BOF Endpoint Carbon Content Prediction based on Association Rule Case Base Maintenance Strategy

Abstract

Traditional basic oxygen furnace (BOF) prediction based on Case-based reasoning (CBR) is usually dependent on experts and experiences which will result in deviation. Aiming at attributes selection in BOF steelmaking and case base maintenance, this paper proposes using Ridge regression and Association rules to improve CBR and predicting the endpoint carbon content of BOF steelmaking. In CBR, case representation is the basic, the selected attributes in case representation play an important role but dependent on expert experiences so that this paper uses lasso regression algorithm to get the attributes. Through the simulation experiment, the results show that the improved CBR can obviously improve the accuracy of endpoint carbon content prediction.