In recent years, there has been a notable integration of artificial intelligence (AI) technologies into mine ventilation systems. A mine ventilation network presents a complex system with numerous interconnected processes, some of which pose challenges for deterministic simulation methods. The utilization of machine learning techniques and evolutionary algorithms offers a promising avenue to address these complexities, resulting in enhanced monitoring and control of air parameter distribution within the ventilation network. These methods facilitate the timely identification of resistance faults and enable prompt calculation of ventilation parameters during emergency scenarios, such as underground explosions and fires. Furthermore, evolutionary algorithms play a crucial role in the advancement of methods for visual analysis of ventilation systems. However, it is essential to acknowledge that the current utilization of AI technologies in mine ventilation is limited and does not encompass the full spectrum of challenging-to-formalize problems. Promising areas for AI application include analyzing changes in air distribution caused by unaccounted thermal draft and gas pressure, as well as developing novel approaches for calculating shock losses. Moreover, the application of AI technologies in optimizing large-scale mine ventilation networks remains an unresolved issue. Addressing these challenges holds significant potential for enhancing safety and efficiency in mine ventilation systems.

A Stefan problem is analyzed for the case of rock freezing during shaft sinking. It is shown that the rate of thermal diffusion in rock mass is comparable with the velocity of the boundary between...

The mathematical methods of forecasting microclimate conditions in an arbitrary layout network of excavations are developed based on analysis of formation of thermal environment in mines. The mathematical models allow calculation of heat distribution in excavations, considering hydrostatic compression-expansion of air in vertical and inclined workings, presence of heat liberation (absorption) and moisture transitions.

This article deals with the reduction of dust content in the working areas of potash mines in the conditions of operating combines. This problem is topical, since the high dust concentrations in the atmosphere of dead-end drifts lead to the development of occupational diseases among miners, and traditional methods of dust control are often unsuitable for potash salts conditions. We describe the results of experimental studies carried out for the potash mine of the Verkhnekamskoye potassium-magnesium salt deposit. Next, a mathematical model of the dust–air mixture dynamics is formulated and parametrized. The Euler–Euler approach and the model of a homogeneous multicomponent medium are used. The results of a simulation for two ventilation systems (forced and exhaust) are presented. Several options for air flow rates are considered. A conclusion is reached about the most effective parameters of ventilation systems, which allows for minimizing the concentration of dust in the zone of the combine driver.