Process metallurgy and data-driven prediction and feedback of blast furnace heat indicators

The prediction and control of furnace heat indicators are of great importance for improving the heat levels and conditions of the complex and difficult-to-operate hour-class delay blast furnace (BF) system. In this work, a prediction and feedback model of furnace heat indicators based on the fusion...

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Veröffentlicht in:	International journal of minerals, metallurgy and materials Jg. 31; H. 6; S. 1228 - 1240
Hauptverfasser:	Shi, Quan, Tang, Jue, Chu, Mansheng
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Beijing University of Science and Technology Beijing 01.06.2024 Springer Nature B.V Engineering Research Center of Frontier Technologies for Low-carbon Steelmaking (Ministry of Education),Shenyang 110819,China School of Metallurgy,Northeastern University,Shenyang 110819,China Institute for Frontier Technologies of Low-carbon Steelmaking,Northeastern University,Shenyang 110819,China
Schlagworte:	Algorithms Ceramics Characterization and Evaluation of Materials Chemistry and Materials Science Composites Corrosion and Coatings Delay Dynamic models Extractive metallurgy Feedback Genetic algorithms Glass Heat Indicators Ironmaking Machine learning Materials Science Metallic Materials Metallurgy Natural Materials Prediction models Predictions Process metallurgy Research Article Slag Surfaces and Interfaces Thin Films Tribology genetic algorithm furnace heat blast furnace prediction and feedback stacking
ISSN:	1674-4799, 1869-103X
Online-Zugang:	Volltext
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