Optimizing electrolytic process of anode copper: a mechanism-data dual-driven explainable multi-task deep learning framework

• MT-DeepChemNet-CEEMD-RRTO-CBAM was first proposed. • RMSE was 12.4% lower than other variants, and MDCI was 0.94. • CEEMD-RRTO-CBAM shortens the training time to 35.00 s. • GCN explicit modeling variable dependence enhances interpretability. • An innovative solution of mechanism fusion is provided. Complex process parameters and limited accuracy of traditional models lead to difficulties in quality control and low optimization efficiency. Therefore, a high-quality prediction and optimization method for electrolytic copper refining process is proposed. By integrating electrochemical principles (i.e., Butler-Volmer equation), graph convolution network (GCN), complementary ensemble empirical mode decomposition (CEEMD), RRT-based optimizer (RRTO) and convolutional block attention module (CBAM), the framework accurately predicts electrolytic copper quality. Compared with traditional single-task model, MT-DeepChemNet-CEEMD-RRTO-CBAM framework shows significant advantages in predicting quality indexes of electrolytic copper. Specifically, the root mean square error is 0.72, which is 19% lower than the 0.89 of the traditional single-task models, the mechanism consistency index (MDCI) reaches 0.94, which is 15% higher than the 0.82 of the traditional single-task models. In addition, the number of parameters of the framework is only 1.20 M, which is 86% less than that of the traditional single-task model, and the training time is also shortened from 217.00 s to 35.00 s, which is 83.87% less than that of the traditional single-task model. Through multi-task learning, the framework can predict multiple quality indicators simultaneously, make full use of the inherent correlation between different quality indicators, improve data utilization efficiency and reduce overfitting risk. Therefore, this work constructs an integrated modeling framework that combines electrochemical mechanism and multi-task deep learning. A high-precision prediction approach based on decomposition-optimization-attention is proposed. The multi-index collaborative quality prediction and optimization method of electrolytic copper refining process is improved.