AP Based CBR for Endpoint Carbon Content Prediction of BOF Steelmaking

Abstract

The endpoint carbon content of steelmaking is an important criterion for steel quality. Aiming at increasing the accuracy of endpoint carbon content prediction in basic oxygen furnace (BOF) steelmaking, this paper uses case-based reasoning (CBR) method to predict the endpoint carbon content of BOF steelmaking. In CBR, case retrieval makes a significant impact on reasoning result. Therefore, we apply affinity propagation (AP) clustering algorithm and waterfilling algorithm to enhance the case retrieval so as to improve the accuracy and stability of endpoint carbon content prediction. Through the simulation experiment, this paper compares the new model we proposed with the widely used method at present. The results show that the improved CBR can obviously improve the accuracy of endpoint carbon content prediction.