Hybrid intelligent control of BOF oxygen volume and coolant addition

Abstract

The control of oxygen blowing volume and coolant addition amount is very important in the production of Basic Oxygen Furnace (BOF). In this paper, for oxygen blowing volume and coolant addition amount calculation, a combination model is proposed based on information theory and artificial intelligence technology, which is composed of Case-based Reasoning (CBR) model and Support Vector Machine (SVM) model. For the former, mutual information is introduced to determine the weights of attributes and make the retrieval step more valid than traditional method. For the latter, mutual information is adopted to distinguish the importance of input variables by setting a weight for each variable. The CBR model is considered as experience-based model and the SVM model is a data-based model. To solve the control problems of oxygen blowing volume and coolant addition amount, CBR model and SVM model are combined by conditional entropy. Tests on a 180t BOF data substantiate the effectiveness of the proposed model.