Volume 48 Issue 11
Nov.  2022
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REN Zihui, LI Ang, WU Xinzhong, et al. Research on intelligent control of air volume of mine ventilation network[J]. Journal of Mine Automation，2022，48(11)：110-118. DOI: 10.13272/j.issn.1671-251x.2022040020
Citation: REN Zihui, LI Ang, WU Xinzhong, et al. Research on intelligent control of air volume of mine ventilation network[J]. Journal of Mine Automation，2022，48(11)：110-118. DOI: 10.13272/j.issn.1671-251x.2022040020
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Research on intelligent control of air volume of mine ventilation network

  • School of Information and Control Engineering, China University of Mining and Technology, Xuzhou 221116, China

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  • Received Date: April 08, 2022
  • Revised Date: November 05, 2022
  • Available Online: August 11, 2022
    Graphical Abstract
    • Abstract
  • The existing intelligent optimization algorithm of air volume of mine ventilation network has the defects of complex model, slow convergence speed, easy falling into local optimum when solving the air adjustment parameters. There is a lack of research on the combination of optimal selection of air adjustment branches. To solve the above problems, an intelligent control method of air volume of mine ventilation network based on improved beetle antennae search (BAS) algorithm is proposed. Firstly, the mathematical model of air volume optimal adjustment is established by taking the air volume demand of the air consumption branch as the optimization objective. In view of the air volume adjustment constraint conditions in the model, the non-differentiable exact penalty function and the simulated annealing algorithm are adopted to optimize the penalty term, so that the model is unconstrained. Secondly, by solving the sensitivity matrix and combining the theory of air volume sensitivity and branch dominance, the optimal adjustment branch set is selected. The air resistance adjustment range is determined as the initial solution set of the model. Finally, based on the improved BAS algorithm, the optimal air adjustment parameters are solved. The corresponding air adjustment facilities are controlled to realize air volume adjustment. The reliability of the method is verified by experiments based on the mine ventilation experimental platform. The results show that compared with the standard BAS algorithm and particle swarm optimization (PSO) algorithm, the improved BAS algorithm has superior comprehensive optimization performance. The average value and optimal solution of air volume are higher than those of the PSO algorithm and standard BAS algorithm. Although the average running time is slightly longer than the standard BAS algorithm, it is far shorter than the PSO algorithm. The average convergence algebra is the most, the precision is the highest, and it is easy to jump out of the local loop to get the optimal solution. After setting the air volume adjustment target, the intelligent control method of air volume of the mine ventilation network based on the improved BAS algorithm can quickly and accurately solve the optimal value of the air volume of the branch to be adjusted. The adjusted branch air volume meets the air volume adjustment requirements of mine safety production, and the air volume is increased up to 46.5%.
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    • References (16)
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