Endpoint Prediction of BOF Steelmaking based on BP Neural Network Combined with Improved PSO

Abstract

This paper concerns the endpoint estimation of the basic oxygen furnace (BOF) steel making process. More specifically, a back propagation (BP) neural network is employed to estimate the endpoint carbon content and the endpoint temperature of BOF, and an improved particle swarm optimization (PSO) algorithm is proposed to optimize the prediction model for improving the accuracy of the endpoint prediction. Simulation examples demonstrate the effectiveness of the proposed method.