Dynamic grey model for forecasting silicon content in blast furnace hot metal

A new approach is proposed to forecast the silicon content in blast furnace (BF) hot metal based on the dynamic grey model DGM (1,1) in grey theory. The original silicon content sequence is verified through smoothness checkout and bounds checkout in order to check the application possibility of the DGM (1,1) model. After the original data sequence has been transformed by square-root transformation, the DGM (1,1) is constructed and applied to forecasting the silicon content in BF hot metal. The application results show that the prediction of BF silicon content is successful.