Classification of silicon content variation trend based on fusion of multilevel features in blast furnace ironmaking
•A data-driven model is proposed based on multilevel features fusion to classify the variation trend of silicon content.•Multilevel features are extracted through variable analysis, statistical information, and stacked denoising autoencoder, respectively.•Silicon content trend labels are quantitativ...
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| Vydáno v: | Information sciences Ročník 521; s. 32 - 45 |
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01.06.2020
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| ISSN: | 0020-0255, 1872-6291 |
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| Abstract | •A data-driven model is proposed based on multilevel features fusion to classify the variation trend of silicon content.•Multilevel features are extracted through variable analysis, statistical information, and stacked denoising autoencoder, respectively.•Silicon content trend labels are quantitative descripted by seven different primitives.•Both experimental simulation and industrial application verify the effectiveness and feasibility of the proposed model.
The silicon content variation trend, which can reflect the quality of molten iron, provides significant information that can assist in ensuring the smooth operation of a blast furnace. This paper proposes a novel dynamic data-driven model for the online classification of the variation trend for the silicon content. Typically, a dynamic model for the silicon content variation trend primarily relies on process data feature extraction. First, a multilevel features fusion algorithm based on mutual information is developed to extract a rich and robust feature representation. Subsequently, the fused multilevel feature vectors and their corresponding trend labels are fed into a recurrent neural network model to capture the process dynamics and classify the variation trend. An experimental simulation and industrial application verified the effectiveness and feasibility of the proposed method. The classification results can provide guidance to ensure that the quality of molten iron is maintained within the desired range in the ironmaking process. |
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| AbstractList | •A data-driven model is proposed based on multilevel features fusion to classify the variation trend of silicon content.•Multilevel features are extracted through variable analysis, statistical information, and stacked denoising autoencoder, respectively.•Silicon content trend labels are quantitative descripted by seven different primitives.•Both experimental simulation and industrial application verify the effectiveness and feasibility of the proposed model.
The silicon content variation trend, which can reflect the quality of molten iron, provides significant information that can assist in ensuring the smooth operation of a blast furnace. This paper proposes a novel dynamic data-driven model for the online classification of the variation trend for the silicon content. Typically, a dynamic model for the silicon content variation trend primarily relies on process data feature extraction. First, a multilevel features fusion algorithm based on mutual information is developed to extract a rich and robust feature representation. Subsequently, the fused multilevel feature vectors and their corresponding trend labels are fed into a recurrent neural network model to capture the process dynamics and classify the variation trend. An experimental simulation and industrial application verified the effectiveness and feasibility of the proposed method. The classification results can provide guidance to ensure that the quality of molten iron is maintained within the desired range in the ironmaking process. |
| Author | Xie, Yongfang Gui, Weihua Chen, Zhipeng Jiang, Ke Jiang, Zhaohui Pan, Dong |
| Author_xml | – sequence: 1 givenname: Ke orcidid: 0000-0002-2642-7948 surname: Jiang fullname: Jiang, Ke – sequence: 2 givenname: Zhaohui surname: Jiang fullname: Jiang, Zhaohui – sequence: 3 givenname: Yongfang orcidid: 0000-0002-2060-6574 surname: Xie fullname: Xie, Yongfang – sequence: 4 givenname: Zhipeng orcidid: 0000-0002-5890-3072 surname: Chen fullname: Chen, Zhipeng email: chenzhipeng0803@163.com – sequence: 5 givenname: Dong orcidid: 0000-0002-1876-3108 surname: Pan fullname: Pan, Dong – sequence: 6 givenname: Weihua surname: Gui fullname: Gui, Weihua |
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| Cites_doi | 10.1016/j.ins.2015.07.002 10.1109/TNNLS.2017.2749412 10.2355/isijinternational.ISIJINT-2019-119 10.1016/j.ins.2019.04.018 10.1016/0952-1976(91)90001-M 10.1016/j.ins.2011.12.031 10.1109/78.650093 10.1109/TCYB.2017.2734043 10.1109/TII.2012.2226897 10.1109/TIE.2011.2159693 10.1109/TII.2012.2214394 10.1016/j.ins.2019.01.062 10.3390/s18113792 10.1109/TFUZZ.2013.2269145 10.1109/TII.2018.2868364 10.1109/TFUZZ.2017.2692203 10.1109/TIE.2017.2772141 10.1016/j.ins.2019.06.039 10.1016/j.ins.2017.07.003 10.1109/TIE.2012.2206336 |
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| Keywords | Denoising autoencoder (DAE) Multilevel features fusion Variation trend for silicon content Recurrent neural network (RNN) Classification |
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| SubjectTerms | Classification Denoising autoencoder (DAE) Multilevel features fusion Recurrent neural network (RNN) Variation trend for silicon content |
| Title | Classification of silicon content variation trend based on fusion of multilevel features in blast furnace ironmaking |
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