Applying Flame Spectrum on SVC-RVM Modeling for BOF Endpoint Prediction

A new non-contact method for predicting the basic oxygen furnace(BOF) end point carbon content is proposed in this study. A model applying the flame spectrum of the converter vessel mouth is constructed to carry out the prediction. This model consists two parts, viz. a classifier based on support vector classification to classify the whole period of one BOF heat into two main phases, and a relevance vector machine working at the posterior phase to predict the carbon content. Compared with current non-contact methods of end point carbon content prediction, the proposed method can make better use of the information of the flame of the converter mouth. Simulations on industrial data show that this method yields good results on the classification as well as end point carbon content prediction.