Ironmaking process under artificial intelligence technology: A review

Author:

Zhou Hualun1ORCID,He Yibo1,Li Binzhao1,Song Dazhou1,Zhu Qiang2,Li Yihong1

Affiliation:

1. College of Materials Science and Engineering, Taiyuan University of Science and Technology, Taiyuan, Shanxi, China

2. Australia Institute for Innovative Materials, University of Wollongong, Wollongong, Australia

Abstract

The ironmaking process is a complex and continuous operation, which makes it difficult to collect and predict the production parameters. To address this challenge, artificial intelligence (AI) deep learning has emerged as a promising solution. The paper discusses the evolution and utilisation of AI in the metallurgical industry. The paper emphasises the implementation of AI in various ironmaking processes, including raw material selection and charging for iron production, molten iron composition prediction in blast furnace (BF), and internal operational state and fault detection in BF. Additionally, the paper predicts BF gas and explores the utilisation of automated equipment such as cooling systems. Drawing upon existing literature, this paper emphasises that deep learning has numerous advantages in the ironmaking industry, including its ability to process data quickly, strong adaptability, and high accuracy. The paper also highlights several challenges that the future development of AI in the ironmaking field may encounter. Looking ahead, the future of deep learning in ironmaking appears promising.

Funder

Innovative research projects of graduate students in Shanxi Province

the Foundation of State Key Laboratory of Advanced Metallurgy, USTB

Special Funding Projects for Local Science and Technology Development guided by the Central Committee

Fundamental Research Program of Shanxi Province

Innovation and Entrepreneurship Training Program for College Students in Shanxi Province

Publisher

SAGE Publications

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