BOF endpoint prediction based on the flame radiation by hybrid SVC and SVR modeling

A new non-contact method for predicting the endpoint carbon content of different kinds of basic oxygen furnace (BOF) vessel sizes is presented in this paper. The proposed model is based on the spectral distribution of the flame at the vessel mouth with support vector machine (SVM). Two models are constructed with SVM in this paper: a SVC model is used to classify the whole blowing phases based on the mechanism analysis, and an ɛ-SVR model is constructed to map the flame spectrum variation into the state of the molten steel in the iron bath. The simulation results on industrial datasets show that the proposed method can offer an accurate as well as convenient way to improve the performance of BOF endpoint control compared with current methods of endpoint carbon content prediction.