Skip to main content
Disciplines
Automotive Business IT + Informatics Construction + Real Estate Electrical Engineering + Electronics Energy + Sustainability Insurance + Risk Finance + Banking Management + Leadership Marketing + Sales Mechanical Engineering + Materials
Events
DE
EN
Books
Journals
Topic Page
Marketing
Start single access now
Access for companies
EXTENDED SEARCH
Log in
Top
Journal of Iron and Steel Research International
Published in: Journal of Iron and Steel Research International 3/2024

21-10-2023 | Original Paper

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

Authors: Xiao-jie Liu, Yu-jie Zhang, Xin Li, Zhi-feng Zhang, Hong-yang Li, Ran Liu, Shu-jun Chen

Published in: Journal of Iron and Steel Research International | Issue 3/2024

Login to get access

Activate our intelligent search to find suitable subject content or patents.

search-config
loading …

Abstract

The permeability index is one of the important production indicators to monitor the operation of blast furnace. It is crucial to grasp the trends of changes in the new permeability index in time. For 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 proposed. Firstly, the key factors that affect the permeability index of blast furnace were studied from multiple perspectives. Then, 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 sub-mode. Finally, the prediction results of each sub-mode were summed to obtain the final one. The 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.
previous article Interface interaction between SiO2 and magnetite under high temperature: particle migration and inhibition mechanism
next article Heat distribution model under hydrogen-rich low-carbon conditions in blast furnace
Literature
[1]
[2]
K.W. Hsu, Y.C. Ko, in: J. Darmont, S. Loudcher (Eds.), Utilizing Big Data Paradigms for Business Intelligence, IGI Global, 2019, pp. 242–269.
[3]
[4]
H.B. Zhao, S.S. Cheng, Y.Q. Bai, J.F. Ma, L. Wan, W.P. Wang, Iron and Steel 46 (2011) No. 11, 10–15.ADS
[5]
S.X. Liu, S. Zhang, S.L. Sun, Y.X. Yin, in: 2022 37th Youth Academic Annual Conference of Chinese Association of Automation (YAC), IEEE, Beijing, China, 2022, pp. 1–6.
[6]
[7]
[8]
Y.Z. Pan, H.B. Zuo, J.S. Wang, Q.G. Xue, G. Wang, X.F. She, J. Iron Steel Res. Int. 27 (2020) 121–131.CrossRef
[9]
R. Roeplal, Y. Pang, A. Adema, J. van der Stel, D. Schott, Powder Technol. 415 (2023) 118161.CrossRef
[10]
J. Zhao, H.W. Li, X. J. Liu, X. Li, H.Y. Li, Q. Lü, China Metallurgy 31 (2021) No. 3, 22–29.
[11]
D. Liang, C.G. Bai, L.Y. Wen, F. Wang, X.W. Lv, S.F. Zhang, J. Chongqing Univ. 32 (2009) 376–380.
[12]
X.L. Su, Y.X. Yin, S. Zhang, Control Theory and Applications 33 (2016) 1674–1684.
[13]
S. Wold, J. Trygg, A. Berglund, H. Antti, Chemom. Intell. Lab. Syst. 58 (2001) 131–150.CrossRef
[14]
H. Abdi, Encyclopedia for Research Methods for the Social Sciences 6 (2003) 792–795.
[15]
[16]
G.W. Xu, M. Liu, Z.F. Jiang, W.M. Shen, C.X. Huang, IEEE Trans. Instrum. Meas. 69 (2020) 509–520.ADSCrossRef
[17]
[18]
E.A. Holm, R. Cohn, N. Gao, A.R. Kitahara, T.P. Matson, B. Lei, S.R. Yarasi, Metall. Mater. Trans. A 51 (2020) 5985–5999.CrossRef
[19]
P. Zhou, M. Yuan, H. Wang, T.Y. Chai, Math. Probl. Eng. 2015 (2015) 326160.
[20]
[21]
J. Jiang, J.X. Zhang, Computer Simulation 38 (2021) No. 7, 97–101+180.
[22]
[23]
Z.F. Zhang, X.J. Liu, X. Li, Q. Lv, S.J. Chen, R. Liu, China Metallurgy 32 (2022) No. 10, 75–81.
[24]
[25]
T. Bashir, C. Haoyong, M.F. Tahir, Z. Liqiang, Energy Rep. 8 (2022) 1678–1686.CrossRef
[26]
J.H. Zhang, Y.L. Jin, F.M. Shen, X.L. Su, J. Iron Steel Res. 19 (2007) No.11, 60–63.
[27]
K.L. Du, M.N.S. Swamy, in: K.L. Du, M.N.S. Swamy (Eds.), Search and Optimization by Metaheuristics, Birkhäuser, Cham, 2016, pp. 153–173.
[28]
S.Z. Zhao, J.J. Liang, P.N. Suganthan, M.F. Tasgetiren, in: 2008 IEEE Congress on Evolutionary Computation (IEEE World Congress on Computational Intelligence), Hong Kong, China, 2008, pp. 3845–3852.
[29]
S.Z. Zhao, P.N. Suganthan, Q.K. Pan, M. Fatih Tasgetiren, Expert Syst. Appl. 38 (2011) 3735–3742.
[30]
Y. Shi, R. Eberhart, in: 1998 IEEE International Conference on Evolutionary Computation Proceedings. IEEE World Congress on Computational Intelligence (Cat. No.98TH8360), Anchorage, AK, USA, 1998, pp. 69–73.
[31]
Y.Q. Wei, Y.S. Dai, Y.N. Zhang, J. Chen, J.J. Ding, Computer Engineering and Applications 49 (2013) No. 10, 45–49.
[32]
J.M. Jia, K.K. Chi, Z.X. Wu, Computer Engineering and Design 42 (2021) 3495–3501.
[33]
Z.J. Teng, J.L. Lv, L.W. Guo, Y.Y. Xu, J. Harbin Inst. Technol. 50 (2018) No. 11, 40–49.
[34]
L. Shan, H. Qiang, J. Li, Z.Q. Wang, Control and Decision 20 (2005) 179–182.
[35]
[36]
P. Wikström, Data mining analysis of the relationship between input variables and hot metal silicon in a blast furnace, Department of Chemical Engineering and Geosciences Division of Process Metallurgy, Lulea University of Technology, Lulea, Sweden, 2004.
[37]
[38]
[39]
L.H. Chiang, R.J. Pell, M.B. Seasholtz, J. Process Control 13 (2003) 437–449.CrossRef
[40]
Metadata
Title
Prediction for permeability index of blast furnace based on VMD–PSO–BP model
Authors
Xiao-jie Liu
Yu-jie Zhang
Xin Li
Zhi-feng Zhang
Hong-yang Li
Ran Liu
Shu-jun Chen
Publication date
21-10-2023
Publisher
Springer Nature Singapore
Published in
Journal of Iron and Steel Research International / Issue 3/2024
Print ISSN: 1006-706X
Electronic ISSN: 2210-3988
DOI
https://doi.org/10.1007/s42243-023-01097-y

Other articles of this Issue 3/2024

Journal of Iron and Steel Research International 3/2024 Go to the issue

Original Paper

Effect of Al2O3/SiO2 mass ratio and CaO content on viscosity and structure of slag for pyrometallurgical processing of spent automotive catalysts

Original Paper

Interface interaction between SiO2 and magnetite under high temperature: particle migration and inhibition mechanism

Original Paper

An analytical model of hot rolling force for a thick plate by combining globally optimal approximation yield criterion and egg-circular velocity field

Original Paper

Fabricating functionally graded Fe–Cr–Co permanent magnetic alloys via laser powder bed fusion

Original Paper

Hydrogen storage thermodynamics and kinetics of as-cast Ce–Mg–Ni-based alloy

Original Paper

Evolution of inclusions in vacuum induction melting of superalloys containing 70% return material

Premium Partners

    Image Credits