Key issues and progress of industrial big data-based intelligent blast furnace ironmaking technology

Quan Shi; Jue Tang; Mansheng Chu

doi:10.1007/s12613-023-2636-3

Volume 30 Issue 9

Sep. 2023

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Article Navigation > International Journal of Minerals, Metallurgy and Materials > 2023 > 30(9): 1651-1666

Quan Shi, Jue Tang, and Mansheng Chu, Key issues and progress of industrial big data-based intelligent blast furnace ironmaking technology, Int. J. Miner. Metall. Mater., 30(2023), No. 9, pp. 1651-1666. https://doi.org/10.1007/s12613-023-2636-3

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Quan Shi, Jue Tang, and Mansheng Chu, Key issues and progress of industrial big data-based intelligent blast furnace ironmaking technology, Int. J. Miner. Metall. Mater., 30(2023), No. 9, pp. 1651-1666. https://doi.org/10.1007/s12613-023-2636-3

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Invited Review

Key issues and progress of industrial big data-based intelligent blast furnace ironmaking technology

+ Author Affiliations

Corresponding authors:
Jue Tang E-mail: tangj@smm.neu.edu.cn

Mansheng Chu E-mail: chums@smm.neu.edu.cn
Received: 15 November 2022; Revised: 6 February 2023; Accepted: 29 March 2023; Available online: 30 March 2023

Abstract

Abstract

Blast furnace (BF) ironmaking is the most typical “black box” process, and its complexity and uncertainty bring forth great challenges for furnace condition judgment and BF operation. Rich data resources for BF ironmaking are available, and the rapid development of data science and intelligent technology will provide an effective means to solve the uncertainty problem in the BF ironmaking process. This work focused on the application of artificial intelligence technology in BF ironmaking. The current intelligent BF ironmaking technology was summarized and analyzed from five aspects. These aspects include BF data management, the analyses of time delay and correlation, the prediction of BF key variables, the evaluation of BF status, and the multi-objective intelligent optimization of BF operations. Solutions and suggestions were offered for the problems in the current progress, and some outlooks for future prospects and technological breakthroughs were added. To effectively improve the BF data quality, we comprehensively considered the data problems and the characteristics of algorithms and selected the data processing method scientifically. For analyzing important BF characteristics, the effect of the delay was eliminated to ensure an accurate logical relationship between the BF parameters and economic indicators. As for BF parameter prediction and BF status evaluation, a BF intelligence model that integrates data information and process mechanism was built to effectively achieve the accurate prediction of BF key indexes and the scientific evaluation of BF status. During the optimization of BF parameters, low risk, low cost, and high return were used as the optimization criteria, and while pursuing the optimization effect, the feasibility and site operation cost were considered comprehensively. This work will help increase the process operator’s overall awareness and understanding of intelligent BF technology. Additionally, combining big data technology with the process will improve the practicality of data models in actual production and promote the application of intelligent technology in BF ironmaking.
- BF ironmaking;
- intelligent BF;
- industrial big data;
- machine learning;
- integrated mechanism and data

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