Prediction of Coiling Temperature for Hot-Rolled Strip Steel Based on WOA-CNN-GRU-SE Model
Coiling temperature is a pivotal process parameter for hot-rolled strip steel, which directly determines the microstructure and mechanical properties of final products. Affected by the coupling of multiple process variables, coiling temperature presents strong nonlinearity and complex time-varying characteristics. Traditional heat transfer mechanism models, Random Forest (RF), Extreme Learning Machine (ELM) and single Long Short-Term Memory (LSTM) networks fail to fully explore the deep correlation among variables. In addition, their hyperparameters are generally selected by manual trial-and-error, leading to unsatisfactory prediction accuracy and poor robustness in practical production. To address the above limitations, this paper proposes a novel prediction model named WOA-CNN-GRU-SE, where the Whale Optimization Algorithm (WOA) is adopted for parameter optimization. Firstly, Convolutional Neural Network (CNN) is utilized to extract local coupling features from various working condition parameters. Secondly, the Squeeze-and-Excitation (SE) attention mechanism is applied to adaptively recalibrate channel weights, which enhances key features closely related to temperature variation and suppresses redundant interference information. Afterwards, Gated Recurrent Unit (GRU) is employed to conduct in-depth learning of temporal features. Furthermore, WOA is used to globally optimize critical hyperparameters, including learning rate, the number of GRU hidden units and L2 regularization coefficient, so as to eliminate the drawbacks of manual parameter tuning. Comparative experiments are conducted on actual production data from a hot rolling line. The results demonstrate that the proposed model outperforms CNN-GRU, CNN-GRU-SE, LSTM, RF and ELM in prediction performance. Its hit rate reaches 92.56% within the industrial error range of ±6 °C. This model effectively realizes accurate prediction of coiling temperature under complex working conditions and possesses great application potential in industrial practice.