Improvement of the Blast Furnace Viscosity Prediction Model Based on Discrete Points Data

Viscosity is considered to be a significant indicator of the metallurgical property of blast furnace slag. An improved model for viscosity prediction based on the Chou model was presented in this article. The updated model has optimized the selection strategy of distance algorithm and negative weigh...

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Veröffentlicht in:Metallurgical and materials transactions. B, Process metallurgy and materials processing science Jg. 46; H. 1; S. 378 - 387
Hauptverfasser: Guo, Hongwei, Zhu, Mengyi, Li, Xinyu, Guo, Jian, Du, Shen, Zhang, Jianliang
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Boston Springer US 01.02.2015
Springer Nature B.V
Schlagworte:
Algorithms
Characterization and Evaluation of Materials
Chemistry and Materials Science
Iron and steel plants
Materials Science
Materials selection
Mathematical models
Metallic Materials
Metallurgy
Nanotechnology
Process metallurgy
Slag
Standard deviation
Structural Materials
Surfaces and Interfaces
Thin Films
Viscosity
Blast Furnace Slag
Negative Weight
Blast Furnace
Viscosity Data
Target Point
ISSN:1073-5615, 1543-1916
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Zusammenfassung:Viscosity is considered to be a significant indicator of the metallurgical property of blast furnace slag. An improved model for viscosity prediction based on the Chou model was presented in this article. The updated model has optimized the selection strategy of distance algorithm and negative weights at the reference points. Therefore, the extensionality prediction disadvantage in the original model was ameliorated by this approach. The model prediction was compared with viscosity data of slags of compositions typical to BF operations obtained from a domestic steel plant. The results show that the approach can predict the viscosity with average error of 9.23 pct and mean standard deviation of 0.046 Pa s.
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ISSN:1073-5615
1543-1916
DOI:10.1007/s11663-014-0176-y