A comprehensive evaluation model for the start-up and shutdown decision-making of the milling system

taking into account the energy consumption and tracking performance of the unit load

has been proposed to address issues such as subjective decision-making based on manual experience

high labor intensity in operation

and difficulty in exploring energy-saving optimization potential. This model safely incorporates the grid load scheduling command signal as input. Furthermore

a milling system start-stop intelligent decision-making method based on deep reinforcement learning has been studied

and a closed-loop control system for the automatic start-stop of the milling system has been developed. The research results have been verified through simulation and successfully applied to a commonly used coal milling system in a certain ultra-supercritical 1 000 MW unit

achieving energy savings. The findings of this study can provide effective reference for the development of unmanned or minimally manned operation techniques for thermal power units.