Affiliation:
1. College of Safety Science and Engineering, Xi’an University of Science and Technology, Xi’an 710054, China
2. State Key Laboratory of Gas Disaster Detecting, Preventing and Emergency Controlling, Chongqing 400037, China
3. Chongqing Research Institute of China Coal Technology Engineering Group, Chongqing 400037, China
Abstract
To address the challenge of intelligently controlling air volume in regions affected by the frequent fluctuations in underground ventilation networks, a remote intelligent air regulation method based on machine learning was presented. This method encompasses three core components: local fan frequency conversion regulation, associated branch air resistance regulation, and a comprehensive integration of both. Leveraging foundational mine ventilation theory, the principles behind branch sensitivity air regulation were dissected. By applying these principles, the key performance indicators crucial for the regulation of air volume within the ventilation system were identified. Subsequently, an intelligent model for regional air volume control was constructed. To validate the approach, an experimental platform for intelligent air volume control was established, guided by geometric, dynamic, and kinematic similarity criteria. Then, the experimental methodologies for simulating various ventilation scenarios were discussed, the data acquisition techniques were introduced, and the obtained results were analyzed. Employing machine learning techniques, we utilized five distinct algorithms to predict the operational parameters of targeted air volume ventilation equipment. It enabled precise and efficient control of air volume within the region. The results indicated that the least squares support vector machine (LS-SVM) stood out by delivering high-precision predictions of target air volume ventilation equipment parameters, all while maintaining a relatively short calculation time. This swift generation of feedback data and corresponding air volume control strategies will contribute to the precise management of air volume in the area. This work served as a valuable theoretical and practical guide for intelligent mining ventilation control.
Funder
Open Fund of the State Key Laboratory of Gas Disaster Detecting, Preventing and Emergency Controlling
Subject
Process Chemistry and Technology,Chemical Engineering (miscellaneous),Bioengineering
Reference34 articles.
1. The intelligent theory and technology of mine ventilation;Lu;J. China Coal Soc.,2020
2. Principle, key technology and preliminary realization of mine intelligent ventilation;Zhou;J. China Coal Soc.,2020
3. Regulation and optimization of air quantity in a mine ventilation network with multiple fans;Wang;Arch. Min. Sci.,2022
4. Yang, S., Zhang, X., Liang, J., and Xu, N. (2023). Research on Optimization of Monitoring Nodes Based on the Entropy Weight Method for Underground Mining Ventilation. Sustainability, 15.
5. On-line monitoring and dynamic analysis and early warning of mine ventilation;Yang;Saf. Coal Mines,2011