A mine main fans switchover system with lower air flow volatility based on improved particle swarm optimization algorithm
A reliable ventilation system is essential for maintaining a comfortable working environment and ensuring safety production in an underground coal mine. The automated fan switchover technique was developed for changing the main fan for maintenance with lower air flow volatility of underground mine i...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
SAGE Publishing
2019-03-01
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| Series: | Advances in Mechanical Engineering |
| Online Access: | https://doi.org/10.1177/1687814019829281 |
| _version_ | 1828947252278722560 |
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| author | Hengqing Ge Guang Xu Jinxin Huang Xiaoping Ma |
| author_facet | Hengqing Ge Guang Xu Jinxin Huang Xiaoping Ma |
| author_sort | Hengqing Ge |
| collection | DOAJ |
| description | A reliable ventilation system is essential for maintaining a comfortable working environment and ensuring safety production in an underground coal mine. The automated fan switchover technique was developed for changing the main fan for maintenance with lower air flow volatility of underground mine in the switchover process. This article established the optimization model in the main fans switchover process, used the improved particle swarm optimization algorithm to solve the model, and achieved minimum air flow volatility in the fans switchover process. Compared to previous studies, computer simulations have shown that the proposed algorithm can effectively find the global optimal solution with less initial parameters and achieved lower air flow volatility in underground mine. The particle swarm optimization solution, searching diversity, prevents it from confining to local optimal solutions and enhances convergence. The reasonable step length is beneficial to reduce the air flow volatility and main fans switchover time. The air flow volatility is larger comparatively when some doors are nearly open or closed fully at the start–stop phase of the switchover process. A case application in a China’s domestic coal mine shows that the air flow volatility of the underground mine in the main fans switchover process is no more than 0.4%. |
| first_indexed | 2024-12-14T05:26:09Z |
| format | Article |
| id | doaj.art-22e903f84f7a422480435b920b4f58dc |
| institution | Directory Open Access Journal |
| issn | 1687-8140 |
| language | English |
| last_indexed | 2024-12-14T05:26:09Z |
| publishDate | 2019-03-01 |
| publisher | SAGE Publishing |
| record_format | Article |
| series | Advances in Mechanical Engineering |
| spelling | doaj.art-22e903f84f7a422480435b920b4f58dc2022-12-21T23:15:32ZengSAGE PublishingAdvances in Mechanical Engineering1687-81402019-03-011110.1177/1687814019829281A mine main fans switchover system with lower air flow volatility based on improved particle swarm optimization algorithmHengqing Ge0Guang Xu1Jinxin Huang2Xiaoping Ma3School of Information and Control Engineering, China University of Mining and Technology, Xuzhou, ChinaDepartment of Mining Engineering and Metallurgical Engineering, Western Australian School of Mines, Curtin University, Kalgoorlie, WA, AustraliaDepartment of Mining Engineering and Metallurgical Engineering, Western Australian School of Mines, Curtin University, Kalgoorlie, WA, AustraliaSchool of Information and Control Engineering, China University of Mining and Technology, Xuzhou, ChinaA reliable ventilation system is essential for maintaining a comfortable working environment and ensuring safety production in an underground coal mine. The automated fan switchover technique was developed for changing the main fan for maintenance with lower air flow volatility of underground mine in the switchover process. This article established the optimization model in the main fans switchover process, used the improved particle swarm optimization algorithm to solve the model, and achieved minimum air flow volatility in the fans switchover process. Compared to previous studies, computer simulations have shown that the proposed algorithm can effectively find the global optimal solution with less initial parameters and achieved lower air flow volatility in underground mine. The particle swarm optimization solution, searching diversity, prevents it from confining to local optimal solutions and enhances convergence. The reasonable step length is beneficial to reduce the air flow volatility and main fans switchover time. The air flow volatility is larger comparatively when some doors are nearly open or closed fully at the start–stop phase of the switchover process. A case application in a China’s domestic coal mine shows that the air flow volatility of the underground mine in the main fans switchover process is no more than 0.4%.https://doi.org/10.1177/1687814019829281 |
| spellingShingle | Hengqing Ge Guang Xu Jinxin Huang Xiaoping Ma A mine main fans switchover system with lower air flow volatility based on improved particle swarm optimization algorithm Advances in Mechanical Engineering |
| title | A mine main fans switchover system with lower air flow volatility based on improved particle swarm optimization algorithm |
| title_full | A mine main fans switchover system with lower air flow volatility based on improved particle swarm optimization algorithm |
| title_fullStr | A mine main fans switchover system with lower air flow volatility based on improved particle swarm optimization algorithm |
| title_full_unstemmed | A mine main fans switchover system with lower air flow volatility based on improved particle swarm optimization algorithm |
| title_short | A mine main fans switchover system with lower air flow volatility based on improved particle swarm optimization algorithm |
| title_sort | mine main fans switchover system with lower air flow volatility based on improved particle swarm optimization algorithm |
| url | https://doi.org/10.1177/1687814019829281 |
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