Intelligent Mine Ventilation Systems

The article discusses the development and design of intelligent mine ventilation systems, which are an important part of the construction and modernization of modern mines, as well as key technical support for the implementation of intelligent mining technologies. A high-fidelity simulation environment is constructed using neural networks based on field data. In the process of mine ventilation control, due to the complexity of the tunnel environment, it is difficult to investigate the implicit relationship between the ventilation system outlet control parameters and the concentration of gas and dust, oxygen, and carbon dioxide in the tunnel. Hence, it is difficult to obtain an overall control strategy based on experience. This article explores the possibility of applying reinforcement learning to intelligent shaft ventilation systems, a dynamic process in which model parameters, such as strategies and value functions, are updated through continuous interaction with the environment.

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Publication Details

Journal
Symmetry
Published
2026-02-09
DOI
https://doi.org/10.3390/sym18020311
Primary Topic
Coal Properties and Utilization
Type
article
Field-Weighted Citation Impact
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article

Intelligent Mine Ventilation Systems

Eduard Muratbakeev, V. V. Potekhin, Yuriy Kozhubaev, Hua Cheng et al.
Symmetry
Coal Properties and Utilization
article

Intelligent Mine Ventilation Systems

Eduard Muratbakeev, V. V. Potekhin, Yuriy Kozhubaev, Hua Cheng, Roman Ershov
article en

Abstract

The article discusses the development and design of intelligent mine ventilation systems, which are an important part of the construction and modernization of modern mines, as well as key technical support for the implementation of intelligent mining technologies. A high-fidelity simulation environment is constructed using neural networks based on field data. In the process of mine ventilation control, due to the complexity of the tunnel environment, it is difficult to investigate the implicit relationship between the ventilation system outlet control parameters and the concentration of gas and dust, oxygen, and carbon dioxide in the tunnel. Hence, it is difficult to obtain an overall control strategy based on experience. This article explores the possibility of applying reinforcement learning to intelligent shaft ventilation systems, a dynamic process in which model parameters, such as strategies and value functions, are updated through continuous interaction with the environment.

SymmetryVol. 18(2)
Peter the Great St. Petersburg Polytechnic University (RU), Saint Petersburg Mining University (RU)
Openalex Percentile: Top 63%
Coal Properties and Utilization
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Intelligent Mine Ventilation Systems — Eduard Muratbakeev, V. V. Potekhin, et al. · Symmetry (2026) | TGRS Research Map | TGRS