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Intelligent monitoring and CNN-based performance evaluation of borehole-pipe-pump gas drainage systems in coal mines
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  • Published: 03 March 2026

Intelligent monitoring and CNN-based performance evaluation of borehole-pipe-pump gas drainage systems in coal mines

  • Yunlei Tong1,
  • Yang Yang1,
  • Jiayan Niu2 &
  • …
  • Fengyang Yang2 

Scientific Reports , Article number:  (2026) Cite this article

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Subjects

  • Energy science and technology
  • Engineering

Abstract

Efficient gas drainage is crucial for safe coal mine production and the clean utilization of gas resources. Despite recent advances, complex geological conditions and unstable system operation limit the effectiveness of traditional monitoring in underground borehole-pipe-pump systems. This study conducts controlled experiments to analyze the operational behavior of the gas drainage network under various leakage scenarios, quantitatively revealing characteristic patterns in negative pressure and flow rate. Based on these insights, an intelligent gas-drainage performance evaluation model using a Convolutional Neural Network (CNN) is developed to automate classification of drainage effectiveness. Experimental results using 10,000 samples from Xinfa Coal Mine show that the CNN model achieves optimal performance with a learning rate of 0.1 and batch size of 256, reaching classification accuracies of 100% for Classes I–III, 93% for Class IV, and 50% for Class V. The proposed approach integrates experimental simulation, leakage characterization, and deep-learning-based evaluation into a unified framework, providing an effective solution for real-time monitoring and intelligent assessment of gas drainage systems. This study offers technical support for improving gas extraction efficiency, enhancing mine safety, and promoting the clean and efficient utilization of coal-mine gas.

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Data availability

The dataset used in this study was collected directly by the authors during field experiments at Xinfa Coal Mine. Due to confidentiality restrictions imposed by the enterprise, the data are not publicly available. Access to the data may be granted upon reasonable request to the corresponding author and with permission from Xinfa Coal Mine.

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Funding

This research received no external funding. The article processing charge (APC) was not funded by any organization.

Author information

Authors and Affiliations

  1. Xin Barag Right Banner Rongda Mining Co., Ltd., Hulunbuir, 021300, China

    Yunlei Tong & Yang Yang

  2. School of Safety Engineering, Heilongjiang University of Science and Technology, Harbin, 150022, China

    Jiayan Niu & Fengyang Yang

Authors
  1. Yunlei Tong
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  2. Yang Yang
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  3. Jiayan Niu
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  4. Fengyang Yang
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Contributions

Conceptualization, Yunlei Tong; methodology, Yang Yang; software, Yunlei Tong; validation, Jiayan Niu; investigation, Fengyang Yang; writing—original draft preparation, Yunlei Tong; writing—review and editing, Jiayan Niu; All authors have read and agreed to the published version of the manuscript.

Corresponding author

Correspondence to Jiayan Niu.

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The authors declare no competing interests.

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Cite this article

Tong, Y., Yang, Y., Niu, J. et al. Intelligent monitoring and CNN-based performance evaluation of borehole-pipe-pump gas drainage systems in coal mines. Sci Rep (2026). https://doi.org/10.1038/s41598-026-42294-5

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  • Received: 05 December 2025

  • Accepted: 25 February 2026

  • Published: 03 March 2026

  • DOI: https://doi.org/10.1038/s41598-026-42294-5

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Keywords

  • Gas drainage
  • Borehole-pipe-pump system
  • Operational monitoring
  • Leakage analysis
  • Intelligent evaluation
  • Convolutional neural network (CNN)
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