Abstract

The ventilation system in underground mine is an important guarantee for workers’ safety and environmental conditions. As the mining activities continue, the mine ventilation system is constantly changing. Therefore, to ensure ventilation on demand, the mine ventilation network regulation and optimization are very important. In this paper, the path method based on graph theory is studied. However, the existing path algorithms do not meet the needs of actual mine ventilation regulation and optimization. Therefore, in this paper, the path algorithm is optimized and improved from four aspects. First, based on the depth-first search algorithm, the independent path search algorithm is proposed to solve the problem of false paths in the independent path searched when there is a unidirectional circuit in the ventilation network. Secondly, the independent path calculation formula is amended to ensure that the number of the independent path for the ventilation network with a downcast and an upcast shaft, multi-downcast and multi-upcast shaft and unidirectional circuits is calculated accurately. Thirdly, to avoid both an increase in the number of control points in the multi-fan ventilation network and disturbances in the airflow distribution by determining the reference path through all the independent paths, all the independent paths with the shared fan must be identified. Fourthly, The number and the position of the regulators in the ventilation network are determined and optimized, and the final optimization of air quantity regulation for the ventilation network is realized. The case study shows that this algorithm can effectively and accurately realize the regulation of air quantity of a multi-fan mine ventilation network.

Details

Title
Regulation and Optimization of Air Quantity in a Mine Ventilation Network with Multiple Fans
Author
Wang, Jinmiao; Jia, Mingtao; Bin, Lin; Wang, Liguan; Zhong, Deyun
Pages
179-193
Publication year
2022
Publication date
2022
Publisher
Polish Academy of Sciences
ISSN
08607001
e-ISSN
16890469
Source type
Scholarly Journal
Language of publication
English
ProQuest document ID
2650257396
Copyright
© 2022. This work is licensed under https://creativecommons.org/licenses/by-sa/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.