The invention relates to a flotation process
automatic control method for intelligently optimizing a
reinforcement learning algorithm. In the
mineral flotation operation process, a flotation
reagent system needs to be precisely controlled, for example, the dosage of reagents such as a collecting agent, an inhibitor and a
foaming agent is adjusted, or process reaction parameters such as the pH value of ore pulp, the flotation temperature and the
reagent reaction time are adjusted, so that the flotation index reaches the best, and maximum utilization of mineral resources is achieved. Main equipment for adding flotation reagents is a variable frequency pump, the
reagent amount is adjusted through PID, meanwhile, process index data of a flotation tank are collected through a sensor, the whole
control algorithm is optimized through improved FPA
reinforcement learning, flotation process index data parameters serve as environment state observation values of
reinforcement learning intelligent agents, and the flotation process index data parameters are calculated. Meanwhile, the design environment is rewarded according to the conditions meeting the flotation requirements, and the
intelligent agent is trained through repeated environment interaction. And finally, during operation, the
intelligent agent continuously gives an optimal action value parameter of a flotation
cell actuator in real time, and unmanned reinforcement learning optimization control of
mineral flotation is achieved.