Stacked semi-supervised autoencoder-regularized RVFLNs for reliable prediction of molten iron quality in blast furnace

This paper proposes a novel stacked semi-supervised autoencoder-regularized random vector functional-link networks (RVFLNs) for reliable prediction of molten iron quality (MIQ) in blast furnace (BF) ironmaking. First, in order to accurately describe the importance of different process variables on t...

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Published in:	Neural computing & applications Vol. 37; no. 17; pp. 10425 - 10438
Main Authors:	Zhou, Ping, Zhao, Peng, Ou, Zihui, Chai, Tianyou
Format:	Journal Article
Language:	English
Published:	London Springer London 01.06.2025 Springer Nature B.V
Subjects:	Algorithms Artificial Intelligence Coal Computational Biology/Bioinformatics Computational Science and Engineering Computer Science Consumption Data Mining and Knowledge Discovery Distributed control systems High temperature Image Processing and Computer Vision Iron Iron compounds Ironmaking Neural networks Prediction models Probability and Statistics in Computer Science Process variables Regularization S.I.: Timely Advances of Deep Learning with applications and Data-Driven Modeling Special Issue on Timely Advances of Deep Learning with applications and Data-Driven Modeling Sulfur content Variables Attention mechanism Random vector functional-link networks (RVFLNs) Blast furnace (BF) ironmaking Stacked semi-supervised autoencoder Reliable prediction of molten iron quality (MIQ)
ISSN:	0941-0643, 1433-3058
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Abstract	This paper proposes a novel stacked semi-supervised autoencoder-regularized random vector functional-link networks (RVFLNs) for reliable prediction of molten iron quality (MIQ) in blast furnace (BF) ironmaking. First, in order to accurately describe the importance of different process variables on the multivariate MIQ indices, an attention mechanism based on feedforward compensation is introduced. The mechanism is embedded at the front end of the stacked semi-supervised autoencoder deep network. Secondly, to extract the deep feature information which is closely related to the prediction target from the process data, a deep network structure composed of multiple semi-supervised autoencoder models is introduced. The output of the last hidden layer is then used as the input of the subsequent prediction model. At the same time, two regularization terms L 1 and L 2 are incorporated into RVFLNs to sparse network output weights and improve the robustness of modeling, aiming to solve the multicollinearity and overfitting problems of the basic RVFLNs. Experiments using the standard dataset and actual industrial data of BF demonstrate that the proposed method has good performance.
AbstractList	This paper proposes a novel stacked semi-supervised autoencoder-regularized random vector functional-link networks (RVFLNs) for reliable prediction of molten iron quality (MIQ) in blast furnace (BF) ironmaking. First, in order to accurately describe the importance of different process variables on the multivariate MIQ indices, an attention mechanism based on feedforward compensation is introduced. The mechanism is embedded at the front end of the stacked semi-supervised autoencoder deep network. Secondly, to extract the deep feature information which is closely related to the prediction target from the process data, a deep network structure composed of multiple semi-supervised autoencoder models is introduced. The output of the last hidden layer is then used as the input of the subsequent prediction model. At the same time, two regularization terms L1 and L2 are incorporated into RVFLNs to sparse network output weights and improve the robustness of modeling, aiming to solve the multicollinearity and overfitting problems of the basic RVFLNs. Experiments using the standard dataset and actual industrial data of BF demonstrate that the proposed method has good performance. This paper proposes a novel stacked semi-supervised autoencoder-regularized random vector functional-link networks (RVFLNs) for reliable prediction of molten iron quality (MIQ) in blast furnace (BF) ironmaking. First, in order to accurately describe the importance of different process variables on the multivariate MIQ indices, an attention mechanism based on feedforward compensation is introduced. The mechanism is embedded at the front end of the stacked semi-supervised autoencoder deep network. Secondly, to extract the deep feature information which is closely related to the prediction target from the process data, a deep network structure composed of multiple semi-supervised autoencoder models is introduced. The output of the last hidden layer is then used as the input of the subsequent prediction model. At the same time, two regularization terms L 1 and L 2 are incorporated into RVFLNs to sparse network output weights and improve the robustness of modeling, aiming to solve the multicollinearity and overfitting problems of the basic RVFLNs. Experiments using the standard dataset and actual industrial data of BF demonstrate that the proposed method has good performance.
Author	Ou, Zihui Zhou, Ping Chai, Tianyou Zhao, Peng
Author_xml	– sequence: 1 givenname: Ping orcidid: 0000-0002-9398-172X surname: Zhou fullname: Zhou, Ping email: zhouping@mail.neu.edu.cn organization: State Key Laboratory of Synthetical Automation for Process Industries, Northeastern University – sequence: 2 givenname: Peng surname: Zhao fullname: Zhao, Peng organization: State Key Laboratory of Synthetical Automation for Process Industries, Northeastern University – sequence: 3 givenname: Zihui surname: Ou fullname: Ou, Zihui organization: State Key Laboratory of Synthetical Automation for Process Industries, Northeastern University – sequence: 4 givenname: Tianyou surname: Chai fullname: Chai, Tianyou organization: State Key Laboratory of Synthetical Automation for Process Industries, Northeastern University
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Keywords	Attention mechanism Random vector functional-link networks (RVFLNs) Blast furnace (BF) ironmaking Stacked semi-supervised autoencoder Reliable prediction of molten iron quality (MIQ)
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SubjectTerms	Algorithms Artificial Intelligence Coal Computational Biology/Bioinformatics Computational Science and Engineering Computer Science Consumption Data Mining and Knowledge Discovery Distributed control systems High temperature Image Processing and Computer Vision Iron Iron compounds Ironmaking Neural networks Prediction models Probability and Statistics in Computer Science Process variables Regularization S.I.: Timely Advances of Deep Learning with applications and Data-Driven Modeling Special Issue on Timely Advances of Deep Learning with applications and Data-Driven Modeling Sulfur content Variables
Title	Stacked semi-supervised autoencoder-regularized RVFLNs for reliable prediction of molten iron quality in blast furnace
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