Process Metallurgy and Genetic Algorithm Multi‐Objective Optimization of Blast Furnace Hearth Working State

The working state of the blast furnace (BF) hearth is vital for achieving high efficiency, quality, low fuel consumption, and extended lifespan in BF production. In this study, optimizing the BF hearth by considering three key factors is focused on: hot metal temperature (HMT), silicon content ([Si]...

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Vydané v:Steel research international Ročník 96; číslo 6
Hlavní autori: Shi, Quan, Tang, Jue, Chu, Mansheng
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Weinheim Wiley Subscription Services, Inc 01.06.2025
Predmet:
blast furnace
Energy consumption
genetic algorithm
Genetic algorithms
hearth working state
Industrial applications
multi‐objective optimization
Optimization
Pareto optimum
Prediction models
Process metallurgy
Silicon
ISSN:1611-3683, 1869-344X
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Shrnutí:The working state of the blast furnace (BF) hearth is vital for achieving high efficiency, quality, low fuel consumption, and extended lifespan in BF production. In this study, optimizing the BF hearth by considering three key factors is focused on: hot metal temperature (HMT), silicon content ([Si]), and hearth activity index (HAI). A multi‐objective and multistep optimization method, combining prediction models of HMT, [Si], and HAI with a genetic algorithm, is proposed to enhance the hearth's performance. A Pareto optimal solution set screening method is also established based on target thresholds, weights, and operation priorities, improving its applicability in industrial production. Through this model, significant improvement in hearth working state is demonstrated, verified through case analysis and industrial applications. The prediction model achieves high accuracy in forecasting HMT, [Si], and HAI, with hit rates of 92.26%, 93.45%, and 94.64%, respectively. During the application, the pass rates of these indicators increase by 8.32%, 16.67%, and 13.51%, showcasing the effectiveness of the approach in industrial settings. This study considers three key factors—hot metal temperature, silicon content, and hearth activity index—to optimize the blast furnace (BF) hearth's working state. A prediction model combined with a genetic algorithm enables multi‐objective optimization, significantly improving the BF hearth's working state.
Bibliografia:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:1611-3683
1869-344X
DOI:10.1002/srin.202300671