Controlling the ventilation in underground mining enterprises (UME), characterized by high inertia and numerous influencing external factors, based on real-time sensor data located in mine workings and on the surface, with a high level of accuracy in regulating air supply by the main ventilation unit (MVU), is feasible only under conditions of a pre-defined sequence of control actions. This task can be classified as an approximate dynamic programming (ADP) problem, which involves synthesizing a suboptimal control function for MVU operation in a predictive modeling mode of air distribution, given a known space of possible states and the selection of the optimal control strategy that meets a specified criterion. A simulation model of a digital twin subsystem for ventilation process control is presented, using the example of two types of UME (potash mines and oil shafts), which can be used to solve ADP tasks. For predictive modeling of air distribution and determining the energy efficiency criterion of the MVU, which consumes up to half of the total electricity of the UME, the digital twin is integrated with external data, based on which energy consumption is evaluated while maintaining the required volume of supplied air. This control approach enables not only safe and energy-efficient management of the ventilation process but also participation in the planning and implementation of measures for price-dependent electricity demand management.