Mining Institute, Ural Branch, Russian Academy of Sciences (Perm, Russia)

L. Yu. Levin, Director, Doctor of Engineering Sciences, Corresponding Member of the Russian Academy of Sciences
S. V. Maltsev, Divisional Manager, Candidate of Engineering Sciences
A. E. Sukhanov, Junior Researcher, asukhanov@aerologist.ru
D. A. Borodavkin, Researcher, Candidate of Engineering Sciences

Implementation of ventilation-on-demand systems in underground ore mines with complex ventilation networks remains an unresolved scientific and practical problem. This is due to limitations in the applicability of basic control algorithms. Approaches that have proven effective in small-scale mines lose their validity when applied to facilities with complex, highly branched topologies. In underground ore mines of the Russian Federation, where the total length of workings can exceed hundreds of kilometers, stepwise regulation methods lead to instability of ventilation system parameters and large time delays. Thus, there is a contradiction between the requirements for responsive control and the physical constraints imposed by the high inertia of large, ramified ventilation networks. This article proposes a three-level architecture for an adaptive ventilation-on-demand control system that enables rapid and energyefficient redistribution of airflow under conditions of complex mine topology. The tools for parameter control at each level are described, and the corresponding control rules are presented. The proposed strategy is based on the integration of predictive neuralnetwork modeling with existing hardware for ventilation monitoring and control. This approach makes it possible to reduce the adverse effects of aerodynamic inertia in ore mines and to predict optimal operating modes of the ventilation system components.
The study was carried out as part of a major research project funded by the Ministry of Science and Higher Education of the Russian Federation (grant No. 075–15–2024–535).

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