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Coal resources under carbon peak: Segmentation of massive laser point clouds for coal mining in underground dusty environments using integrated graph deep learning model

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  • Xing, Zhizhong
  • Zhao, Shuanfeng
  • Guo, Wei
  • Meng, Fanyuan
  • Guo, Xiaojun
  • Wang, Shenquan
  • He, Haitao

Abstract

With the background of China's carbon peak, the low-carbon and sustainable development of the coal industry is vital to China's national energy security. Because the underground visibility is low and the dust is continuously spreading, coal mine point cloud segmentation can provide a key basis for underground environment perception, and then provides a premise for the construction of green coal mines. In this study, we propose to segment the coal mining face (CMF) point cloud under the harsh environment based on the advanced dynamic graph convolution neural network (DGCNN) and to obtain the information of the coal cutting roof line. The results show that the multi-level and series pooling DGCNN (ML&SP-DGCNN) which was constructed on the basis of a large number of previous studies shows the best performance. In this study, the coal cutting roof line obtained by segmenting the CMF point cloud provides a key basis for dynamically correcting the underground geological model and straightening the CMF. More importantly, the established CMF point cloud segmentation model lays a foundation for perceiving the underground environment, which is of great help to realize the sustainable green production of coal resources.

Suggested Citation

  • Xing, Zhizhong & Zhao, Shuanfeng & Guo, Wei & Meng, Fanyuan & Guo, Xiaojun & Wang, Shenquan & He, Haitao, 2023. "Coal resources under carbon peak: Segmentation of massive laser point clouds for coal mining in underground dusty environments using integrated graph deep learning model," Energy, Elsevier, vol. 285(C).
    • Handle: RePEc:eee:energy:v:285:y:2023:i:c:s0360544223021655
    DOI: 10.1016/j.energy.2023.128771
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    References listed on IDEAS

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    1. Guo, Shanshan & Ma, Liang, 2023. "A comparative study of different deep learning algorithms for lithium-ion batteries on state-of-charge estimation," Energy, Elsevier, vol. 263(PC).
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