Prediction of final cooling temperature for hot rolled plate based on Bayesian optimized LightGBM
Plate hot rolling is a typical process industry,which goes through continuous casting,heat-ing,descaling,rolling,cooling,coiling and other technological processes in turn.Because of the large range and fast speed of temperature change in the cooling process,the cooling process has the greatest influence on the microstructure and properties of steel plate,and the final cooling temperature is a key control parameter in the cooling process.In order to improve the accuracy of the final cooling temperature prediction,the LightGBM(light gradient boosting machine)model was used for regres-sion prediction of the final cooling temperature.The size of plate,chemical composition and upstream and downstream process parameters are used as inputs of the model,and the final cooling temperature is used as output of the model.Bayesian optimization method is used to complete the super-parameter optimization of the model.In addition,Shapley additive explanation(SHAP)method is used to test the influence of input parameters on the predicted parameters.The results show that Bayesian opti-mized LightGBM(BO-LightGBM)model achieves lower error in both training set and test set,95％of the absolute error of predicted data is controlled at±10 ℃,and the time consumption of the model is reduced by 97％compared with other ensemble learning models,the prediction accuracy and pre-diction efficiency of hot rolling process temperature of plate are improved.
万方数据
维普
