Application of the fuzzy C-means clustering algorithm in blast furnace burden surface identification

Blast furnace burden surface data derived from multi radars were processed. Fuzzy C-means and feature weighted fuzzy C-means clustering were applied to identify the burden surface data according to the data information, and a standard burden surface model database was set up. Each target burden surface was matched with the model database by using the method of nearness in fuzzy pattern recognition, and this provides a basis for the next burden surface control. The algorithm was carried out into a 2 500 m³ blast furnace, and the control effect has been improved. The simulation results show the effectiveness of the proposed method.