Prediction model of improved multi-layer extreme learning machine for permeability index of blast furnace
SU Xiao-li
YIN Yi-xin
ZHANG Sen
Abstract：Permeability index of blast furnace is one of significant indicators of measuring the anterograde state of blast furnace for operators.Aiming at the defects of traditional permeability index measurement model,this paper proposes a prediction model for permeability index based on improved multi-layer extreme learning machine algorithm (ML-ELM).Firstly,relevant operation parameters are chosen through analyzing the mechanism of blast furnace.Given to blast furnace production data contain noise,wavelet transform is adopted to get rid of interference.Secondly,the prediction model of permeability index is established.Multi-layer extreme learning machine and partial least square method (PLS) are combined to overcome output matrix multicollinearity of the last hidden layer for ML-ELM and prediction accuracy is improved.And the improved algorithm is named as PLS-ML-ELM.Finally,practical production data are used to train and test this model.Simulation results indicate that the model can quickly and accurately predict permeability index and can offer efficient decision for sequent blast furnace operation.
Keywords：blast furnacepermeability indexmodelmulti-layer extreme learning machineprediction
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：11( 1674-1684 )