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.
Muratbakeev et al. (Mon,) studied this question.