Classification and case matching algorithm on blast furnace burden surface
CAO Ming
ZHANG Sen
YIN Yi-xin
XIAO Wen-dong
Abstract：A method is supposed in this paper based on the improved k-means algorithm and graded case based matching method to study the relationship between the burden surface and the gas flow in blast furnace. To get the gas flow distribution from historical data, first of all, an improved k-means algorithm was proposed based on a new evaluation approach of effectiveness index. Comparison with other clustering algorithms proved that the proposed algorithm has a high accuracy and high efficiency. A matching technique was put forward on the basis of the above clustering results to obtain the most matched historical burden surface. At last, compared with the improved grey similarity matching algorithm and Euclidean nearest neighbor matching algorithm, the results showed that the proposed method has higher resolution and efficiency. Matching accuracy is as high as 92.5%in the experiments which is more accurate than the other methods. The approach is more suitable for the investigation of the relationship between burden surface and gas flow so as to assist monitors of blast furnace to control burden surface.
Keywords：blast furnacesburden surfacegas flowclustering algorithmscase matching
Publication Date：2017-01-01
Online Publishing Date：2025-08-15(First online date of this platform, not the publication date of the document)
Pages：5( 408-412 )