Prediction model of BOF end-point P and O contents based on PCA–GA–BP neural network

Abstract Low-carbon, green and intelligent production is urgently needed in China’s iron and steel industry. Accurate prediction of liquid steel composition at the end of basic oxygen furnace (BOF) plays an important role in promoting high-quality, high-efficiency and stable production in steelmaking process. A prediction model based on the principal component analysis (PCA) – genetic algorithm (GA) – back propagation (BP) neural network is proposed for BOF end-point P and O contents of liquid steel. PCA is used to eliminate the correlation between the factors, and the obtained principal components are seen as input parameters of the BP neural network; then, GA is employed to optimize the initialized weights and thresholds of the BP neural network. The flux composition and bottom blowing are considered in the input variables. The results indicate that the prediction accuracy of the single output model is higher than that of the dual output model. The root-mean-square error of P content between predicted and actual values is 0.0015%, and that of O content is 0.0049%. Therefore, the model can provide a good reference for BOF end-point control.