Feature extraction and clustering of blast furnace burden surface
ZHANG Hai-gang
ZHANG Sen
YIN Yi-xin
ZHANG Xiao-juan
Abstract：Burden distribution is the main way to adjust the upper part of the blast furnace. Shape features of the burden surface are the important basis for guiding the blast furnace foreman to make the next burden distribution decision. In this paper, a new burden surface definition and extraction approach is proposed by analyzing the measured surface radars data and combining with expert experience, six features are extracted to characterize the shape of burden surface. The spectral clustering algorithm is then utilized to the extracted feature clustering problem in order to set up the standard feature model database of the burden surface. Finally, the new burden surface features data will be matched with the samples in the history model library. This work will lay the foundation for further study of the blast furnace burden distribution control. The simulation results show that this feature extraction method and the clustering algorithm is effective. Compared with the ordinary K-mean and fuzzy C-mean clustering algorithms, the spectral clustering algorithm presents better performance with relatively faster convergent speed, which is more efficient and accurate to establish the history model library for the burden line.
Keywords：blast furnaceburden surfacefeature extractionspectral clusteringfeature 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：9( 938-946 )