Prediction of end-point carbon content and temperature in AOD based on Stacking model fusion
An end-point carbon content and temperature prediction method was proposed based on Stacking model fusion embedded in six machine learning algorithms(RF、XGBoost、AdaBoost、KNN、SVR and Ridge Regression),so as to improve the prediction accuracy and reliability of end-point carbon content and temperature in AOD.The input variables of the model were determined through fundamental theory related to AOD steelmaking and correlation analysis.The box plot method was used to preprocess the historical data,and the optimal model parameters were determined by combining the 5-fold cross-validation and Bayesian optimization algorithm.Consequently,a two-layer Stacking model(RF+XGBoost+KNN—RF)of end-point carbon content prediction and a three-layer Stacking model(RF+AdaBoost+KNN—RF+XGBoost+KNN—XGBoost)of end-point temperature prediction were established through screening the six machine learning algorithms.The prediction results show that the prediction accuracy of the RF+XGBoost+KNN—RF model is 87.86％within the carbon mass fraction error of±0.01％,and of the RF+AdaBoost+KNN—RF+XGBoost+KNN—XGBoost is 94.22％within the temperature error of±15 ℃,which are significantly improved compared with the single machine learning model.
AODendpoint prediction of carbon content and temperaturemodel fusionStacking model
万方数据
维普
