What question did this study set out to answer?

The main goal is to develop an effective model for accurately predicting temperature in aluminum smelting furnaces.

March 10, 2026

Multi‐Step Temperature Prediction for a TGAL Regenerative Aluminum Smelting Furnace

Key Points

The main goal is to develop an effective model for accurately predicting temperature in aluminum smelting furnaces.
Implement a TGAL hybrid model combining TCN, GCN, attention mechanism, and LSTM.
Apply wavelet denoising to reduce noise in industrial data.
Select correlated features using Pearson correlation coefficient.
Validate the model using over 44,000 data samples.
Evaluate performance improvement metrics like RMSE, MAE, and MAPE.
Achieved maximum improvements of 7.44% in RMSE and 24.85% in MAE for 2-step prediction.
For 10-step prediction, improvements were at least 4.23% in RMSE and 6.91% in MAE.
Statistical tests confirmed TGAL's predictive accuracy significantly exceeds that of comparison models.
Model performance remains limited under extreme conditions due to data noise and nonlinear dynamics.

Abstract

ABSTRACT This study addresses the industrial challenge that the temperature inside regenerative aluminum smelting furnaces cannot be directly or accurately measured. To overcome this issue, a TGAL hybrid model combining a Temporal Convolutional Network (TCN), Graph Convolutional Network (GCN), Multi‐Head Attention mechanism, and Long Short‐Term Memory (LSTM) network is proposed for multi‐step accurate prediction of furnace temperature. The method first applies wavelet denoising to suppress noise in the industrial data collected by the SCADA system and employs the Pearson correlation coefficient to select highly correlated features, thereby improving the quality of the input data. The proposed TGAL model exploits the synergy of TCN in capturing long‐term temporal dependencies, GCN in uncovering spatial correlations among variables, the attention mechanism in dynamically weighting features, and LSTM in temporal dynamic modeling. Validation on 44,640 one‐minute data samples from actual production shows that, compared with traditional models, the proposed model achieves maximum improvements of 7.44% in RMSE, 24.85% in MAE, and 25.27% in MAPE for 2‐step prediction, respectively. For 10‐step prediction, the improvement rates remain at least 4.23% in RMSE, 6.91% in MAE, and 6.31% in MAPE. Moreover, Diebold–Mariano statistical tests confirm that the TGAL model's predictive accuracy is significantly superior to that of the comparison models. Nevertheless, the model performance under extreme operating conditions remains limited by data noise and nonlinear dynamics, and the physical mechanisms of the smelting process have yet to be incorporated. To address these limitations, future work will focus on dynamic coupling modeling and the embedding of physical information to further enhance the model's generalization capability and physical consistency.

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