Research on an Intelligent Prediction Model for the Reduction Endpoint of Copper Smelting in Anode Furnaces Based on the Crystallization Ratio Method and Image Recognition

The anode furnace is a key piece of equipment in the copper smelting process and plays a vital role in producing high-quality copper. Accurately determining the endpoint during the anode furnace reduction process is critical to ensuring copper quality and smelting efficiency. In order to solve the problem of low accuracy in predicting the reduction endpoint, this study uses the crystallization ratio method to accurately predict the endpoint of the anode furnace reduction process. A predictive model for the endpoint of the reduction phase of the anode furnace was also developed. The results show that the average prediction error of the model in the reduction stage is 1.09%, and the endpoint prediction accuracy is 98.91%. The prediction accuracy of this model is significantly improved compared to the traditional BP neural network and GRNN methods, which effectively improves the accuracy of endpoint determination of the reduction stage, thus improving the production efficiency and quality of the anode furnace.