IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v15y2023i20p14749-d1257671.html
   My bibliography  Save this article

Research on Optimization of Monitoring Nodes Based on the Entropy Weight Method for Underground Mining Ventilation

Author

Listed:
  • Shouguo Yang

    (State Key Laboratory of Gas Disaster Detecting, Preventing and Emergency Controlling, Chongqing 400037, China
    College of Safety Science and Engineering, Xi’an University of Science and Technology, Xi’an 710054, China)

  • Xiaofei Zhang

    (College of Safety Science and Engineering, Xi’an University of Science and Technology, Xi’an 710054, China)

  • Jun Liang

    (Chongqing Research Institute of China Coal Technology Engineering Group, Chongqing 400037, China)

  • Ning Xu

    (College of Safety Science and Engineering, Xi’an University of Science and Technology, Xi’an 710054, China)

Abstract

Air pressure monitoring is the basis of mining-intelligent ventilation. In order to optimize the coverage of monitoring nodes, the node importance in the ventilation network was taken as the optimization basis in this study. Two evaluation indexes of the extent of node coverage and the influence degree of nodes were obtained by analyzing the influence degree of node air pressure. The entropy weight method (EWM) was used to weigh the evaluation indexes to obtain the importance of all nodes in the ventilation network. A node layout method with node importance as the optimization of air pressure-monitoring nodes was proposed. The minimum distance correlation between the limited monitoring nodes and the monitored nodes was set as the constraint condition, and any air pressure monitoring node could only monitor its adjacent nodes. The nodes with high node importance were selected as air pressure-monitoring nodes in turn until the coverage of air pressure-monitoring nodes in the ventilation network was maximized. By applying the entropy weight method (EWM) and the clustering algorithm (CA) to the case mine, the research results show that the application of the entropy weight method (EWM) to optimize the air pressure-monitoring nodes was more feasible than the clustering algorithm (CA). The coverage rate was 81.6% at different constraint values, and the maximum coverage rate was 92.1%, which meets the needs of arranging the least air pressure-monitoring nodes to monitor the maximum range of air pressure changes and can carry out full coverage monitoring of mine air pressure.

Suggested Citation

  • Shouguo Yang & Xiaofei Zhang & Jun Liang & Ning Xu, 2023. "Research on Optimization of Monitoring Nodes Based on the Entropy Weight Method for Underground Mining Ventilation," Sustainability, MDPI, vol. 15(20), pages 1-16, October.
    • Handle: RePEc:gam:jsusta:v:15:y:2023:i:20:p:14749-:d:1257671
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/15/20/14749/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/15/20/14749/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Qiu-Ping Bi & Yu-Cheng Li & Cheng Shen, 2021. "Screening of Evaluation Index and Construction of Evaluation Index System for Mine Ventilation System," Sustainability, MDPI, vol. 13(21), pages 1-15, October.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Dalton Garcia Borges de Souza & Erivelton Antonio dos Santos & Francisco Tarcísio Alves Júnior & Mariá Cristina Vasconcelos Nascimento, 2021. "On Comparing Cross-Validated Forecasting Models with a Novel Fuzzy-TOPSIS Metric: A COVID-19 Case Study," Sustainability, MDPI, vol. 13(24), pages 1-25, December.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jsusta:v:15:y:2023:i:20:p:14749-:d:1257671. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.