Improved CBR for Endpoint Carbon Content Prediction of BOF Steelmaking

Traditional basic oxygen furnace (BOF) prediction is usually dependent on experts and experiences which will result in deviation. Aiming at increasing the accuracy of endpoint carbon content prediction in BOF steelmaking, this paper proposes a data driven way called case-based reasoning (CBR) method to predict 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. Case retrieval makes a significant impact on reasoning result. Therefore, we apply affinity propagation (AP) clustering algorithm and water-filling algorithm to enhance the case retrieval so as to improve the accuracy and stability of endpoint carbon content prediction. Through the simulation experiment, the results show that the improved CBR can obviously improve the accuracy of endpoint carbon content prediction.