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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Bibliographic Details

Published in:	Information sciences Vol. 521; pp. 32 - 45
Main Authors:	Jiang, Ke, Jiang, Zhaohui, Xie, Yongfang, Chen, Zhipeng, Pan, Dong, Gui, Weihua
Format:	Journal Article
Language:	English
Published:	Elsevier Inc 01.06.2020
Subjects:	Classification Denoising autoencoder (DAE) Multilevel features fusion Recurrent neural network (RNN) Variation trend for silicon content Denoising autoencoder (DAE) Multilevel features fusion Variation trend for silicon content Recurrent neural network (RNN) Classification
ISSN:	0020-0255, 1872-6291
Online Access:	Get full text
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