Research Status and Application Analysis of Intelligent Prediction for Key Blast Furnace Parameters

DOI: https://doi.org/10.62517/jiem.202503407

Author(s)

Yifan Huang1, Jiazhe Ji2, Shengle Li1, Yeqing Zhu3, Jing Dong3,*

Affiliation(s)

1College of Metallurgy and Energy, North China University of Science and Technology, Tangshan, Hebei, China 2College of Artificial Intelligence, North China University of Science and Technology, Tangshan, Hebei, China 3College of Science, North China University of Science and Technology, Tangshan, Hebei, China *Corresponding Author

Abstract

Blast furnace ironmaking is a central process in the steel industry, where key operational parameters critically influence efficiency and product quality. Traditional empirical methods often fail to address the complexity and variability of smelting, underscoring the need for intelligent prediction to improve operational stability. This study reviews current research and industrial applications of intelligent prediction for major blast furnace parameters, analysing control mechanisms and quantitative characterisation methods for blast volume, blast temperature, oxygen enrichment, pulverised coal injection, and blast kinetic energy. It further examines the functional roles and mechanistic relationships of process state variables, such as pressure differential, permeability, and gas utilisation, and quality indicators including molten iron silicon content and temperature. Finally, based on typical applications involving multi-source data fusion, deep learning, and ensemble modelling, the study outlines advanced frameworks such as multi-objective optimisation and digital twins, offering practical strategies for cost reduction, efficiency improvement, and sustainable development in the steel industry.

Keywords

Blast Furnace Process; Metallurgical Mechanisms; Data-Driven; Intelligent Prediction; Model Integration

References

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