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...

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