Predicting the Endpoint Phosphorus Content of Molten Steel in BOF by Two-stage Hybrid Method

Abstract

A two-stage hybrid method is proposed to predict the phosphorus content of molten steel at the endpoint of steelmaking in BOF (Basic Oxygen Furnace). At the first clustering stage, the weighted K-means is performed to produce clusters with homogeneous data. At the second predicting stage, each fuzzy neural network is carried out on each cluster and the results from all fuzzy neural networks are combined to be the final result of the hybrid method. The hybrid method and single fuzzy neural network are compared and the results show that the hybrid method outperforms single fuzzy neural network.