Volume 50 Issue 9
Sep.  2024
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WANG Jiachen, YANG Shengli, LI Lianghui, et al. Research progress on intelligent coal caving theory and technology[J]. Journal of Mine Automation，2024，50（9）：1-12.  doi: 10.13272/j.issn.1671-251x.18213
Citation: WANG Jiachen, YANG Shengli, LI Lianghui, et al. Research progress on intelligent coal caving theory and technology[J]. Journal of Mine Automation，2024，50（9）：1-12.  doi: 10.13272/j.issn.1671-251x.18213
shu

Research progress on intelligent coal caving theory and technology

doi: 10.13272/j.issn.1671-251x.18213
  • 1.

    School of Energy and Mining Engineering, China University of Mining and Technology-Beijing, Beijing 100083, China

  • 2.

    Engineering Research Center of Green and Intelligent Mining for Thick Coal Seam, Ministry of Education, Beijing 100083, China

  • 3.

    Coal Industry Engineering Research Center of Top-coal Caving Mining,Beijing 100083, China

  • Received Date: 2024-08-19
  • Rev Recd Date: 2024-09-25
    • Available Online: 2024-10-17
    Abstract
  • The longwall top-coal caving technology is an effective method for extracting thick and ultra-thick coal seams, and it has become a hallmark technology in China's coal mining industry. This paper reviews the research progress on the "Four elements" coal caving theory, the relationship between the top coal recovery rate and the rock mixed ratio, a recovery rate prediction model based on block distribution, and the relationship between instantaneous rock mixed ratio and cumulative rock mixed ratio. The challenges of intelligent coal caving technology are analyzed, emphasizing that the rock mixed ratio is a key factor affecting the top coal recovery rate and coal quality. Rapid and accurate calculation of the rock mixed ratio during the coal caving process is crucial for breakthroughs in intelligent coal caving technology. This technology is categorized into two types: non-image recognition and image recognition. The research progress, advantages, disadvantages, and usage conditions of different technologies are discussed in detail. Non-image recognition intelligent coal caving technology includes memory coal caving technology, sound and vibration signal detection technology, γ-ray detection technology, ground penetrating radar technology, microwave irradiation combined with infrared detection technology, and laser scanning coal caving monitoring technology. Image-based intelligent coal caving technology encompasses precise control of underground illumination environment, dust removal algorithms for coal caving images, accuracy assurance strategies for rock mixed ratio calculations, and infrared image recognition of coal and rock.

     

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