Abnormality Monitoring in the Blast Furnace Ironmaking Process Based on Stacked Dynamic Target-Driven Denoising Autoencoders

Accurate monitoring of abnormalities is of great significance to the stable operation of the blast furnace ironmaking process. This article proposes a data-driven model to accurately monitor the abnormal conditions of blast furnaces. Generally, data-driven models primarily rely on feature extraction...

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Bibliographic Details
Published in:	IEEE transactions on industrial informatics Vol. 18; no. 3; pp. 1854 - 1863
Main Authors:	Jiang, Ke, Jiang, Zhaohui, Xie, Yongfang, Pan, Dong, Gui, Weihua
Format:	Journal Article
Language:	English
Published:	Piscataway IEEE 01.03.2022 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:	Abnormalities Blast furnace (BF) Blast furnace practice Blast furnaces Data models Deep learning denoising autoencoder (DAE) Design factors dynamic target-driven denoising autoencoder (D-DAE) Feature extraction Iron Ironmaking Machine learning Monitoring Noise reduction process monitoring
ISSN:	1551-3203, 1941-0050
Online Access:	Get full text
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