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Citations:  Bibtex RefMan EndNote GB/T7714

 ORCID:

Xinmin ZHANG

https://orcid.org/0000-0002-4761-3969

Yueyang LUO

https://orcid.org/0000-0003-4119-6129

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Frontiers of Information Technology & Electronic Engineering 

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


Author(s):  Yueyang LUO, Xinmin ZHANG, Manabu KANO, Long DENG, Chunjie YANG, Zhihuan SONG

Affiliation(s):  State Key Laboratory of Industrial Control Technology, College of Control Science and Engineering, Zhejiang University, Hangzhou 310027, China; more

Corresponding email(s):  luoyueyang@zju.edu.cn, xinminzhang@zju.edu.cn, manabu@human.sys.i.kyotou.ac.jp

Key Words:  Soft sensors; Data-driven modeling; Machine learning; Deep learning; Blast furnace; Ironmaking process


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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,in press.https://doi.org/10.1631/FITEE.2200366

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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.

高炉炼铁过程数据驱动软测量技术研究综述

罗月阳1,张新民1,Manabu Kano2,邓龙3,杨春节1,宋执环1
1浙江大学控制科学与工程学院工业控制技术国家重点实验室,中国杭州市,310027
2日本京都大学系统科学系,日本京都市,606-8501
3宝山钢铁股份有限公司研究院,中国上海市,201900
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。

关键词组:软测量;数据驱动建模;机器学习;深度学习;高炉;炼铁过程

Darkslateblue:Affiliate; Royal Blue:Author; Turquoise:Article

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