Data-driven soft sensors in blast furnace ironmaking:a survey  

作　　者：Yueyang LUO Xinmin ZHANG Manabu KANO Long DENG Chunjie YANG Zhihuan SONG 

机构地区：[1]State Key Laboratory of Industrial Control Technology,College of Control Science and Engineering,Zhejiang University,Hangzhou 310027,China [2]Department of Systems Science,Kyoto University,Kyoto 606-8501,Japan [3]Research Institute,Baoshan Iron&Steel Co.,Ltd.,Shanghai 201900,China

出　　处：《Frontiers of Information Technology & Electronic Engineering》2023年第3期327-354,共28页信息与电子工程前沿（英文版）

基　　金：Project supported by the National Natural Science Founda-tion of China(Nos.62003301,61933013,and 61833014);the Natural Science Foundation of Zhejiang Province,China(No.LQ21F030018);the Open Research Project of the State Key Laboratory of Industrial Control Technology,Zhejiang Univer-sity,China(Nos.ICT2022B30 and ICT2022B08)。

摘　　要：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,wefirst 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.

关 键 词：Soft sensors Data-driven modeling Machine learning Deep learning Blast furnace IRONMAKING 

分 类 号：TP18[自动化与计算机技术—控制理论与控制工程] TF54[自动化与计算机技术—控制科学与工程]