Prediction model of BOF end-point temperature and carbon content based on PCA-GA-BP neural network

Abstract

Accurate prediction of temperature and carbon content of liquid steel plays an important role in steelmaking process. In order to enhance the accuracy of predicting the basic oxygen furnace (BOF) end-point temperature and carbon content of liquid steel, a hybrid model based on principal component analysis (PCA) − genetic algorithm (GA) − backpropagation (BP) neural network is proposed. PCA is used to reduce the dimensionality of the input variables and eliminate the collinearity among the variables, then the obtained principal components are seen as new input variables of the BP neural network. GA is employed to optimize the initialized weights and thresholds of the BP neural network. Data from a 250t BOF of H steel plant in China is used to test and validate the model. The results show that the prediction accuracy of the single output models is higher than that of the dual output models. The PCA-GA-BP neural network model with single output shows higher prediction performance than others. The root mean square error of temperature between predicted and actual values is 7.89, and that of carbon content is 0.0030. Therefore, the model can provide a good reference for BOF end-point control.