An economic benefit optimization model for hydrometallurgical equipment control was established, and an optimization algorithm based on Double Deep Deterministic Q-Network (DDQN) model is introduced. At the same time, Residual Network is combined with residual network. ResNet's deep learning capability to realize the detection and early warning of abnormal equipment operation status. The simulation results show that the intelligent control algorithm can not only greatly improve the operating efficiency of hydrometallurgical equipment, but also enhance the stability and reliability of the system and improve the economic benefit of enterprises.

hydrometallurgy  /  equipment  /  intelligent control  /  DDQN  /  ResNet  /  simulation analysis