A Multi-Step Furnace Temperature Prediction Model for Regenerative Aluminum Smelting Based on Reversible Instance Normalization-Convolutional Neural Network-Transformer

In the regenerative aluminum smelting process, the furnace temperature is critical for the quality and energy consumption of the product. However, the process requires protective sensors, making real-time furnace temperature measurement costly, while the strong nonlinearity and distribution drift of the process data affect furnace temperature prediction. To handle these issues, a multi-step prediction model for furnace temperature that incorporates reversible instance normalization (RevIN), convolutional neural network (CNN), and Transformer is proposed. First, the self-attention mechanism of the Transformer is combined with CNN to extract global and local information in the furnace temperature data, thus addressing the strong nonlinear characteristics of the furnace temperature. Second, RevIN with learnable affine transformation is utilized to address the distribution drift in the furnace temperature data. Third, the temporal correlation of the prediction model is enhanced by a time-coding method. The experimental results show that the proposed model demonstrates higher prediction accuracy for furnace temperature at different prediction steps in the regenerative aluminum smelting process compared to other models.