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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Description
Summary: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 from high-dimensional raw data. Recently, deep learning networks have been developed and considered a promising technology in extracting high-level abstract features. However, most of these networks cannot capture deep target-related features for abnormality monitoring. Thus, this article proposes a novel stacked dynamic target-driven denoising autoencoder for layer-by-layer hierarchical feature representation, and the dynamic relationship between samples and targets is described by dynamic factors. Then, we design a corresponding target-driven reconstruction loss function to pretrain the deep network successively. Experimental results in an ironmaking plant demonstrate the effectiveness and feasibility of the proposed method.
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ISSN:1551-3203
1941-0050
DOI:10.1109/TII.2021.3084911