Abstract
AbstractForecasting of silicon content in blast furnace (BF) hot metal has always been an important tool in the control of iron‐making process. To get an accurate prediction of silicon content is an urgent task for BF operators. The approach based on generalized autoregressive conditional heteroskedastic (GARCH) has been introduced to predict step‐ahead silicon content in BF hot metal. The algorithm has been explained in detail and simulation results have been analyzed from different criteria. It is shown that the algorithm gives good results and is helpful for practical production. Copyright © 2008 John Wiley and Sons Asia Pte Ltd and Chinese Automatic Control Society
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