Model and application of hearth activity in a commercial blast furnace

Lian, Yong; Jiao, Kexin; Zhang, Jianliang; Ma, Hengbao

doi:10.1080/03019233.2021.1907957

Cited by 3 publications

(3 citation statements)

References 25 publications

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“…Finally, the weights of the base model indicators were normalized such that the sum of the weights of the base model indicators was 1. The four hearth activity base model indicators were combined into the HACI using the indicator weights, as shown in Equation (3).…”

Section: Hearth Activity Composite Index (Haci)mentioning

confidence: 99%

“…Therefore, hearth activity can be characterized by the smoothness of the liquid slag and iron flowing into the hearth and discharging freely from it. [1][2][3][4] Hearth activity plays a crucial role in achieving high efficiency, high quality, low consumption, and long life in BF production, which directly affects the running and first-generation BF lifespan. [5][6][7][8][9][10] In recent years, BF-intensified smelting has become common, and the problem of hearth activity in BFs has attracted increasing attention; however, there has been little research on the quantitative evaluation of hearth activity.…”

Section: Introductionmentioning

confidence: 99%

“…Therefore, hearth activity can be characterized by the smoothness of the liquid slag and iron flowing into the hearth and discharging freely from it. [ 1–4 ] Hearth activity plays a crucial role in achieving high efficiency, high quality, low consumption, and long life in BF production, which directly affects the running and first‐generation BF lifespan. [ 5–10 ]…”

Section: Introductionmentioning

confidence: 99%

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Evaluation, Prediction, and Feedback of Blast Furnace Hearth Activity Based on Data‐Driven Analysis and Process Metallurgy

Shi,

Tang,

Chu

2023

steel research int.

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Uniform blast furnace (BF) hearth activity is crucial for BF smooth running. For complex, difficult‐to‐control, and hour‐delay BF systems, the quantitative characterization, prediction, and adjustment of the BF hearth activity are significant in improving the furnace status. In this study, data including raw fuel, process operation, smelting state, and slag and iron discharge during the entire BF process were analyzed, with a total of 171 variables and 5033 groups of data. Based on the knowledge of BF technology, a comprehensive index of hearth activity was then proposed to quantitatively characterize and grade the activity level of the BF hearth; the rationality of this method was verified from the two aspects of furnace heat level and furnace status coincidence. Compared with the traditional single machine learning algorithm, the performance of the proposed method that combines genetic algorithm (GA) and stacking exhibited significant improvement. The hit rates for 10% and 5% errors in the prediction and estimation of hearth activity were 94.64% and 80.36%, respectively. To enhance the BF hearth activity, quantized and dynamic actions and suggestions were also simultaneously pushed, which were highly recognized by BF operators. The model of BF hearth activity was successfully applied in practical online production. During the application period, the average furnace hearth activity increased by 10% compared to the historical value, and the average daily output level of hot metal increased by 1.3%.This article is protected by copyright. All rights reserved.

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Section: Hearth Activity Composite Index (Haci)mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Evaluation, Prediction, and Feedback of Blast Furnace Hearth Activity Based on Data‐Driven Analysis and Process Metallurgy

Shi,

Tang,

Chu

2023

steel research int.

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

Shi,

Tang,

Chu

2024

steel research int.

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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.

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The quantitative model of hearth activity based on data mining and characteristics of deadman

Deng

Liu

et al. 2023

Metall. Res. Technol.

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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.

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Model and application of hearth activity in a commercial blast furnace

Cited by 3 publications

References 25 publications

Evaluation, Prediction, and Feedback of Blast Furnace Hearth Activity Based on Data‐Driven Analysis and Process Metallurgy

Evaluation, Prediction, and Feedback of Blast Furnace Hearth Activity Based on Data‐Driven Analysis and Process Metallurgy

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

The quantitative model of hearth activity based on data mining and characteristics of deadman

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