Burden Distribution Decision for Blast Furnace Based on Multiple Condition State

In the present industry, burden operation mainly relies on the experiences of the workers. So it is difficult to control burden operation. To solve these problems, this paper presents a model to adjust the burden distribution using fuzzy C-means (FCM) and wavelet analysis. First, multiple condition states of blast furnaces are analysed through data processing, the state parameters are clustered based on the similarity, and then this paper searches for the corresponding burden parameters from the history data. Finally, for different state clusters, best burden parameters are selected to adjust the conditions. Simulation results show that the burden distribution adjustments based on the state parameters clustering are efficient.