Deep learning-based prediction framework of temperature control time for wide-thick slab hot rolling production.

• A deep learning-based prediction framework of temperature control time is proposed. • Designing a historical rolling data processing method based on mean coding. • Designing a feature selection strategy based on maximum information coefficient. • Designing adaptive genetic algorithms to improve the deep neural network. • Experiments demonstrate the proposed prediction framework has the best performance. Accurate prediction of temperature control time is essential to improving the stability of wide-thick slab hot rolling production. However, the historical rolling data have high-dimensional and nonlinear characteristics, and there are complex relationships between the features affecting the temperature control time, which significantly impede modeling performance. To solve these problems and enhance prediction accuracy, this study proposes a prediction framework of temperature control time (PF-TCT). The proposed PF-TCT framework contains data preprocessing, feature selection, and model training. In the data preprocessing stage, considering the uneven distribution of categorical features taking values, a uniform random sampling method is designed to divide the dataset, and mean encoding is used to convert the categorical features into numerical features. In the feature selection stage, considering the interpretability of relationships between static parameters in historical rolling data and the accuracy of identifying redundant features, a selection strategy is designed based on the maximum information coefficient and approximate Markov blanket. In the model training stage, the deep neural network (DNN), which has a strong fitting ability and high flexibility, is used to build the prediction model. To avoid the dependencies between features and uneven sample distribution affecting the performance of the DNN, an adaptive genetic algorithm (AGA) is designed to optimize the DNN. In the AGA, population fitness and genetic iteration effect are considered to endow the selection, crossover, and mutation operators with adaptive learning ability, which can effectively provide good initial settings for the DNN parameters, thereby significantly improving the prediction performance of the PF-TCT. The proposed PF-TCT is verified by numerical experiments on actual data from a large steel group in China. The experimental results show that the data preprocessing and feature selection process in the PF-TCT can effectively improve the performance of the prediction model. Compared with the classical models, the AGA-based DNN proposed in this paper has lower prediction error and better stability and generalization performances. Finally, considering the maximum information coefficient matrix and approximate Markov blanket of features, the feature selection process is further analyzed, and the process knowledge for improving the hot rolling production of wide-thick slabs is discussed. [ABSTRACT FROM AUTHOR]