Concentrate Prediction of End-point Manganese Content for BOF Steelmaking Process Based on Principal Component Analysis (PCA)and GA-BP

• 1、State Key Laboratory of Advanced Metallurgy，University of Science and Technology Beijing，Beijing 100083，China.
• 2、Xinyu Iron & Steel Co., Ltd of Jiangxi, Xinyu,, China,338001

Abstract：In order to improve the prediction accuracy of the prediction model of end-point manganese content for BOF steelmaking process，a data processing method based on the combination of principal component analysis and BP neural network was presented by analyzing its influential factors during BOF steelmaking process. By using principal component analysis，the amount of variables which affect the end-point manganese content will be reduced. Then the principal components are employed to train the BP neural network in order to obtain the prediction model of end-point manganese content for BOF steelmaking process. In comparison with the network model which uses the original variables as the inputs to predict the end-point manganese content, the results show that GA-BP neural network prediction modeling method based on principal component analysis method has the better prediction accuracy and the better generalization performance , The hit ratio of the model is 86% when the predictive errors of the model are within ±0.025%, and the mean square error is 2.78×10-8. and this modeling method has both high efficiency in calculation.

Keywords： Iron and steel metallurgy BOF end-point manganese content BP neural network genetic algorithm PCA