Research and Application of Coupled Mechanism and Data-Driven Prediction of Blast Furnace Permeability Index

Tan, Kangkang; Li, Zezheng; Han, Yang; Qi, Xiwei; Wang, Wei

doi:10.3390/app13179556

Cited by 4 publications

(1 citation statement)

References 16 publications

(28 reference statements)

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“…(4) Ensure that the air permeability index of the blast furnace is in a high range. Air permeability index reflects the movement of gas flow, but the adjustment of this parameter should match the change of blast volume 30) .…”

Section: Real-time Regulation Of Yield Of Molten Ironmentioning

confidence: 99%

Dynamic Prediction Model of Yield of Molten Iron Based on Multi-Head Attention Mechanism

Duan,

Liu,

et al. 2024

ISIJ Int.

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：Efficient matching of the preheat-numbers of the molten iron ladle with yield of molten iron in a single iron time is conducive to reducing energy waste, so it is of great economic significance to iron and steel enterprises to accurately predict yield of molten iron according to the characteristics of the per furnace. In this article, considering the variation of yield of molten iron and corresponding synergistic parameters depending on furnace conditions, we use the multi-head attention mechanism to capture the Attention-score of the feature parameters, and the weight matrix of the predictor is dynamically adjusted, so that it can assign more training weights to high-value feature parameters in real time according to the change of furnace conditions, efficiently complete the prediction of yield of molten iron. First, twenty characteristics are selected by mutual information method. Then, the influence degree of selected features on yield of molten iron is calculated in real time. Finally, stacked denoising auto encoder (SDAE) is used to train the deep neural network, and we constructed the predictor after adjusting the parameters to optimal. The test results show that the prediction accuracy of proposed model is 95% under the error range of ±50t, which is higher than traditional SVM, DAE, and SDAE model without the multi-head attention mechanism. Ultimately, we analyze and quantify the influence process of 20 factors on yield of molten iron, and develop a dynamic prediction system of yield of molten iron based on proposed model, which has good applicability for the prediction of yield of molten iron and effectively guides the transfer scheduling of molten iron ladles.Keywords：Dynamic prediction model of yield of molten iron; Multi-head attention mechanism; Stacked denoising auto encoder; Transfer scheduling of molten iron ladles;

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Section: Real-time Regulation Of Yield Of Molten Ironmentioning

confidence: 99%

Dynamic Prediction Model of Yield of Molten Iron Based on Multi-Head Attention Mechanism

Duan,

Liu,

et al. 2024

ISIJ Int.

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A hybrid prediction model of blast furnace permeability index combining least square support vector machine and artificial neural network

Yu,

Li,

Wang

et al. 2024

Ironmaking & Steelmaking: Processes, Products and Applicati

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Blast furnace (BF) permeability index is a crucial parameter that can quickly, intuitively, and comprehensively reflect the furnace condition. Accurate prediction of this index is crucial for optimising production efficiency and ensuring the stable operation of the BF. In this study, a hybrid permeability index prediction model is constructed by combining a least squares support vector machine and an artificial neural network, using the mean shift clustering algorithm (MSCA) to classify the BF conditions is applied. The results show that the MSCA algorithm shows remarkable precision in classifying the stable and unstable operating states of BF, achieving an impressive accuracy rate of 93.98%. The hybrid prediction model could accurately predict the permeability index and has a mean absolute error of 0.6877, a mean square error of 0.4721 and an R2 of 0.9215, highlighting its robust predictive performance. These findings underscore the practical significance of our model in enhancing BF operational efficiency and reliability.

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Interpretable prediction of sinter particle size distribution based on multi-task learning and bidirectional gated recurrent unit

Yang,

Fan,

Song

et al. 2024

Ironmaking & Steelmaking: Processes, Products and Applicati

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The particle size distribution of sintered ore is the key indicator reflecting the quality of sintered ore products. Aiming at the problems of low accuracy and lack of interpretability of the existing sinter ore particle size distribution prediction models, this paper proposes a multi-task learning prediction model that uses the bidirectional gated recurrent unit as an information sharing layer to predict the particle size distribution of sinter ore. Firstly, features with high correlation with different particle size ranges (<5 mm; <10 mm) are selected as model inputs by the feature selection method. Secondly, a bidirectional gated recurrent unit is used as the information-sharing layer to construct a multi-task learning model, and the information sharing between two tasks is used to obtain their coupling information to consider the correlation between different particle sizes and improve the prediction accuracy of the model. Additionally, the Shapely interpretation method is introduced to analyse the interpretability of the important features of the model to enhance the predictive model interpretability. Finally, the model proposed in this article is compared with the rest of the combined models, and the results show that the proposed model has high prediction accuracy. This method provides a new direction for the high-quality production of sintered ore.

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Research and Application of Coupled Mechanism and Data-Driven Prediction of Blast Furnace Permeability Index

Cited by 4 publications

References 16 publications

Dynamic Prediction Model of Yield of Molten Iron Based on Multi-Head Attention Mechanism

Dynamic Prediction Model of Yield of Molten Iron Based on Multi-Head Attention Mechanism

A hybrid prediction model of blast furnace permeability index combining least square support vector machine and artificial neural network

Interpretable prediction of sinter particle size distribution based on multi-task learning and bidirectional gated recurrent unit

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