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

Yueyang LUO; Xinmin ZHANG; Manabu KANO; Long DENG; Chunjie YANG; Zhihuan SONG

doi:10.1631/FITEE.2200366

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

Regular Papers | Updated：2023-03-24

- Data-driven soft sensors in blast furnace ironmaking: a survey
  Cover Article Enhanced Publication
- 高炉炼铁过程数据驱动软测量技术研究综述
- Frontiers of Information Technology & Electronic Engineering Vol. 24, Issue 3, Pages: 327-354(2023)
- Affiliations：
  
  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
- Author bio：
  
  [ "Yueyang LUO received his BE degree in automation from School of Mechanical Engineering and Automation, Beihang University, Beijing, China in 2021. He is currently working toward his ME degree in electronic information engineering in College of Control Science and Engineering, Zhejiang University, China. His research interests include time-series prediction, deep learning, and soft sensor." ]
  [ "Xinmin ZHANG, corresponding author of this invited paper, received his PhD degree in systems science from Kyoto University, Japan in 2019. From April to December 2019, he was a postdoctoral research fellow in Department of Systems Science, Kyoto University, Japan. Currently, he is an associate professor with the State Key Laboratory of Industrial Control Technology, College of Control Science and Engineering, Zhejiang University, China. His research interests include industrial big data modeling and analysis, process control, fault diagnosis, soft-sensor, and machine learning and deep learning with applications to industrial processes." ]
  [ "Manabu KANO received his BS, MS, and PhD degrees from Department of Chemical Engineering at Kyoto University in 1992, 1994, and 1999, respectively. He has been an instructor at Kyoto University since 1994. From 1999 to 2000, he was a visiting scholar at Ohio State University, USA. Since 2012, he has been a professor of systems science, Kyoto University. He has conducted research in collaboration with various industries, such as semiconductor, chemical, steel, and pharmaceutical. His research interests focus on process data analysis for quality improvement, process monitoring, and process control." ]
  [ "Long DENG is a senior engineer with the Central Research Institute of Baoshan Iron & Steel Co., Ltd., Shanghai, China. His research interests include automation and intelligent control of key processes, such as steelmaking, continuous casting, heating, and heat treatment." ]
  [ "Chunjie YANG received his BS degree in machine design, MS degree in fluid transmission and control, and PhD degree in industrial automation from Zhejiang University, China in 1992, 1995, and 1998, respectively. In 2009, he was a visiting scholar at Florida State University, USA. He is currently a professor in College of Control Science and Engineering, and a Qiushi Distinguished Professor of Zhejiang University, China. His current research interests include industrial big data, process monitoring, soft-sensor, and fault diagnosis." ]
  [ "Zhihuan SONG received his BE and ME degrees in industrial automation from Hefei University of Technology, China in 1983 and 1986, respectively, and his PhD degree in industrial automation from Zhejiang University, China in 1997. Since 1997, he has been in College of Control Science and Engineering, Zhejiang University, where he was first a postdoctoral research fellow, then an associate professor, and is currently a full professor. His research interests include modeling and fault diagnosis of industrial processes, analytics and applications of industrial big data, and advanced process control technologies." ]
- Funds：
- DOI：10.1631/FITEE.2200366
  CLC： TP277
- Published：0 March 2023，
  
  Received：30 August 2022，
  
  Accepted：2022-12-15
- Accepted：
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YUEYANG LUO, XINMIN ZHANG, MANABU KANO, et al. Data-driven soft sensors in blast furnace ironmaking: a survey. [J]. Frontiers of information technology & electronic engineering, 2023, 24(3): 327-354.
DOI：

YUEYANG LUO, XINMIN ZHANG, MANABU KANO, et al. Data-driven soft sensors in blast furnace ironmaking: a survey. [J]. Frontiers of information technology & electronic engineering, 2023, 24(3): 327-354. DOI： 10.1631/FITEE.2200366.

摘要

在高能耗、高污染、极为复杂的冶炼过程中，高炉是极为重要的反应器。软测量技术是在线实时预测反映高炉能耗和运行稳定性质量指标的关键技术，在节能减排、提高产品质量和带来经济效益方面发挥着重要作用。随着物联网、大数据和人工智能的发展，高炉炼铁过程中的数据驱动软测量技术受到越来越多关注，但目前尚无关于高炉炼铁过程数据驱动软测量技术的系统性总结与评价。本文详细总结了高炉炼铁过程数据驱动软测量技术的最新研究成果与发展现状。具体而言，首先对高炉炼铁中使用的各种数据驱动软测量建模方法（如多尺度方法、自适应方法、深度学习等）进行了全面分类总结与分析。其次，对高炉炼铁中数据驱动软测量技术的应用现状（如硅含量、熔铁温度、气体利用率等）作对比分析。最后，展望了数据驱动软测量技术在高炉数字孪生、多源信息融合、碳达峰与碳中和等方面的潜在挑战和未来发展趋势。

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.

关键词

软测量数据驱动建模机器学习深度学习高炉炼铁过程

Keywords

Soft sensorsData-driven modelingMachine learningDeep learningBlast furnaceIronmaking process

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