Technologies

Using AI+big data technology to optimize and control the overall production of electrolytic copper, intelligent control of the five core process parameters of electrolyte temperature, composition, additives, current, and residual electrode rate is achieved through deep learning algorithms, completing the transformation of production decision-making from operational experience to model driven, achieving continuous reduction of copper consumption per ton and stable improvement of cathode copper quality.

From engineer experience driven to data model driven. The intelligent control system for polycrystalline silicon reduction furnace adopts the "; Data+Model+Application; The framework integrates machine learning, reinforcement learning, deep learning, and advanced control technologies. Through the collaborative operation of multi-objective optimization evaluation models, static models, and dynamic models, the four key indicators of power consumption, density rate, deposition rate, and primary conversion rate are comprehensively optimized to effectively reduce production costs while ensuring product quality.