Machine learning algorithm of blast furnace radar in strong interference environment
ZHAO Xiao-yue
HE Shu-rui
CHEN Xian-zhong
HOU Qing-wen
Abstract：The burden surface of blast furnace belongs to rough surface that composes of solid or molten mineral-gascoke mixture in high temperature.The electromagnetic reflection features include non-uniform and non-stationary gassolid fluidized material electromagnetic echoes,periodic shadowing effect caused by distributing chute,fixed interferences caused by cross temperature measurement as well as random noises caused by environmental factors such as electromagnelic radiation.In this paper,the Hilbert-Huang transform (HHT) method of instantaneous frequency analysis is used in place of the traditional fast Fourier transformation (FFT) method to process the burden surface signals extracted by frequency modulated continuous wave (FMCW) in complex environment.Combined with empirical mode decomposition,the original non-stationary signal is decomposed into several stationary intrinsic model functions.Then the method classifies and learns in accordance with the decision tree algorithm based on prior knowledge,getting various types of signal component weight and weighting and highlighting the real material electromagnetic signal.The time-frequency characteristics of the decomposed signals are obtained by Hilbert transform,which can reveal the rich smelting information contained in the fluidized burden surface.Meanwhile the algorithm can also improve the frame accuracy and stability of the burden surface imaging and provide reliable data support for the energy saving and emission reduction of the steel industry.
Keywords：blast furnace radarmachine learningC4.5 algorithmHilbert-Huang transformmarginal spectrum
Publication Date：2016-01-01
Online Publishing Date：2025-08-15(First online date of this platform, not the publication date of the document)
Pages：7( 1667-1673 )