J]. 有色金属(选矿部分),2003(2):30-32. doi: 10.3969/j.issn.1671-9492.2003.02.010

    HU Zhiping. Introduction and application of PSI-200 particle size analyzer[J]. Nonferrous Metals(Mineral Processing Section),2003(2):30-32. doi: 10.3969/j.issn.1671-9492.2003.02.010
    [9] 黄习敏. 基于图像识别的在线粒度检测方法研究与检测系统设计[D]. 赣州:江西理工大学,2019.

    HUANG Ximin. Research and design of online particle size detection system based on image recognition[D]. Ganzhou:Jiangxi University of Science and Technology,2019.
    [10] FU Yihao,ALDRICH C. Online particle size analysis on conveyor belts with dense convolutional neural networks[J]. Minerals Engineering,2023,193. DOI: 10.1016/j.mineng.2023.108019.
    [11] ZHANG Zelin,LIU Yang,HU Qi,et al. Multi-information online detection of coal quality based on machine vision[J]. Powder Technology,2020,374:250-262. doi: 10.1016/j.powtec.2020.07.040
    [12] WANG X,SU M X,CAI X S. Effects of material viscosity on particle sizing by ultrasonic attenuation spectroscopy[J]. Procedia Engineering,2015,102:256-264. doi: 10.1016/j.proeng.2015.01.141
    [13] WU Yuanyi,LIN Mengxing,ROHANI S. Particle characterization with on-line imaging and neural network image analysis[J]. Chemical Engineering Research and Design,2020,157:114-125. doi: 10.1016/j.cherd.2020.03.004
    [14] 薛明华,夏多兵,胡子健,等. 基于超声波衰减谱的石膏浆液粒度测量方法[J]. 中国电力,2019,52(9):173-178.

    XUE Minghua,XIA Duobing,HU Zijian,et al. Ultrasonic attenuation spectrum based method for measuring the particle size distribution of gypsum slurry[J]. Electric Power,2019,52(9):173-178.
    [15] 李烨明,谢代梁,胡鹤鸣,等. 基于超声波衰减效应的悬移质粒径分布反演[J]. 水力发电学报,2020,39(1):21-30. doi: 10.11660/slfdxb.20200103

    LI Yeming,XIE Dailiang,HU Heming,et al. Inversion of particle size distributions of suspended loads based on ultrasonic attenuation effect[J]. Journal of Hydroelectric Engineering,2020,39(1):21-30. doi: 10.11660/slfdxb.20200103
    [16] TSUJI K,NAKANISHI H,NORISUVE T. Viscoelastic ECAH:scattering analysis of spherical particles in suspension with viscoelasticity[J]. Ultrasonics,2021,115:463-474.
    [17] WANG Mi,ZHENG Dandan,DONG Jun,et al. Comparison of ultrasonic attenuation models for small droplets measurement based on numerical simulation and experiment[J]. Applied Acoustics,2021,183:1-10.
    [18] TEBBUTT J S,CHALLIS R E. UItrasonic wave propagation in colloidal suspensions and emulsions:a comparison of four models[J]. Ultrasonics,1996,34(2/4/5):363-368.
    [19] AUSTIN J C,HOLMES A K,TEBBUTT J S,et al. Ultrasonic wave propagation in colloid suspensions and emulsions:Recent experimental results[J]. Ultrasonics,1996,34(2):369-374.
    [20] 王亚娟. 氨基化核壳结构的磁性微球的制备与研究[D]. 天津:天津工业大学,2021.

    WANG Yajuan. Preparation and research of magnetic microspheres with amino core-shell structure[D]. Tianjin:Tianjin Polytechnic University,2021.
    [21] WANG Xuezhong,LIU Lande,LI R F,et al. Online characterisation of nanoparticle suspensions using dynamic light scattering,ultrasound spectroscopy and process tomography[J]. Chemical Engineering Research & Design,2009,87(6):874-884.
    [22] 姚文学. 超声波衰减谱法在线测量微纳米颗粒粒度分布的研究[D]. 广州:华南理工大学,2016.

    YAO Wenxue. Study on ultrasound attenuation spectroscopy for on-line characterization of size distribution of nano and microparticles in slurries[D]. Guangzhou:South China University of Technology,2016.
    [23] 何桂春. 超声波矿浆粒度检测的非线性建模研究[D]. 北京:北京科技大学,2006.

    HE Guichun. Study on nonlinear modeling for particle size measurement based on ultrasound in mineral slurry[D]. Beijing:University of Science and Technology Beijing,2006.
    [24] HE Guichun,NI Wen. Ultrasonic attenuation model for measuring particle size and inverse calculation of particle size distribution in mineral slurries[J]. Journal of Central South University of Technology(English Edition),2006,13(4):445-450. doi: 10.1007/s11771-006-0065-x
    [25] 何桂春,倪文,梁雪梅. 基于分形修正的超声波衰减−粒度建模[J]. 金属矿山,2006(4):50-54. doi: 10.3321/j.issn:1001-1250.2006.04.016

    He Guichun,NI Wen,LIANG Xuemei. Modeling for ultrasonic attenuation-particle size based on fractal modification[J]. Metal Mine,2006(4):50-54. doi: 10.3321/j.issn:1001-1250.2006.04.016
    [26] 毕斯琴. 基于超声波的水煤浆粒度在线测量方法研究[D]. 武汉:武汉工程大学,2023.

    BI Siqin. Study on online measurement method of coal water slurry size based on ultrasonic wave[D]. Wuhan:Wuhan Institute of Technology,2023.
    [27] 何桂春,倪文,毛益平. 超声波矿浆粒度检测研究[J]. 矿冶工程,2005(6):45-47. doi: 10.3969/j.issn.0253-6099.2005.06.012

    HE Guichun,NI Wen,MAO Yiping. Study on particle size measurement for mineral slurry by ultrasonic techniques[J]. Mining and Metallurgical Engineering,2005(6):45-47. doi: 10.3969/j.issn.0253-6099.2005.06.012
    [28] 谢良才. 基于BP神经网络的数据挖掘技术探究及其在煤热转化数据规律分析中的应用[D]. 西安:西北大学,2021.

    XIE Liangcai. Research on data mining technology based on improved BP neural network and its application in the law analysis of coal thermal conversion data[D]. Xi'an:Northwestern University,2021.
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  • 收稿日期:  2024-04-02
  • 修回日期:  2024-05-20
  • 网络出版日期:  2024-06-13

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