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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Bibliographic Details
Published in:International journal of minerals, metallurgy and materials Vol. 31; no. 6; pp. 1228 - 1240
Main Authors: Shi, Quan, Tang, Jue, Chu, Mansheng
Format: Journal Article
Language:English
Published: 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
Subjects:
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 Access:Get full text
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Summary: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 of data-driven and BF ironmaking processes was proposed. The data on raw and fuel materials, process operation, smelting state, and slag and iron discharge during the whole BF process comprised 171 variables with 9223 groups of data and were comprehensively analyzed. A novel method for the delay analysis of furnace heat indicators was established. The extracted delay variables were found to play an important role in modeling. The method that combined the genetic algorithm and stacking efficiently improved performance compared with the traditional machine learning algorithm in improving the hit ratio of the furnace heat prediction model. The hit ratio for predicting the temperature of hot metal in the error range of ±10°C was 92.4%, and that for the chemical heat of hot metal in the error range of ±0.1wt% was 93.3%. On the basis of the furnace heat prediction model and expert experience, a feedback model of furnace heat operation was established to obtain quantitative operation suggestions for stabilizing BF heat levels. These suggestions were highly accepted by BF operators. Finally, the comprehensive and dynamic model proposed in this work was successfully applied in a practical BF system. It improved the BF temperature level remarkably, increasing the furnace temperature stability rate from 54.9% to 84.9%. This improvement achieved considerable economic benefits.
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content type line 14
ISSN:1674-4799
1869-103X
DOI:10.1007/s12613-023-2693-7