Ladle Furnace Liquid Steel Temperature Prediction Model Based on Optimally Pruned Bagging

For accurately forecasting the liquid steel temperature in ladle furnace （LF）, a novel temperature predic tion model based on optimally pruned Bagging combined with modified extreme learning machine （ELM） is pro posed. By analyzing the mechanism of LF thermal system, a thermal model with partial linear structure is obtained. Subsequently, modified ELM, named as partial linear extreme learning machine （PLELM）, is developed to estimate the unknown coefficients and undefined function of the thermal model. Finally, a pruning Bagging method is pro- posed to establish the aggregated prediction model for the sake of overcoming the limitation of individual predictor and further improving the prediction performance. In the pruning procedure, AdaBoost is adopted to modify the ag- gregation order of the original Bagging ensembles, and a novel early stopping rule is designed to terminate the aggre- gation earlier. As a result, an optimal pruned Bagging ensemble is achieved, which is able to retain Bagging＇s ro- bustness against highly influential points, reduce the storage needs as well as speed up the computing time. The pro- posed prediction model is examined by practical data, and comparisons with other methods demonstrate that the new ensemble predictor can improve prediction accuracy, and is usually consisted compactly.

Development of an improved CBR model for predicting steel temperature in ladle furnace refining

In the prediction of the end-point molten steel temperature of the ladle furnace, the influence of some factors is nonlinear. The prediction accuracy will be affected by directly inputting these nonlinear factors into the data-driven model. To solve this problem, an improved case-based reasoning model based on heat transfer calculation(CBR-HTC) was established through the nonlinear processing of these factors with software Ansys. The results showed that the CBR-HTC model improves the prediction accuracy of end-point molten steel temperature by5.33% and 7.00% compared with the original CBR model and 6.66% and 5.33% compared with the back propagation neural network(BPNN)model in the ranges of [-3, 3] and [-7, 7], respectively. It was found that the mean absolute error(MAE) and root-mean-square error(RMSE)values of the CBR-HTC model are also lower. It was verified that the prediction accuracy of the data-driven model can be improved by combining the mechanism model with the data-driven model.

Final Temperature Prediction Model of Molten Steel in RH-TOP Refining Process for IF Steel Production

In order to precisely control the final temperature of molten steel in RH （Ruhrstahl Heraeus）-TOP blowing refining, the final temperature prediction models of molten steel in RH-TOP blowing refining process for Interstitial Free （IF） steel production were established under the condition of oxygen blowing and non-oxygen blowing respec- tively. The results show that the beginning molten steel temperature of refining and the amount of added scrap were influential factors, the baking temperature in vacuum chamber was a factor that had small influence. When the model was operated, the hitting probability was above 95%（under the condition of both oxygen blowing and non-oxygen blo- wing） of prediction deviation ±10℃. The accuracy is analyzed.