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Multi-Objective Intelligent Decision and Linkage Control Algorithm for Mine Ventilation

Author

Listed:
  • Junqiao Li

    (College of Safety and Emergency Management Engineering, Taiyuan University of Technology, Jinzhong 030024, China)

  • Yucheng Li

    (College of Safety and Emergency Management Engineering, Taiyuan University of Technology, Jinzhong 030024, China)

  • Wei Zhang

    (College of Safety and Emergency Management Engineering, Taiyuan University of Technology, Jinzhong 030024, China)

  • Jinyang Dong

    (College of Safety and Emergency Management Engineering, Taiyuan University of Technology, Jinzhong 030024, China)

  • Yunan Cui

    (College of Safety and Emergency Management Engineering, Taiyuan University of Technology, Jinzhong 030024, China)

Abstract

A novel bare-bones particle swarm optimization (BBPSO) algorithm is proposed to realize intelligent mine ventilation decision-making and overcome the problems of low precision, low speed, and difficulty in converging on an optimal global solution. The proposed method determines the decision objective function based on the minimal power consumption and maximal air demand. Three penalty terms, namely, dynamic ventilation condition, the supplied air volume at the location where the air is required, and roadway wind speed, are established. The particle construction method of “wind resistance” instead of “wind resistance & air volume” is proposed to reduce the calculation dimension effectively. Three optimization strategies, namely the contraction factor, optimal initial value, and elastic mirror image, are proposed to avoid premature convergence of the algorithm. The application flow of intelligent decision-making in the field and the parallel computing architecture are also discussed. Five methods are used to solve the problems. The results reveal that the improved parallel BBPSO algorithm (BBPSO-Para-Improved) outperforms other algorithms in terms of convergence efficiency, convergence time, and global optimization performance and meets the requirements of large ventilation systems for achieving economic and safety targets.

Suggested Citation

  • Junqiao Li & Yucheng Li & Wei Zhang & Jinyang Dong & Yunan Cui, 2022. "Multi-Objective Intelligent Decision and Linkage Control Algorithm for Mine Ventilation," Energies, MDPI, vol. 15(21), pages 1-17, October.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:21:p:7980-:d:955004
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    References listed on IDEAS

    as
    1. Du Plessis, Gideon Edgar & Liebenberg, Leon & Mathews, Edward Henry, 2013. "The use of variable speed drives for cost-effective energy savings in South African mine cooling systems," Applied Energy, Elsevier, vol. 111(C), pages 16-27.
    2. Ghoreishi-Madiseh, Seyed Ali & Sasmito, Agus P. & Hassani, Ferri P. & Amiri, Leyla, 2017. "Performance evaluation of large scale rock-pit seasonal thermal energy storage for application in underground mine ventilation," Applied Energy, Elsevier, vol. 185(P2), pages 1940-1947.
    3. Chatterjee, Arnab & Zhang, Lijun & Xia, Xiaohua, 2015. "Optimization of mine ventilation fan speeds according to ventilation on demand and time of use tariff," Applied Energy, Elsevier, vol. 146(C), pages 65-73.
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