Volume 48 Issue 7
Aug.  2022
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LYU Yuanbo, WANG Shibo, GE Shirong, et al. Development of coal and rock identification device based on near-infrared spectroscopy[J]. Journal of Mine Automation，2022，48(7)：32-42. DOI: 10.13272/j.issn.1671-251x.17953
Citation: LYU Yuanbo, WANG Shibo, GE Shirong, et al. Development of coal and rock identification device based on near-infrared spectroscopy[J]. Journal of Mine Automation，2022，48(7)：32-42. DOI: 10.13272/j.issn.1671-251x.17953
shu

Development of coal and rock identification device based on near-infrared spectroscopy

  • 1.

    School of Mechatronic Engineering, China University of Mining and Technology, Xuzhou 221116, China

  • 2.

    School of Mechanical Electronic & Information Engineering, China University of Mining and Technology-Beijing, Beijing 100083, China

  • 3.

    Department of Electrical and Electronic Engineering, Suzhou College of Information Technology, Suzhou 215200, China

  • 4.

    Research and Development Center, CAS Nanjing Astronomical Instruments Co., Ltd., Nanjing 210042, China

  • 5.

    Xuzhou Huada Electromechanical Technology Co., Ltd., Xuzhou 221116, China

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  • Received Date: May 22, 2022
  • Revised Date: July 14, 2022
  • Available Online: August 08, 2022
    Graphical Abstract
    • Abstract
  • The current near-infrared spectroscopy identification of coal and rock is to collect spectral data in a static state for offline identification. The technology cannot meet the need for real-time identification of high-speed moving coal and rock on conveyor during caving operation. In order to solve this problem, a coal and rock identification device is developed based on near-infrared spectroscopy technology. The device consists of a data acquisition and processing device, and a light source and probe integrated device. The light source and probe integrated device is used to collect the reflected light of coal and rock. The improved coal and rock identification algorithms (cosine angle algorithm and correlation coefficient method) in the data acquisition and processing device is used to analyze the spectrum data. The spectrum information can be analyzed immediately after obtaining a coal and rock spectrum curve. Then the current coal and rock type can be determined. In order to obtain the best characteristic band and standard spectral library size of the improved coal and rock identification algorithms, the effects of different characteristic bands and standard spectral library sizes on the identification accuracy are obtained through experiments. The characteristic widths of 1 300 -1 500 nm, 1 800-2 000 nm and 2 100-2 300 nm are suitable for most coal and rock samples. The size of the standard spectral library is positively correlated with the accuracy. It is necessary to increase the number of curves in the standard spectral library during identification. In order to improve the spectral quality collected by the coal and rock identification device, the relative motion of coal and rock and the light source and probe integrated device is simulated in the laboratory. The influence law of different spectral acquisition parameters on spectral quality is explored. The integration time mainly refers to the light intensity of the light source. When the acquisition conditions are good, the integration time should be set to be slightly higher than the lower limit by 5-10 ms. For the fully mechanized top coal caving face, the real-time requirement of coal and rock identification is high, and the coal and rock on the scraper conveyor change rapidly during the coal caving process. The integration number is set to one for the best. The smoothing times mainly refer to the speed of environmental fluctuation, which can be set to eliminate the change of ambient light. In order to improve the identification accuracy of coal and rock identification device in the coal flow movement state of working face, the identification accuracy of improved cosine algorithm and correlation coefficient method in the relative movement of coal and rock and light source and probe integrated device is explored. The improved correlation coefficient method is more suitable for the identification algorithm used in working face, and the accuracy rate is 91.3%. The results of the coal and rock identification test in coal mine show that after collecting the spectral curves of coal and rock in a coal drawing cycle, the device immediately analyzes the spectral information and determines the current coal and rock category by the improved identification algorithm. The device realizes the real-time identification of coal and rock in the coal drawing process.
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