JACIII Vol.27 No.4 pp. 585-593
doi: 10.20965/jaciii.2023.p0585

Research Paper:

Condition Recognition Method with Information Granulation for Burden Distribution in Blast Furnace

Yuanfeng Huang*1,*2 ORCID Icon, Sheng Du*1,*2,*3 ORCID Icon, Jie Hu*1,*2,*3 ORCID Icon, Witold Pedrycz*4,*5 ORCID Icon, and Min Wu*1,*2,*3,† ORCID Icon

*1School of Automation, China University of Geosciences
No.388 Lumo Road, Hongshan District, Wuhan 430074, China

*2Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems
No.388 Lumo Road, Hongshan District, Wuhan 430074, China

*3Engineering Research Center of Intelligent Technology for Geo-Exploration, Ministry of Education
No.388 Lumo Road, Hongshan District, Wuhan 430074, China

*4Department of Electrical and Computer Engineering, University of Alberta
Edmonton, Alberta T 2, Canada

*5Systems Research Institute, Polish Academy of Sciences
Warsaw , Poland

Corresponding author

November 28, 2022
March 12, 2023
July 20, 2023
blast furnace, burden distribution, condition recognition, information granulation

The operating conditions influence the stability and consumption of a blast furnace. Recognizing these conditions makes changing the burden distribution parameters more efficient. The cooling stave temperature (CST) is a crucial state parameter that indicates the conditions of the process. Owing to the high data volume of the CST and the lack of methods for recognizing the stability of the slag crust, it is difficult for operators to recognize the conditions accurately according to the CST during the ironmaking process. Thus, in this study, a condition recognition method with information granulation for burden distribution in a blast furnace was presented. First, information granulation was employed to reduce the volume of the CST data and present it in a granular form. Then, considering the lack of a method for calculating the similarity of CST information granules, a novel fuzzy similarity calculation method was devised to calculate the membership grades of information granules belonging to different standard granules. Finally, the conditions were recognized according to the membership values. Experimental results based on industrial data demonstrated that the proposed method can be used to recognizes the conditions in the blast furnace.

Condition recognition framework with information granulation for the blast furnace

Condition recognition framework with information granulation for the blast furnace

Cite this article as:
Y. Huang, S. Du, J. Hu, W. Pedrycz, and M. Wu, “Condition Recognition Method with Information Granulation for Burden Distribution in Blast Furnace,” J. Adv. Comput. Intell. Intell. Inform., Vol.27 No.4, pp. 585-593, 2023.
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Last updated on Sep. 29, 2023