Abstract
This paper proposes a new method for predicting the change tendency of silicon content in hot metal based on Bayesian networks. Firstly, some important factors that affect silicon content are selected out using grey relationship analysis (GRA). Secondly, a Bayesian network (BN) model is constructed to predict silicon content in hot metal based on the causal relationship of the factors. The model shows good performance due to the high percentages of prediction hitting the target, and can help blast furnace (BF) foreman acquaint himself with the status of BF.
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