A model of basic oxygen furnace (BOF) end-point prediction based on spectrum information of the furnace flame with support vector machine (SVM)

In the common basic oxygen furnace technique, we often get the information of furnace flame by our eyes, from which we could estimate the content of the carbon in the molten iron. In order to using an optical device to estimate the end point of BOF accurately, we design an arithmetic to predict the end point. The variety of the furnace flame essentially is the variety of the spectrum. So we proceed with the variety of spectrum distributing, distilling some parameters which could depict the variety of spectrum, combining with the support vector machine (SVM) to compose a system which could predict the end point of BOF relatively accuracy. The result of the experiment shows that the model meets the requirements of end-point judgment online. Furthermore, by discussing with the different output function of SVM, we can confirm that we can measure the content of carbon in the molten iron in real time by using this model.