高炉料面特征提取与聚类分析

Feature extraction and clustering of blast furnace burden surface

布料是高炉上部调节的主要方式,料面形状特征是指导高炉工长做出下一次布料决策的重要依据.本文通过分析高炉雷达实测料面数据,结合专家经验,提出了一种料面特征定义和提取的方法,提取出了能够表征料面形状的6个特征;然后将谱聚类算法引入到料面特征数据的聚类中,建立了料面特征模型库;最后将新的料面特征与模型库数据匹配,为后续高炉布料控制奠定研究基础.在仿真结果中采用真实高炉生产数据,验证了料面特征提取方法的有效性.仿真结果表明谱聚类算法相比传统的K-mean和模糊C均值聚类算法,具有收敛速度快,聚类性能高等特点,能够准确有效地建立料面特征模型库.

Burden distribution is the main way to adjust the upper part of the blast furnace.Shape features of the burdensurface are the important basis for guiding the blast furnace foreman to make the next burden distribution decision.In thispaper,a new burden surface definition and extraction approach is proposed by analyzing the measured surface radars dataand combining with expert experience,six features are extracted to characterize the shape of burden surface.The spectralclustering algorithm is then utilized to the extracted feature clustering problem in order to set up the standard feature modeldatabase of the burden surface.Finally,the new burden surface features data will be matched with the samples in thehistory 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 theordinary K-mean and fuzzy C-mean clustering algorithms,the spectral clustering algorithm presents better performancewith relatively faster convergent speed,which is more efficient and accurate to establish the history model library for theburden line.