Prediction of Carbon and Silicon Content in Molten Iron Based on BP Neural Networks

In order to eliminate the factitious limit on function form and improve adaptability to unstable production conditions in the traditional thermal analysis based on multiple regression,a BP neural network algorithm for thermal analysis has been built and used in predicting carbon,silicon content in molten iron.38 groups of the experimental samples were used to train the network and 8 groups are used to verify the network.The absolute errors of predicted C,Si content were 0.12% and 0.16% respectively.The results show that this method can avoid the factitious limit on modeling to improve prediction accuracy,as well as has strong learning ability and adaptability for unstable production conditions.