Volume 50 Issue 5
May  2024
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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

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

doi: 10.13272/j.issn.1671-251x.2024040010
  • Received Date: 2024-04-02
  • Rev Recd Date: 2024-05-20
  • Available Online: 2024-06-13
  • Real time online detection of the particle size of the overflow in the selection and classification of fine-grained coal can be carried out, and the classification parameters can be adjusted to reduce the content of coarse particles in the overflow and improve the total clean coal recovery rate. The current research generally limits the detection of overflow particle size to around 180 μm, and the upper limit of slurry volume concentration is 10%. It cannot meet the requirements of overflow particle size detection for fine-grained coal classification cyclones with coarse particle size, wide particle size range, and high volume concentration. A set of ultrasonic online particle size detection system has been developed to improve the upper limit of coal particle size and slurry volume concentration detection. Based on the ultrasonic attenuation model, a coal particle size detection model suitable for on-site conditions of fine-grained coal classification with coal particle size of 44.5-600 μm and slurry volume concentration of 0-40% is constructed. A coal particle size distribution prediction model is established using a BP neural network optimized by particle swarm optimization algorithm, achieving the prediction of the particle size distribution of the overflow slurry in a fine-grained coal classification cyclone. The simulation results based on the coal particle size detection model show that the ultrasonic attenuation value decreases first and then increases with the increase of coal particle size, and increases with the increase of ultrasonic frequency and slurry volume concentration. The ultrasonic online particle size detection system and coal particle size distribution prediction model are respectively used to detect the distribution of overflow particle size (actual value is 150.0, 215.0, 315.0 μm) in a hydraulic classification cyclone of a certain mine. The results show that the relative errors of the measurement values of the detection system are 10.87%, 9.81%, 8.48%, and the relative errors of the predicted values of the prediction model are 9.27%, 6.05%, and 6.92%. It indicates that the research have achieved accurate detection of overflow particle size in fine-grained coal classification.

     

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  • [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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