Aiming at the problems such as relatively simple control and intelligent detection model of hydrometallurgical equipment and weak generalization ability,an algorithm model for intelligent control and fault detection of hydrometallurgical equipment based on deep learning was proposed. Firstly, SAC deep reinforcement learning algorithm was used to perform intelligent control of hydrometallurgical equipment. The improved ARIMA algorithm is used to detect the fault of the equipment. In order to further improve the real-time performance of the algorithm, LoRA fine-tuning network is introduced to fine-tune and accelerate the model with low parameters, and LoRA fine-tuning network to fine-tune and accelerate the model with low parameters. The accuracy of the model is 93.24% and the accuracy of fault detection is 91.34%. The practical application effect is good.

real-time machine learning  /  SAC  /  ARIMA  /  LoRA