| Citation: |
ZHANG Qilong, ZHOU Bing, WANG Guoqiang, et al. Intelligent perception method for real-time airflow parameters in metal mines and its application[J]. Journal of Mine Automation，2025，51（2）：121-130. DOI: 10.13272/j.issn.1671-251x.2024090057
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Real-time acquisition of global airflow parameters is a key technology for the intelligent control of the ventilation system in metal mines. Currently, AI-based prediction methods for airflow parameters are limited by data dependency, computational costs, and adaptability to different operating conditions. To address this issue, an intelligent perception method for global airflow parameters suitable for metal mines was proposed. First, a wind speed measurement device was used to monitor the average airflow velocity in the roadways in real-time. Monitoring points were strategically arranged, and an airflow parameter monitoring system was established to obtain key ventilation parameters such as air volume and air pressure. Then, based on the actual conditions of the ventilation system and a three-dimensional schematic diagram, a three-dimensional simulation system was developed and optimized using actual measured airflow data. The system simulated the airflow parameters of the mine under different fan operating conditions and natural wind pressure states. Next, based on the simulation data, a training and testing dataset for the AI algorithm model was constructed. Finally, the airflow information collected by the airflow parameter monitoring system was used as input for the AI algorithm model, enabling real-time perception of the global airflow distribution in the mine. Performance evaluation of the intelligent perception model was conducted using ventilation network calculation data. The results showed: ① the model's coefficient of determination (R2) was 0.998, the root mean square error was 0.215 9, the mean absolute error was 0.085, and the mean absolute percentage error was 1.89%. ② The model's predicted values closely aligned with the actual observed values, verifying the excellent performance of the multilayer perceptron (MLP) in airflow parameter prediction. ③ The model maintained its prediction accuracy when faced with different datasets, demonstrating good generalization ability. ④ The average error of the intelligent ventilation system's perception data was controlled within 5%, and the perceived underground airflow parameters were in close agreement with the actual measured values.
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