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<p>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.</p>

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

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年3月3日

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  • Yunlei Tong,
  • Yang Yang,
  • Jiayan Niu,
  • Fengyang Yang

摘要

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.