Research On The Methods Of Molten Temperature Prediction In LF

Secondary refining has become a hot research topic in steelmaking fields at home and abroad. At present, Ladle furnace (LF) is applied widely in the iron and steel industry. LF refining technology plays a substantial role in secondary metallurgic process. The main purpose of ladle furnace treatment is to ensure that the molten steel has the required temperature when the ladle is taken over at downstream secondary metallurgy units or at a continuous caster. Therefore, the temperature control of LF is essential for improving both productivity and product quality. The temperature prediction is a key for the temperature control of LF successfully. Based on the 300t LF in Baoshan Iron & Steel Co. Ltd., the new hybrid modeling method for molten steel temperature prediction in LF is proposed by combining mechanism and intelligent methods, aiming at the shortages of single mechanism and intelligent models. In order to enhance the performance of prediction model, ensemble algorithm is used during the modeling process.The energy conservation is analyzed during the process of LF refining. The mechanism model based on energy balance and the intelligent model based on ELM are established for predicting the molten steel temperature. These models lay a foundation for following hybrid model of molten steel temperature prediction in LF.Aiming at the disadvantages of using single mechanism or intelligent model, a hybrid model for predicting the molten steel temperature in LF is proposed, in which mechanism method and intelligent method are combined. The intelligent method is used to predict the parameters of mechanism model that are calculated hardly. And then, the mechanism model is used to predict the temperature of molten steel. The modified AdaBoost.RT is presented in intellige0t model to enhance the accuracy of single ELM.Focusing on parallel modeling method, a multi-model hybrid modeling method based on Bagging for molten steel temperature prediction is proposed. In this method, mechanism models and intelligent models are used together as weak learners. They are aggregated to establish the prediction hybrid model. Additionally, aiming at the problem of using Bagging algorithm, PCA is used for sub training data set that has been sampled by Bootstrap. By using PCA, the diversity of data set and the accuracy of every weak learner are all considered carefully.Aiming at the shortages of existing updating method for soft sensor model, such as wasting time, restricting the performance of intelligent algorithm, a new updating method for soft sensor model based on incremental learning is proposed. In the new method, the new data is used to train new model firstly. And then the new model is combined with the old models according to their weights, and the update for whole model is completed. The new updating method using the past training results, and need not the old data any more. So it is benefit to save space and training time by using the new updating method.During above processes of modeling and updating, the training data sets are supposed as the data sets without gross error. Actually the gross errors are inevitable during the process of data acquisition. Therefore a conclusion can be drawn that it is necessary to detect the gross errors of modeling data. A new clustering method is proposed for detecting the gross errors in order to ensure the accuracy of soft sensor model. Considering over-detection will exist during the process of detecting, the new method is combined with the modeling. The modeling errors are used to supervise the gross error detection. This new method can detect the gross errors effectively and obtain a good soft sensor model.