Modelling of blast furnace ironmaking process based on digital twin

Abstract The blast furnace ironmaking process is a crucial step in the steel industry. Effectively modelling the blast furnaces is significant in ensuring smooth operation and accelerating the digitalisation transformation of blast furnaces. The authors focus on the mechanism modelling of the blast furnace operation process, using a digital twin model development platform to simulate the main reaction processes inside the blast furnaces. Under on‐site production conditions, Si, Mn and Ti contents of molten iron and the corresponding indicators for model accuracy are calculated. For Si, Mn, and Ti content, the Root Mean Square Error values are 0.0678, 0.0108 and 0.0093 for dataset 1, while 0.0933, 0.0120 and 0.0111 for dataset 2, respectively, indicating that the model has a small simulation error and high accuracy. By comparing the simulation results with accurate laboratory results, the model is validated to have satisfactory simulation reliability and compensates for the existing shortcomings in the blast furnace mechanism modelling field.

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Publication Details

Journal
Digital twins and applications.
Published
2024-10-28
DOI
https://doi.org/10.1049/dgt2.12016
Primary Topic
Iron and Steelmaking Processes
Type
article
Field-Weighted Citation Impact
0.00

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article

Modelling of blast furnace ironmaking process based on digital twin

Yonghong Xu, Chunjie Yang, Zhe Liu
Digital twins and applications.
Iron and Steelmaking Processes
article

Modelling of blast furnace ironmaking process based on digital twin

Yonghong Xu, Chunjie Yang, Zhe Liu
article en

Abstract

Abstract The blast furnace ironmaking process is a crucial step in the steel industry. Effectively modelling the blast furnaces is significant in ensuring smooth operation and accelerating the digitalisation transformation of blast furnaces. The authors focus on the mechanism modelling of the blast furnace operation process, using a digital twin model development platform to simulate the main reaction processes inside the blast furnaces. Under on‐site production conditions, Si, Mn and Ti contents of molten iron and the corresponding indicators for model accuracy are calculated. For Si, Mn, and Ti content, the Root Mean Square Error values are 0.0678, 0.0108 and 0.0093 for dataset 1, while 0.0933, 0.0120 and 0.0111 for dataset 2, respectively, indicating that the model has a small simulation error and high accuracy. By comparing the simulation results with accurate laboratory results, the model is validated to have satisfactory simulation reliability and compensates for the existing shortcomings in the blast furnace mechanism modelling field.

Digital twins and applications.Vol. 1(2)
State Key Laboratory of Industrial Control Technology (CN), Zhejiang University of Technology (CN)
National Natural Science Foundation of China
Industry, innovation and infrastructure
Openalex Percentile: Top 81%
Iron and Steelmaking Processes
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