Prediction for permeability index of blast furnace based on VMD-PSO-BP model

作     者：Xiao-jie Liu Yu-jie Zhang Xin Li Zhi-feng Zhang Hong-yang Li Ran Liu Shu-jun Chen Xiao-jie Liu;Yu-jie Zhang;Xin Li;Zhi-feng Zhang;Hong-yang Li;Ran Liu;Shu-jun Chen

作者机构：School of Metallurgy and EnergyNorth China University of Science and TechnologyTangshan 063210HebeiChina Chengde BranchHBIS Group Co.Ltd.Chengde 067000HebeiChina 

出 版 物：《Journal of Iron and Steel Research International》 (Journal of Iron and Steel Research, International)

年 卷 期：2024年第31卷第3期

页      面：573-583页

核心收录：

学科分类：080602[工学-钢铁冶金] 0806[工学-冶金工程] 08[工学] 

基　　金：supports from the National Natural Science Foundation of China Youth Fund Project(52004096) 

主　　题：Big data-Blast furnace Air permeability Variational mode decomposition Particle swarm optimization Back propagation Model prediction 

摘      要：The permeability index is one of the important production indicators to monitor the operation of blast *** is crucial to grasp the trends of changes in the new permeability index in *** the complex vibration spectrum of the permeability index,a prediction model of the permeability index based on the VMD-PSO-BP(variational mode decomposition-particle swarm optimization-back propagation)method was ***,the key factors that affect the permeability index of blast furnace were studied from multiple ***,the permeability index was divided into multiple sub-modes based on the difference of frequency bands by the VMD algorithm,and a PSO-BP prediction model was established for each ***,the prediction results of each sub-mode were summed to obtain the final *** results show that the composite prediction accuracy by using the VMD algorithm is 3%higher than that of the traditional prediction method,which has better applicability.