Prediction on finish rolling temperature for hot-rolled strip based on random forest
The complex boundary conditions and difficulty in parameter prediction in the precision rolling zone limit the accuracy of tradi-tional online hot rolling temperature prediction for strip steel.Therefore,in order to improve the accuracy of final rolling temperature predic-tion,a data-driven modeling approach using random forest was adopted,and taking forty-three characteristic factors that affect the final roll-ing temperature as the input values for the data-driven final rolling temperature prediction model.Then,the imbalanced datasets such as changing specifications were processed by a hybrid algorithm of NCL and SMOTE,and the random feature selection of the decision tree in-cluded features that were highly or lowly correlated with the target variable.The results show that the constructed random forest prediction model for the final rolling temperature of hot-rolled strip has a maximum prediction value error of within 15℃on the test set and good regres-sion effect and generalization ability,meeting the accuracy requirements for the final rolling temperature prediction of hot-rolled strip on site.
hot rollingfinish rolling temperaturerandom forestdata-driven modelnon-equilibrium dataset
NETL
NSTL
万方数据
