A novel strategy based on machine learning of selective cooling control of work roll for improvement of cold rolled strip flatness

Precise selective cooling control of work roll can significantly improve the cold rolled strip flatness in steel manufacturing industry. To improve the control accuracy of the coolant output of selective work roll cooling control system, a machine learning (ML) algorithm with differential evolution-gray wolf algorithm optimization support vector machine regression (DE-GWO-SVR) model has been proposed for the first time in this study. This model combines the differential evolution (DE) with grey wolf optimization algorithm (GWO) to improve the optimization performance of the algorithm. Then, the SVR model parameters are optimized with differential evolutionary gray wolf hybrid algorithm (DE-GWO) to improve the regression accuracy. Finally, the influences of data normalization methods and the selection of SVR kernel functions were systematically investigated. Compared with the test results of other regression models, the evaluation index R 2 based on the DE-GWO-SVR model is greater and the RMSE, MAE, and MAPE are smaller. The DE-GWO-SVR model performs the best, with a higher regression accuracy than the other regression models. Besides, it has been successfully applied to a 1450 mm five-stand industrial cold rolling mill. The model has higher control accuracy for the thermal crown of the work roll and better control effect for the flatness deviation of the strip steel. This study provides a novel strategy with a help of ML algorithm to effectively improve the flatness quality of cold rolled strips by optimizing the selective cooling control of work roll, which exhibits a great practical application potential in steel manufacturing.