Research on Intelligent Ventilation System of Metal Mine Based on Real-Time Sensing Airflow Parameters with a Global Scheme
Source abstract
Abstract
In ventilation systems of metal mines, the real-time measurement of the airflow field and a reduction in pollutants are necessary for clean environmental management and human health. However, the limited quantitative data and expensive detection technology hinder the accurate assessment of mine ventilation effectiveness and safety status. Therefore, we propose a new method for constructing a mine intelligent ventilation system with a global scheme, which can realize the intelligent prediction of unknown points in the mine ventilation system by measuring the airflow parameters of multiple known points. Firstly, the nodal wind pressure method combined with the Hardy–Cross iterative algorithm is used to solve the mine ventilation network, and the airflow parameters under normal operation and extreme working conditions are simulated, based on which an intelligent ventilation training database is established. Secondly, we compared the airflow parameter prediction ability of three different machine learning models with different neural network models based on the collected small-sample airflow field dataset of a mine roadway. Finally, the depth learning method is optimized to build the intelligent algorithm model of the mine ventilation system, and a large number of three-dimensional simulation data and field measurement data of the mine ventilation system are used to train the model repeatedly to realize the intelligent perception of air flow parameters of a metal mine ventilation network and the construction of an intelligent ventilation system. The results show that the maximum error of a single airflow measurement point is 1.24%, the maximum overall error is 3.25%, and the overall average error is 0.51%. The intelligent algorithm has a good model training effect and high precision and can meet the requirements of the research and application of this project. Through case analysis, this method can predict the airflow parameters of any position underground and realize the real-time control of mine safety.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Coal Properties and Utilization · Air Quality Monitoring and Forecasting
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
School of Resources and Safety Engineering, Central South University, Changsha 410083, China
Xinjiang Kalatongke Mining Co., Ltd., Altay 836107, China
School of Quality and Safety Engineering, China Jiliang University, Hangzhou 310018, China
Funding
National Natural Science Foundation of China · Natural Science Foundation of Hunan Province
References (32)
Show source-provided references
1. Brkić, D., and Praks, P. (2019). Short Overview of Early Developments of the Hardy Cross Type Methods for Computation of Flow Distribution in Pipe Networks. Appl. Sci., 9.10.3390/app9102019 ↗
2. Ventilation and Heat Exchange Characteristics in High Geotemperature Tunnels Considering Buoyancy-Driven Flow and Groundwater Flow10.1016/j.ijthermalsci.2021.107400 ↗
3. Analysis on the Development Status of Coal Mine Dust Disaster Prevention Technology in China
4. Coalbed Methane Emissions and Drainage Methods in Underground Mining for Mining Safety and Environmental Benefits: A Review10.1016/j.psep.2019.05.010 ↗
5. Penner, K.B. (2015). Ventilation Design Strategy to Meet Future Production Requirements for a Saskatchewan Potash Mine. [Master’s Thesis, Queen’s University].
6. Key Engineering Technologies to Achieve Green, Intelligent, and Sustainable Development of Deep Metal Mines in China10.1016/j.eng.2021.07.010 ↗
7. Application of Ventilation Management Programs for Improved Mine Safety10.1016/j.ijmst.2017.05.018 ↗
8. Gao, K., Qi, Z., Liu, Y., and Zhang, J. (2022). Calculation Model for Ventilation Friction Resistance Coefficient by Surrounding Rock Roughness Distribution Characteristics of Mine Tunnel. Sci. Rep., 12.10.1038/s41598-022-07115-5 ↗
9. Ventilation Optimization through Digital Transformation10.17159/2411-9717/1950/2022 ↗
10. An Optimization Method of Mine Ventilation System Based on R2 Index Hybrid Multi-Objective Equilibrium Optimization Algorithm10.1016/j.egyr.2022.08.228 ↗
11. Experimental Study on Precise Control Air Quantity on Demand by the Cooperation of Multiple Main Fans in Mine10.1016/j.jclepro.2023.137626 ↗
12. Optimizing Arrangement of Air Distribution Controllers in Mine Ventilation System10.1134/S1062739148050140 ↗
13. Using Inverting CO Critical Value to Predict Coal Spontaneous Combustion Severity in Mine Gobs with Considering Air Leakages—A Case Study10.1016/j.psep.2022.08.023 ↗
14. Huang, D., Liu, J., Deng, L., Li, X., and Song, Y. (2019). An Adaptive Kalman Filter for Online Monitoring of Mine Wind Speed. Arch. Min. Sci., 813–827.10.20944/preprints201903.0048.v1 ↗
15. Determination of Velocity Correction Factors for Real-Time Air Velocity Monitoring in Underground Mines10.1007/s40789-017-0184-z ↗
16. An Efficient Method for Optimizing Sensors’ Layout for Accurate Measurement of Underground Ventilation Networks10.1109/ACCESS.2023.3295779 ↗
17. Computerized simulation of mine ventilation networks for sustainable decision making process10.30638/eemj.2014.159 ↗
18. A Hybrid-Encoding Adaptive Evolutionary Strategy Algorithm for Windage Alteration Fault Diagnosis10.1016/j.psep.2020.01.037 ↗
19. Wang, D., Liu, J., Lijun, D., and Honglin, W. (2023). A Supervised Diagnostic Experiment of Resistance Variable Multifault Locations in a Mine Ventilation System. Sci. Rep., 13.10.1038/s41598-023-32530-7 ↗
20. Theory of RPOD Adjustment of Air Volume for Mine Intelligent Ventilation
21. Wang, Y., Pan, Q., Gao, L., Cao, Y., Liu, P., Yi, H., and Gao, C. (2024). Analysis of Distribution Method of Designed Air Quantity in Coal Mine Ventilation—A Case Study. Sci. Rep., 14.10.1038/s41598-024-61787-9 ↗
22. Intelligent Safety Adjustment of Branch Airflow Volume during Ventilation-on-Demand Changes in Coal Mines10.1016/j.psep.2017.08.024 ↗
23. Research progress and prospects of intelligent technology in underground mining of hard rock mines10.1016/j.gsme.2024.03.007 ↗
24. Liu, Y., Liu, Z., Gao, K., Huang, Y., and Zhu, C. (2022). Efficient Graphical Algorithm of Sensor Distribution and Air Volume Reconstruction for a Smart Mine Ventilation Network. Sensors, 22.10.3390/s22062096 ↗
25. Machine Learning Algorithm Selection for Windage Alteration Fault Diagnosis of Mine Ventilation System10.1016/j.aei.2022.101666 ↗
26. Wang, J., Jia, M., Bin, L., Wang, L., and Zhong, D. (2023). Regulation and Optimization of Air Quantity in a Mine Ventilation Network with Multiple Fans. Arch. Min. Sci., 179–193.
27. Zhang, W., Li, Y., and Li, J. (2022). Data Processing Method of Mine Wind Speed Monitoring Based on an Improved Fuzzy C-Means Clustering Algorithm. Appl. Sci., 12.10.3390/app12199701 ↗
28. Method for the Selection of Inputs and Structure of Feedforward Neural Networks10.1016/j.compchemeng.2006.01.007 ↗
29. Combining Expert Weights for Online Portfolio Selection Based on the Gradient Descent Algorithm10.1016/j.knosys.2021.107533 ↗
30. Influence of sample attributes on generalization performance of machine learning models for windage alteration fault diagnosis of the mine ventilation system10.1016/j.eswa.2022.119320 ↗
31. Accurate and real-time network calculation for mine ventilation without wind resistance measurement10.1016/j.jweia.2022.105183 ↗
32. An intelligent airflow perception model for metal mines based on CNN-LSTM architecture10.1016/j.psep.2024.05.044 ↗