CLC number: TP277
On-line Access: 2023-03-25
Received: 2022-08-30
Revision Accepted: 2023-03-25
Crosschecked: 2022-12-15
Cited: 0
Clicked: 2395
Citations: Bibtex RefMan EndNote GB/T7714
Yueyang LUO, Xinmin ZHANG, Manabu KANO, Long DENG, Chunjie YANG, Zhihuan SONG. Data-driven soft sensors in blast furnace ironmaking: a survey[J]. Frontiers of Information Technology & Electronic Engineering, 2023, 24(3): 327-354.
@article{title="Data-driven soft sensors in blast furnace ironmaking: a survey",
author="Yueyang LUO, Xinmin ZHANG, Manabu KANO, Long DENG, Chunjie YANG, Zhihuan SONG",
journal="Frontiers of Information Technology & Electronic Engineering",
volume="24",
number="3",
pages="327-354",
year="2023",
publisher="Zhejiang University Press & Springer",
doi="10.1631/FITEE.2200366"
}
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%T Data-driven soft sensors in blast furnace ironmaking: a survey
%A Yueyang LUO
%A Xinmin ZHANG
%A Manabu KANO
%A Long DENG
%A Chunjie YANG
%A Zhihuan SONG
%J Frontiers of Information Technology & Electronic Engineering
%V 24
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%P 327-354
%@ 2095-9184
%D 2023
%I Zhejiang University Press & Springer
%DOI 10.1631/FITEE.2200366
TY - JOUR
T1 - Data-driven soft sensors in blast furnace ironmaking: a survey
A1 - Yueyang LUO
A1 - Xinmin ZHANG
A1 - Manabu KANO
A1 - Long DENG
A1 - Chunjie YANG
A1 - Zhihuan SONG
J0 - Frontiers of Information Technology & Electronic Engineering
VL - 24
IS - 3
SP - 327
EP - 354
%@ 2095-9184
Y1 - 2023
PB - Zhejiang University Press & Springer
ER -
DOI - 10.1631/FITEE.2200366
Abstract: The blast furnace is a highly energy-intensive, highly polluting, and extremely complex reactor in the ironmaking process. soft sensors are a key technology for predicting molten iron quality indices reflecting blast furnace energy consumption and operation stability, and play an important role in saving energy, reducing emissions, improving product quality, and producing economic benefits. With the advancement of the Internet of Things, big data, and artificial intelligence, data-driven soft sensors in blast furnace ironmaking processes have attracted increasing attention from researchers, but there has been no systematic review of the data-driven soft sensors in the blast furnace ironmaking process. This review covers the state-of-the-art studies of data-driven soft sensors technologies in the blast furnace ironmaking process. Specifically, we first conduct a comprehensive overview of various data-driven soft sensor modeling methods (multiscale methods, adaptive methods, deep learning, etc.) used in blast furnace ironmaking. Second, the important applications of data-driven soft sensors in blast furnace ironmaking (silicon content, molten iron temperature, gas utilization rate, etc.) are classified. Finally, the potential challenges and future development trends of data-driven soft sensors in blast furnace ironmaking applications are discussed, including digital twin, multi-source data fusion, and carbon peaking and carbon neutrality.
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