A Rapid Prediction Method for Rotary Kiln Head Temperature Based on Residual TCN-GRU-Self Attention Integration

The temperature at the rotary kiln inlet is a core process parameter in cement clinker calcination. Its accurate prediction is crucial for ensuring production stability, optimizing energy consumption, and improving product quality. However, this temperature series exhibits complex dynamic characteristics such as multivariate strong coupling, nonlinearity, and long time delays, posing significant challenges for precise modeling. To address this, this paper proposes a novel residual TCN-GRU-SelfAttention time series prediction model. The model begins by preprocessing and standardizing industrial data to construct high-quality input features. On this basis, dilated convolutions in the Temporal Convolutional Network (TCN) are employed to effectively extract local multi-scale features, with residual connections incorporated to ensure training stability. The Gated Recurrent Unit (GRU) is utilized to capture long-term dependencies in the temperature series. Finally, a Self-Attention mechanism is introduced to dynamically evaluate and weight the importance of features at different time steps, thereby focusing on key information. Experiments based on real rotary kiln operation datasets show that, compared to baseline models such as TCN, GRU, and TCN-GRU, the proposed model achieves significant improvements in key metrics: the coefficient of determination (R²) reaches 0.88, an increase of 4.8%, while the root mean square error (RMSE) is reduced by 31.4%. The results demonstrate that the proposed model exhibits good adaptability and robustness to the complex dynamic conditions of rotary kilns, providing high-precision predictive support for fine-grained kiln control and process decision-making.