A data-driven entry temperature correction strategy for finishing rolling of hot rolling lines
During the traditional hot strip rolling production process,due to the rapid oxidation of the strip steel,the significant measurement deviation arises in the temperature measurement at finishing rolling entrances due to the obstruction and interference of the oxidized iron scale on the surface of the strip steel,which becomes an important influencing factor for the calculation and setting of dis-turbance rolling parameters and model self-learning regulation.A finishing rolling entry temperature prediction model was established based on machine learning neural networks,which integrates range analysis methods to determine data features and screens data based on mechanism and equipment conditions.The model predicts the rough rolling outlet temperature and calculates the temperature drop of the mechanism model to obtain the predicted value of rolling temperature,which aims at cor-recting the disturbance of finishing rolling entry temperature measurement caused by iron oxide scale on the surface of strip steel.Through continuous production data analysis and comparison,the temper-ature deviation decreases from±9.15℃ to±5.33℃.The model evaluation indicator R2 has been increased from 0.41 to 0.84.The average value of the finishing rolling entry temperature difference of samples with sharp pits in the strip steel temperature decreases from 48.45 to 11.02.After perform-ance evaluation,it's believed that the prediction model has high accuracy and strong generalization.
hot rolled stripfinishing rolling entry temperature correctiontemperature predictionneural network coupling
万方数据
维普
