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Binary Coding SVMs for the Multiclass Problem of Blast Furnace System

It poses a great challenge to control the blast furnace system, often meaning to control the components of the hot metal within acceptable boundary, such as the silicon content in hot metal. For this reason, this paper focuses on addressing the multiclass classification problem about the silicon change in hope of providing reasonable blast furnace control guidance. Through the proposed binary coding support vector machine (SVM) algorithm, a four-class problem, i.e., sharp descent, slight descent, sharp ascent, and slight ascent of the silicon content in hot metal, is reduced into two binary classification problems to solve. To heel, the confidence level about these classification results is also estimated. Reliable classification effect plus very few binary classifiers make the binary coding SVMs full of competitive power for practical applications, particularly when the confidence level is high. The four-class classification results can indicate not only the silicon change direction but also the rough silicon change amplitude, which can guide the blast furnace operators to determine the blast furnace control span together with the control direction in advance.