Issue |
Metall. Res. Technol.
Volume 120, Number 1, 2023
|
|
---|---|---|
Article Number | 110 | |
Number of page(s) | 10 | |
DOI | https://doi.org/10.1051/metal/2023002 | |
Published online | 01 February 2023 |
Original Article
The quantitative model of hearth activity based on data mining and characteristics of deadman
1
Institute for Metallurgical Engineering and Technology, North China University of Science and Technology, Tangshan 063210, Hebei, China
2
College of Metallurgy and Energy, Ministry of Education Key Laboratory of Modern Metallurgy Technology, North China University of Science and Technology, Tangshan 063210, Hebei, China
3
School of Metallurgical and Ecological Engineering, University of Science and Technology Beijing, Beijing 100083, China
* e-mail: lr819@163.com
Received:
18
May
2022
Accepted:
2
December
2022
In order to obtain the quantitative model of hearth activity, the key control parameters of hearth activity were mined based on big data technology, the distribution characteristics of slag-iron-coke in deadman and the change of voidage in deadman were analyzed, and then the quantitative model of hearth activity was constructed. The results show that a temperature ratio was selected as the characterization parameter of hearth activity combined with some manifestations when the hearth activity became poor, through data sensitivity analysis. Blast speed, average particle size of coke, binary basicity of slag and Ti in iron were preliminarily selected as the key control parameters based on data correlation. The distribution of slag-iron-coke in hearth showed that the average particle size, CSR of coke and the fluidity of slag iron were very important for hearth activity. The coke particle size and the blast momentum should be given priority consideration according to the voidage of deadman in hearth. Finally, the quantitative model of hearth activity was constructed, the accuracy of the model was verified through the data of a commercial BF, so the hearth activity of any BF can be calculated by substituting the known production data of BF.
Key words: blast furnace / hearth activity / quantitative model / data mining / characteristics of deadman
© EDP Sciences, 2023
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