高炉炼铁过程数据驱动软测量技术研究综述

罗月阳; 张新民; Manabu Kano; 邓龙; 杨春节; 宋执环

doi:10.1631/FITEE.2200366

Your Location：

Home >

Browse articles >

高炉炼铁过程数据驱动软测量技术研究综述

常规文章 | Updated：2023-03-24

- 高炉炼铁过程数据驱动软测量技术研究综述
  Cover Article Enhanced Publication
- Data-driven soft sensors in blast furnace ironmaking: a survey
- 信息与电子工程前沿（英文） 2023年24卷第3期页码：327-354
- 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：
  
  National Natural Science Foundation of China(62003301;61933013;61833014);Natural Science Foundation of Zhejiang Province, China(LQ21F030018);Open Research Project of the State Key Laboratory of Industrial Control Technology, Zhejiang University, China(ICT2022B30;ICT2022B08)
- DOI：10.1631/FITEE.2200366
  中图分类号： TP277
- 收稿：2022-08-30，
  
  录用：2022-12-15，
  
  纸质出版：2023-03-0
- Accepted：
Scan QR Code
罗月阳, 张新民, Manabu Kano, 等. 高炉炼铁过程数据驱动软测量技术研究综述[J]. 信息与电子工程前沿（英文）, 2023,24(3):327-354.

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.
罗月阳, 张新民, Manabu Kano, 等. 高炉炼铁过程数据驱动软测量技术研究综述[J]. 信息与电子工程前沿（英文）, 2023,24(3):327-354. DOI： 10.1631/FITEE.2200366.

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

Keywords

references

An JQ , Wu M , He Y , 2013 . A temperature field detection system for blast furnace based on multi-source information fusion . Intell Autom Soft Comput , 19 ( 4 ): 625 - 634 . doi: 10.1080/10798587.2013.869121 http://doi.org/10.1080/10798587.2013.869121

An JQ , Peng K , Cao WH , et al. , 2016 . A soft-sensing method for missing temperature information based on dynamic neural network on BF wall . J Chem Ind Eng , 67 ( 3 ): 903 - 911 (in Chinese) . doi: 10.11949/j.issn.0438-1157.20151941 http://doi.org/10.11949/j.issn.0438-1157.20151941

An JQ , Shen XL , Wu M , et al. , 2019 . A multi-time-scale fusion prediction model for the gas utilization rate in a blast furnace . Contr Eng Pract , 92 : 104120 . doi: 10.1016/j.conengprac.2019.104120 http://doi.org/10.1016/j.conengprac.2019.104120

Azadi P , Minaabad SA , Bartusch H , et al. , 2020 . Nonlinear prediction model of blast furnace operation status . Comput Aided Chem Eng , 48 : 217 - 222 . doi: 10.1016/B978-0-12-823377-1.50037-9 http://doi.org/10.1016/B978-0-12-823377-1.50037-9

Azadi P , Winz J , Leo E , et al. , 2022 . A hybrid dynamic model for the prediction of molten iron and slag quality indices of a large-scale blast furnace . Comput Chem Eng , 156 : 107573 . doi: 10.1016/j.compchemeng.2021.107573 http://doi.org/10.1016/j.compchemeng.2021.107573

Azzedine A , Nouri F , Bouhouche S , 2021 . Mathematical and numerical results for quality control of hot metal in blast furnace . J Math Comput Sci , 11 ( 3 ): 2914 - 2933 .

Cardoso W , di Felice R , 2021 . Prediction of silicon content in the hot metal using Bayesian networks and probabilistic reasoning . Int J Adv Intell Inform , 7 ( 3 ): 268 - 281 . doi: 10.26555/ijain.v7i3.771 http://doi.org/10.26555/ijain.v7i3.771

Chen K , Liu Y , 2017 . Adaptive weighting just-in-time-learning quality prediction model for an industrial blast furnace . ISIJ Int , 57 ( 1 ): 107 - 113 . doi: 10.2355/isijinternational.ISIJINT-2016-292 http://doi.org/10.2355/isijinternational.ISIJINT-2016-292

Chen K , Liang Y , Gao Z , et al. , 2017 . Just-in-time correntropy soft sensor with noisy data for industrial silicon content prediction . Sensors , 17 ( 8 ): 1830 . doi: 10.3390/s17081830 http://doi.org/10.3390/s17081830

Chen SH , Gao CH , 2020 . Linear priors mined and integrated for transparency of blast furnace black-box SVM model . IEEE Trans Ind Inform , 16 ( 6 ): 3862 - 3870 . doi: 10.1109/TII.2019.2940475 http://doi.org/10.1109/TII.2019.2940475

Chu YX , Gao CH , 2014 . Data-based multiscale modeling for blast furnace system . AIChE J , 60 ( 6 ): 2197 - 2210 . doi: 10.1002/aic.14426 http://doi.org/10.1002/aic.14426

Cui GM , Jiang ZG , Liu PL , et al. , 2018 . Prediction of blast furnace temperature based on multi-information fusion of image and data . Proc Chinese Automation Congress , p. 2317 - 2322 . doi: 10.1109/CAC.2018.8623386 http://doi.org/10.1109/CAC.2018.8623386

Ding ZY , Zhang J , Liu Y , 2017 . Ensemble non-Gaussian local regression for industrial silicon content prediction . ISIJ Int , 57 ( 11 ): 2022 - 2027 . doi: 10.2355/isijinternational.ISIJINT-2017-251 http://doi.org/10.2355/isijinternational.ISIJINT-2017-251

Diniz APM , Côco KF , Gomes FSV , et al. , 2021 . Forecasting model of silicon content in molten iron using wavelet decomposition and artificial neural networks . Metals , 11 ( 7 ): 1001 . doi: 10.3390/met11071001 http://doi.org/10.3390/met11071001

Du S , Wu M , Chen LF , et al. , 2021 . Prediction model of burn-through point with fuzzy time series for iron ore sintering process . Eng Appl Artif Intell , 102 : 104259 . doi: 10.1016/j.engappai.2021.104259 http://doi.org/10.1016/j.engappai.2021.104259

Fang YJ , Jiang ZH , 2020 . Use of adaptive weighted echo state network ensemble for construction of prediction intervals and prediction reliability of silicon content in ironmaking process . Proc 2nd Int Conf on Industrial Artificial Intelligence , p. 1 - 6 . doi: 10.1109/IAI50351.2020.9262187 http://doi.org/10.1109/IAI50351.2020.9262187

Fang YJ , Jiang ZH , Pan D , et al. , 2020 . Soft sensors based on adaptive stacked polymorphic model for silicon content prediction in ironmaking process . IEEE Trans Instrum Meas , 70 : 2503412 . doi: 10.1109/TIM.2020.3038285 http://doi.org/10.1109/TIM.2020.3038285

Fontes DOL , Vasconcelos LGS , Brito RP , 2020 . Blast furnace hot metal temperature and silicon content prediction using soft sensor based on fuzzy C-means and exogenous nonlinear autoregressive models . Comput Chem Eng , 141 : 107028 . doi: 10.1016/j.compchemeng.2020.107028 http://doi.org/10.1016/j.compchemeng.2020.107028

Fortuna L , Graziani S , Rizzo A , et al. , 2007 . Soft Sensors for Monitoring and Control of Industrial Processes . Springer , London, UK . doi: 10.1007/978-1-84628-480-9 http://doi.org/10.1007/978-1-84628-480-9

Gao CH , Jian L , Liu XY , et al. , 2011a . Data-driven modeling based on Volterra series for multidimensional blast furnace system . IEEE Trans Neur Netw , 22 ( 12 ): 2272 - 2283 . doi: 10.1109/TNN.2011.2175945 http://doi.org/10.1109/TNN.2011.2175945

Gao CH , Zeng J , Zhou ZY , 2011b . Identification of multiscale nature and multiple dynamics of the blast furnace system from operating data . AIChE J , 57 ( 12 ): 3448 - 3458 . doi: 10.1002/aic.12554 http://doi.org/10.1002/aic.12554

Gao CH , Lin QQ , Ni JS , et al. , 2021 . A nonuniform delay-coordinate embedding-based multiscale predictor for blast furnace systems . IEEE Trans Contr Syst Technol , 29 ( 5 ): 2223 - 2230 . doi: 10.1109/TCST.2020.3023072 http://doi.org/10.1109/TCST.2020.3023072

Gao S , Dai Y , Li YJ , et al. , 2022 . Augmented flame image soft sensor for combustion oxygen content prediction . Meas Sci Technol , 34 ( 1 ): 015401 . doi: 10.1088/1361-6501/ac95b5 http://doi.org/10.1088/1361-6501/ac95b5

Geerdes M , Chaigneau R , Lingiardi O , 2020 . Modern Blast Furnace Ironmaking: an Introduction ( 4 th Ed.). IOS Press , Amsterdam, the Netherlands .

Glaessgen E , Stargel D , 2012 . The digital twin paradigm for future NASA and U.S. air force vehicles . Proc 53rd AIAA/ASME/ASCE/AHS/ASC Structures, Structural Dynamics and Materials Conf , p. 1818 . doi: 10.2514/6.2012-1818 http://doi.org/10.2514/6.2012-1818

Hashimoto Y , Sawa Y , Kitamura Y , et al. , 2018 . Development and validation of kinematical blast furnace model with long-term operation data . ISIJ Int , 58 ( 12 ): 2210 - 2218 . doi: 10.2355/isijinternational.ISIJINT-2018-177 http://doi.org/10.2355/isijinternational.ISIJINT-2018-177

Hashimoto Y , Kitamura Y , Ohashi T , et al. , 2019a . Transient model-based operation guidance on blast furnace . Contr Eng Pract , 82 : 130 - 141 . doi: 10.1016/j.conengprac.2018.10.009 http://doi.org/10.1016/j.conengprac.2018.10.009

Hashimoto Y , Okamoto Y , Kaise T , et al. , 2019b . Practical operation guidance on thermal control of blast furnace . ISIJ Int , 59 ( 9 ): 1573 - 1581 . doi: 10.2355/isijinternational.ISIJINT-2019-119 http://doi.org/10.2355/isijinternational.ISIJINT-2019-119

He BC , Zhang QZ , Zhang XM , 2022 . A faster dynamic feature extractor and its application to industrial quality prediction . IEEE Trans Ind Inform , p. 1-11 , early access . doi: 10.1109/TII.2022.3205356 http://doi.org/10.1109/TII.2022.3205356

He X , Ji J , Liu KX , et al. , 2019 . Soft sensing of silicon content via bagging local semi-supervised models . Sensors , 19 ( 17 ): 3814 . doi: 10.3390/s19173814 http://doi.org/10.3390/s19173814

Hu TH , Wang XP , Wang Y , et al. , 2021 . Prediction of blast furnace temperature based on evolutionary optimization . Proc 11th Int Conf on Evolutionary Multi-criterion Optimization , p. 759 - 768 . doi: 10.1007/978-3-030-72062-9_60 http://doi.org/10.1007/978-3-030-72062-9_60

Hu YF , Zhou H , Yao S , et al. , 2022 . Comprehensive evaluation of the blast furnace status based on data mining and mechanism analysis . Int J Chem React Eng , 20 ( 2 ): 225 - 235 . doi: 10.1515/ijcre-2021-0160 http://doi.org/10.1515/ijcre-2021-0160

Jian L , Gao CH , 2013 . Binary coding SVMs for the multiclass problem of blast furnace system . IEEE Trans Ind Electron , 60 ( 9 ): 3846 - 3856 . doi: 10.1109/TIE.2012.2206336 http://doi.org/10.1109/TIE.2012.2206336

Jian L , Gao CH , Xia ZQ , 2011 . A sliding-window smooth support vector regression model for nonlinear blast furnace system . Steel Res Int , 82 ( 3 ): 169 - 179 . doi: 10.1002/srin.201000082 http://doi.org/10.1002/srin.201000082

Jian L , Gao CH , Xia ZH , 2012 . Constructing multiple kernel learning framework for blast furnace automation . IEEE Trans Autom Sci Eng , 9 ( 4 ): 763 - 777 . doi: 10.1109/TASE.2012.2211100 http://doi.org/10.1109/TASE.2012.2211100

Jiang K , Jiang ZH , Xie YF , et al. , 2018 . A trend prediction method based on fusion model and its application . Proc 13th World Congress on Intelligent Control and Automation , p. 322 - 328 . doi: 10.1109/WCICA.2018.8630347 http://doi.org/10.1109/WCICA.2018.8630347

Jiang K , Jiang ZH , Xie YF , et al. , 2020 . Classification of silicon content variation trend based on fusion of multilevel features in blast furnace ironmaking . Inform Sci , 521 : 32 - 45 . doi: 10.1016/j.ins.2020.02.039 http://doi.org/10.1016/j.ins.2020.02.039

Jiang YC , Yin S , Dong JW , et al. , 2021a . A review on soft sensors for monitoring, control, and optimization of industrial processes . IEEE Sens J , 21 ( 11 ): 12868 - 12881 . doi: 10.1109/JSEN.2020.3033153 http://doi.org/10.1109/JSEN.2020.3033153

Jiang YC , Yin S , Li K , et al. , 2021b . Industrial applications of digital twins . Phil Trans R Soc A Math Phys Eng Sci , 379 ( 2207 ): 20200360 . doi: 10.1098/rsta.2020.0360 http://doi.org/10.1098/rsta.2020.0360

Jiang YS , Yang N , Yao QQ , et al. , 2020 . Real-time moisture control in sintering process using offline-online NARX neural networks . Neurocomputing , 396 : 209 - 215 . doi: 10.1016/j.neucom.2018.07.099 http://doi.org/10.1016/j.neucom.2018.07.099

Jiang ZH , Pan D , Gui WH , et al. , 2018 . Temperature measurement of molten iron in taphole of blast furnace combined temperature drop model with heat transfer model . Ironmak Steelmak , 45 ( 3 ): 230 - 238 . doi: 10.1080/03019233.2016.1254423 http://doi.org/10.1080/03019233.2016.1254423

Jiménez J , Mochón J , de Ayala JS , et al. , 2004 . Blast furnace hot metal temperature prediction through neural networks-based models . ISIJ Int , 44 ( 3 ): 573 - 580 .

Kramer MA , 1991 . Nonlinear principal component analysis using autoassociative neural networks . AIChE J , 37 ( 2 ): 233 - 243 . doi: 10.1002/aic.690370209 http://doi.org/10.1002/aic.690370209

Lay-Ekuakille A , Ugwiri MA , Okitadiowo JD , et al. , 2021 . Computer vision for sensed images approach in extremely harsh environments: blast furnace chute wear characterization . IEEE Sens J , 21 ( 10 ): 11969 - 11976 . doi: 10.1109/JSEN.2021.3063264 http://doi.org/10.1109/JSEN.2021.3063264

Li HY , Bu XP , Liu XJ , et al. , 2021 . Evaluation and prediction of blast furnace status based on big data platform of ironmaking and data mining . ISIJ Int , 61 ( 1 ): 108 - 118 . doi: 10.2355/isijinternational.ISIJINT-2020-249 http://doi.org/10.2355/isijinternational.ISIJINT-2020-249

Li J , Gao CH , 2010 . Multi-scale entropy analysis on the complexity of blast furnace ironmaking process . Proc 2nd Int Conf on Industrial Mechatronics and Automation , p. 257 - 260 . doi: 10.1109/ICINDMA.2010.5538172 http://doi.org/10.1109/ICINDMA.2010.5538172

Li JL , Zhu RJ , Zhou P , et al. , 2021 . Prediction of the cohesive zone in a blast furnace by integrating CFD and SVM modelling . Ironmak Steelmak , 48 ( 3 ): 284 - 291 . doi: 10.1080/03019233.2020.1771893 http://doi.org/10.1080/03019233.2020.1771893

Li JP , Hua CC , Yang YN , et al. , 2018 . Fuzzy classifier design for development tendency of hot metal silicon content in blast furnace . IEEE Trans Ind Inform , 14 ( 3 ): 1115 - 1123 . doi: 10.1109/TII.2017.2770177 http://doi.org/10.1109/TII.2017.2770177

Li JP , Hua CC , Yang YN , 2021a . A novel multiple-input-multiple-output random vector functional-link networks for predicting molten iron quality indexes in blast furnace . IEEE Trans Ind Electron , 68 ( 11 ): 11309 - 11317 . doi: 10.1109/TIE.2020.3031525 http://doi.org/10.1109/TIE.2020.3031525

Li JP , Hua CC , Yang YN , et al. , 2021b . A novel MIMO T-S fuzzy modeling for prediction of blast furnace molten iron quality with missing outputs . IEEE Trans Fuzzy Syst , 29 ( 6 ): 1654 - 1666 . doi: 10.1109/TFUZZ.2020.2983667 http://doi.org/10.1109/TFUZZ.2020.2983667

Li JP , Hua CC , Yang YN , et al. , 2022 . Data-driven Bayesian-based Takagi-Sugeno fuzzy modeling for dynamic prediction of hot metal silicon content in blast furnace . IEEE Trans Syst Man Cybern Syst , 52 ( 2 ): 1087 - 1099 . doi: 10.1109/TSMC.2020.3013972 http://doi.org/10.1109/TSMC.2020.3013972

Li S , Li WQ , Cook C , et al. , 2018 . Independently recurrent neural network (IndRNN): building a longer and deeper RNN . Proc IEEE/CVF Conf on Computer Vision and Pattern Recognition , p. 5457 - 5466 . doi: 10.1109/CVPR.2018.00572 http://doi.org/10.1109/CVPR.2018.00572

Li S , Chang JC , Chu MS , et al. , 2022 . A blast furnace coke ratio prediction model based on fuzzy cluster and grid search optimized support vector regression . Appl Intell , 52 ( 12 ): 13533 - 13542 . doi: 10.1007/S10489-022-03234-8 http://doi.org/10.1007/S10489-022-03234-8

Li WP , Zhou P , 2020 . Robust regularized RVFLNs modeling of molten iron quality in blast furnace ironmaking . Acta Autom Sin , 46 ( 4 ): 721 - 733 (in Chinese) . doi: 10.16383/j.aas.2018.c170670 http://doi.org/10.16383/j.aas.2018.c170670

Li WY , Zhuo YT , Bao J , et al. , 2021 . A data-based soft-sensor approach to estimating raceway depth in ironmaking blast furnaces . Powd Technol , 390 : 529 - 538 . doi: 10.1016/j.powtec.2021.05.072 http://doi.org/10.1016/j.powtec.2021.05.072

Li YJ , Zhang S , Yin YX , et al. , 2019 . A soft sensing scheme of gas utilization ratio prediction for blast furnace via improved extreme learning machine . Neur Process Lett , 50 ( 2 ): 1191 - 1213 . doi: 10.1007/s11063-018-9888-3 http://doi.org/10.1007/s11063-018-9888-3

Li YJ , Zhang S , Zhang J , et al. , 2020 . Data-driven multiobjective optimization for burden surface in blast furnace with feedback compensation . IEEE Trans Ind Inform , 16 ( 4 ): 2233 - 2244 . doi: 10.1109/tii.2019.2908989 http://doi.org/10.1109/tii.2019.2908989

Li YJ , Li HQ , Zhang J , et al. , 2021 . Data and knowledge driven approach for burden surface optimization in blast furnace . Comput Electr Eng , 92 : 107191 . doi: 10.1016/j.compeleceng.2021.107191 http://doi.org/10.1016/j.compeleceng.2021.107191

Li YJ , Zhang J , Zhang S , et al. , 2022 . Dual ensemble online modeling for dynamic estimation of hot metal silicon content in blast furnace system . ISA Trans , 128 : 686 - 697 . doi: 10.1016/j.isatra.2021.09.018 http://doi.org/10.1016/j.isatra.2021.09.018

Li YQ , Cai D , Wang JL , et al. , 2020 . Recurrence behavior statistics of blast furnace gas sensor data in Industrial Internet of Things . IEEE Int Things J , 7 ( 6 ): 5666 - 5676 . doi: 10.1109/JIOT.2020.2980617 http://doi.org/10.1109/JIOT.2020.2980617

Li YR , Yang CJ , 2021 . Domain knowledge based explainable feature construction method and its application in ironmaking process . Eng Appl Artif Intell , 100 : 104197 . doi: 10.1016/j.engappai.2021.104197 http://doi.org/10.1016/j.engappai.2021.104197

Liu C , Tang LX , Liu JY , 2020 . A stacked autoencoder with sparse Bayesian regression for end-point prediction problems in steelmaking process . IEEE Trans Autom Sci Eng , 17 ( 2 ): 550 - 561 . doi: 10.1109/tase.2019.2935314 http://doi.org/10.1109/tase.2019.2935314

Liu Y , Gao Z , 2015 . Enhanced just-in-time modelling for online quality prediction in BF ironmaking . Ironmak Steelmak , 42 ( 5 ): 321 - 330 . doi: 10.1179/1743281214Y.0000000229 http://doi.org/10.1179/1743281214Y.0000000229

Liu Y , Yu HQ , Gao ZL , et al. , 2011 . Improved online prediction of silicon content in ironmaking process using support vector regression with novel outlier detection . Adv Mater Res , 154-155 : 251 - 255 . doi: 10.4028/www.scientific.net/AMR.154-155.251 http://doi.org/10.4028/www.scientific.net/AMR.154-155.251

Liu Y , Fan Y , Chen JH , 2017 . Flame images for oxygen content prediction of combustion systems using DBN . Energy Fuels , 31 ( 8 ): 8776 - 8783 . doi: 10.1021/acs.energyfuels.7b00576 http://doi.org/10.1021/acs.energyfuels.7b00576

Lughofer E , Pollak R , Feilmayr C , et al. , 2021 . Prediction and explanation models for hot metal temperature, silicon concentration, and cooling capacity in ironmaking blast furnaces . Steel Res Int , 92 ( 9 ): 2100078 . doi: 10.1002/srin.202100078 http://doi.org/10.1002/srin.202100078

Luo SH , Chen TX , 2020 . Two derivative algorithms of gradient boosting decision tree for silicon content in blast furnace system prediction . IEEE Access , 8 : 196112 - 196122 . doi: 10.1109/ACCESS.2020.3034566 http://doi.org/10.1109/ACCESS.2020.3034566

Luo SH , Dai Z , Guo F , et al. , 2019 . Identification of extreme temperature fluctuation in blast furnace based on fractal time series analysis . Techn Gaz , 26 ( 4 ): 1098 - 1103 . doi: 10.17559/TV-20190607101917 http://doi.org/10.17559/TV-20190607101917

Luo SH , Dai ZA , Chen TX , et al. , 2020 . A weighted SVM ensemble predictor based on AdaBoost for blast furnace ironmaking process . Appl Intell , 50 ( 7 ): 1997 - 2008 . doi: 10.1007/s10489-020-01662-y http://doi.org/10.1007/s10489-020-01662-y

Masson MH , Canu S , Grandvalet Y , et al. , 1999 . Software sensor design based on empirical data . Ecol Modell , 120 ( 2-3 ): 131 - 139 . doi: 10.1016/S0304-3800(99)00097-6 http://doi.org/10.1016/S0304-3800(99)00097-6

Pan D , Jiang ZH , Chen ZP , et al. , 2018 . Temperature measurement method for blast furnace molten iron based on infrared thermography and temperature reduction model . Sensors , 18 ( 11 ): 3792 . doi: 10.3390/s18113792 http://doi.org/10.3390/s18113792

Papadopoulos G , Edwards PJ , Murray AF , 2001 . Confidence estimation methods for neural networks: a practical comparison . IEEE Trans Neur Netw , 12 ( 6 ): 1278 - 1287 . doi: 10.1109/72.963764 http://doi.org/10.1109/72.963764

Radhakrishnan V , Mohamed A , 2000 . Neural networks for the identification and control of blast furnace hot metal quality . J Process Contr , 10 ( 6 ): 509 - 524 . doi: 10.1016/S0959-1524(99)00052-9 http://doi.org/10.1016/S0959-1524(99)00052-9

Rajesh N , Khare MR , Pabi SK , 2010 . Application of ANN modelling techniques in blast furnace iron making . Int J Model Simul , 30 ( 3 ): 340 - 344 . doi: 10.1080/02286203.2010.11442589 http://doi.org/10.1080/02286203.2010.11442589

Saxén H , Gao CH , Gao ZW , 2013 . Data-driven time discrete models for dynamic prediction of the hot metal silicon content in the blast furnace—a review . IEEE Trans Ind Inform , 9 ( 4 ): 2213 - 2225 . doi: 10.1109/TII.2012.2226897 http://doi.org/10.1109/TII.2012.2226897

Saxén JE , Saxén H , Toivonen HT , 2016 . Identification of switching linear systems using self-organizing models with application to silicon prediction in hot metal . Appl Soft Comput , 47 : 271 - 280 . doi: 10.1016/j.asoc.2016.05.048 http://doi.org/10.1016/j.asoc.2016.05.048

Shen XL , An JQ , Wu M , et al. , 2020 . Burden control strategy based on reinforcement learning for gas utilization rate in blast furnace . IFAC-PapersOnLine , 53 ( 2 ): 11704 - 11709 . doi: 10.1016/j.ifacol.2020.12.667 http://doi.org/10.1016/j.ifacol.2020.12.667

Song HD , Zhou P , Wang H , et al. , 2016 . Nonlinear subspace modeling of multivariate molten iron quality in blast furnace ironmaking and its application . Acta Autom Sin , 42 ( 11 ): 1664 - 1679 (in Chinese) . doi: 10.16383/j.aas.2016.c150819 http://doi.org/10.16383/j.aas.2016.c150819

Su XL , Zhang S , Yin YX , et al. , 2018 . Prediction model of permeability index for blast furnace based on the improved multi-layer extreme learning machine and wavelet transform . J Franklin Inst , 355 ( 4 ): 1663 - 1691 . doi: 10.1016/j.jfranklin.2017.05.001 http://doi.org/10.1016/j.jfranklin.2017.05.001

Su XL , Sun SL , Zhang S , et al. , 2020 . Improved multi-layer online sequential extreme learning machine and its application for hot metal silicon content . J Franklin Inst , 357 ( 17 ): 12588 - 12608 . doi: 10.1016/j.jfranklin.2020.05.031 http://doi.org/10.1016/j.jfranklin.2020.05.031

Sun QQ , Ge ZQ , 2021 . A survey on deep learning for data-driven soft sensors . IEEE Trans Ind Inform , 17 ( 9 ): 5853 - 5866 . doi: 10.1109/TII.2021.3053128 http://doi.org/10.1109/TII.2021.3053128

Waller M , Saxén H , 2003 . Time-varying event-internal trends in predictive modeling—methods with applications to ladlewise analyses of hot metal silicon content . Ind Eng Chem Res , 42 ( 1 ): 85 - 90 . doi: 10.1021/ie0201718 http://doi.org/10.1021/ie0201718

Wang GP , Yan ZY , Zhai HP , et al. , 2021 . Silicon content prediction of hot metal in blast furnace based on attention mechanism and CNN-IndRNN model . E3S Web Conf , 252 : 02025 . doi: 10.1051/e3sconf/202125202025 http://doi.org/10.1051/e3sconf/202125202025

Wang P , Hu TH , Tang LX , 2022 . A multiobjective evolutionary nonlinear ensemble learning with evolutionary feature selection for silicon prediction in blast furnace . IEEE Trans Neur Netw Learn Syst , 33 ( 5 ): 2080 - 2093 . doi: 10.1109/TNNLS.2021.3059784 http://doi.org/10.1109/TNNLS.2021.3059784

Wang ZY , Gao CH , Liu XG , 2011 . Using LSSVM model to predict the silicon content in hot metal based on KPCA feature extraction . Proc Chinese Control and Decision Conf , p. 1967 - 1971 . doi: 10.1109/CCDC.2011.5968523 http://doi.org/10.1109/CCDC.2011.5968523

Wang ZY , Jiang DW , Wang XD , et al. , 2021 . Prediction of blast furnace hot metal temperature based on support vector regression and extreme learning machine . Chin J Eng , 43 ( 4 ): 569 - 576 (in Chinese) . doi: 10.13374/j.issn2095-9389.2020.05.28.001 http://doi.org/10.13374/j.issn2095-9389.2020.05.28.001

Warne K , Prasad G , Rezvani S , et al. , 2004 . Statistical and computational intelligence techniques for inferential model development: a comparative evaluation and a novel proposition for fusion . Eng Appl Artif Intell , 17 ( 8 ): 871 - 885 . doi: 10.1016/j.engappai.2004.08.020 http://doi.org/10.1016/j.engappai.2004.08.020

Wen L , Zhou P , Wang H , et al. , 2018 . Model free adaptive predictive control of multivariate molten iron quality in blast furnace ironmaking . Proc IEEE Conf on Decision and Control , p. 2617 - 2622 . doi: 10.1109/CDC.2018.8619757 http://doi.org/10.1109/CDC.2018.8619757

Wu M , Zhang KX , An JQ , et al. , 2018 . An energy efficient decision-making strategy of burden distribution for blast furnace . Contr Eng Pract , 78 : 186 - 195 . doi: 10.1016/j.conengprac.2018.06.019 http://doi.org/10.1016/j.conengprac.2018.06.019

Xie J , Zhou P , 2020 . Robust stochastic configuration network multi-output modeling of molten iron quality in blast furnace ironmaking . Neurocomputing , 387 : 139 - 149 . doi: 10.1016/j.neucom.2020.01.030 http://doi.org/10.1016/j.neucom.2020.01.030

Xu X , Hua CC , Tang YG , et al. , 2016 . Modeling of the hot metal silicon content in blast furnace using support vector machine optimized by an improved particle swarm optimizer . Neur Comput Appl , 27 ( 6 ): 1451 - 1461 . doi: 10.1007/s00521-015-1951-7 http://doi.org/10.1007/s00521-015-1951-7

Yan F , Zhang XM , Yang CJ , et al. , 2022 . Data-driven modelling methods in sintering process: current research status and perspectives . Can J Chem Eng , early access . doi: 10.1002/cjce.24790 http://doi.org/10.1002/cjce.24790

Yang YL , Zhang S , Yin YX , 2016 . A modified ELM algorithm for the prediction of silicon content in hot metal . Neur Comput Appl , 27 ( 1 ): 241 - 247 . doi: 10.1007/s00521-014-1775-x http://doi.org/10.1007/s00521-014-1775-x

Yin F , An JQ , Shen XL , et al. , 2020 . Interval prediction model of blast furnace gas utilization rate based on multi-time-scale . Proc 39th Chinese Control Conf , p. 2300 - 2305 . doi: 10.23919/CCC50068.2020.9188400 http://doi.org/10.23919/CCC50068.2020.9188400

Yu X , Tan C , 2022 . China's pathway to carbon neutrality for the iron and steel industry . Glob Environ Change , 76 : 102574 . doi: 10.1016/j.gloenvcha.2022.102574 http://doi.org/10.1016/j.gloenvcha.2022.102574

Yuan M , Zhou P , Li ML , et al. , 2015 . Intelligent multivariable modeling of blast furnace molten iron quality based on dynamic AGA-ANN and PCA . J Iron Steel Res Int , 22 ( 6 ): 487 - 495 . doi: 10.1016/S1006-706X(15)30031-5 http://doi.org/10.1016/S1006-706X(15)30031-5

Zeng JS , Gao CH , Pan W , 2010a . Modeling of high dimensional blast furnace system by manifold learning . Proc 29th Chinese Control Conf , p. 3157 - 3161 .

Zeng JS , Gao CH , Su HY , 2010b . Data-driven predictive control for blast furnace ironmaking process . Comput Chem Eng , 34 ( 11 ): 1854 - 1862 . doi: 10.1016/j.compchemeng.2010.01.005 http://doi.org/10.1016/j.compchemeng.2010.01.005

Zhai XY , Chen MT , Lu WC , 2020 . Fuel ratio optimization of blast furnace based on data mining . ISIJ Int , 60 ( 11 ): 2471 - 2476 . doi: 10.2355/isijinternational.ISIJINT-2020-238 http://doi.org/10.2355/isijinternational.ISIJINT-2020-238

Zhang HG , Yin YX , Zhang S , 2016 . An improved ELM algorithm for the measurement of hot metal temperature in blast furnace . Neurocomputing , 174 : 232 - 237 . doi: 10.1016/j.neucom.2015.04.106 http://doi.org/10.1016/j.neucom.2015.04.106

Zhang HG , Zhang S , Yin YX , et al. , 2018 . Prediction of the hot metal silicon content in blast furnace based on extreme learning machine . Int J Mach Learn Cyber , 9 ( 10 ): 1697 - 1706 . doi: 10.1007/s13042-017-0674-8 http://doi.org/10.1007/s13042-017-0674-8

Zhang WL , Lin Q , Zhao J , et al. , 2016 . Soft computing for blast furnace gas system pressure based on an improved fuzzy model . Proc 12th World Congress on Intelligent Control and Automation , p. 2400 - 2406 . doi: 10.1109/WCICA.2016.7578494 http://doi.org/10.1109/WCICA.2016.7578494

Zhang XM , Kano M , Matsuzaki S , 2019a . A comparative study of deep and shallow predictive techniques for hot metal temperature prediction in blast furnace ironmaking . Comput Chem Eng , 130 : 106575 . doi: 10.1016/j.compchemeng.2019.106575 http://doi.org/10.1016/j.compchemeng.2019.106575

Zhang XM , Kano M , Matsuzaki S , 2019b . Ensemble pattern trees for predicting hot metal temperature in blast furnace . Comput Chem Eng , 121 : 442 - 449 . doi: 10.1016/j.compchemeng.2018.10.022 http://doi.org/10.1016/j.compchemeng.2018.10.022

Zhang XM , Kano M , Tani M , et al. , 2020a . Prediction and causal analysis of defects in steel products: handling nonnegative and highly overdispersed count data . Contr Eng Pract , 95 : 104258 . doi: 10.1016/j.conengprac.2019.104258 http://doi.org/10.1016/j.conengprac.2019.104258

Zhang XM , Wei CH , Song ZH , 2020b . Fast locally weighted PLS modeling for large-scale industrial processes . Ind Eng Chem Res , 59 ( 47 ): 20779 - 20786 . doi: 10.1021/acs.iecr.0c03932 http://doi.org/10.1021/acs.iecr.0c03932

Zhang ZY , Lu YF , Wang XJ , et al. , 2022 . A data-based compact high-order Volterra model for complex blast furnace system . IEEE Trans Ind Inform , 18 ( 9 ): 5827 - 5837 . doi: 10.1109/TII.2021.3131604 http://doi.org/10.1109/TII.2021.3131604

Zhao J , Wang W , Liu Y , et al. , 2011 . A two-stage online prediction method for a blast furnace gas system and its application . IEEE Trans Contr Syst Technol , 19 ( 3 ): 507 - 520 . doi: 10.1109/TCST.2010.2051545 http://doi.org/10.1109/TCST.2010.2051545

Zhao XD , Fang YM , Liu L , et al. , 2020 . Ameliorated moth-flame algorithm and its application for modeling of silicon content in liquid iron of blast furnace based fast learning network . Appl Soft Comput , 94 : 106418 . doi: 10.1016/j.asoc.2020.106418 http://doi.org/10.1016/j.asoc.2020.106418

Zhou B , Ye H , Zhang HF , et al. , 2016 . Process monitoring of iron-making process in a blast furnace with PCA-based methods . Contr Eng Pract , 47 : 1 - 14 . doi: 10.1016/j.conengprac.2015.11.006 http://doi.org/10.1016/j.conengprac.2015.11.006

Zhou L , Gao CH , Zeng JS , et al. , 2011 . The fractal multiscale trend decomposition of silicon content in blast furnace hot metal . ISIJ Int , 51 ( 4 ): 588 - 592 . doi: 10.2355/isijinternational.51.588 http://doi.org/10.2355/isijinternational.51.588

Zhou P , Yuan M , Wang H , et al. , 2015a . Data-driven dynamic modeling for prediction of molten iron silicon content using ELM with self-feedback . Math Probl Eng , 2015 : 326160 . doi: 10.1155/2015/326160 http://doi.org/10.1155/2015/326160

Zhou P , Yuan M , Wang H , et al. , 2015b . Multivariable dynamic modeling for molten iron quality using online sequential random vector functional-link networks with self-feedback connections . Inform Sci , 325 : 237 - 255 . doi: 10.1016/j.ins.2015.07.002 http://doi.org/10.1016/j.ins.2015.07.002

Zhou P , Zhang L , Li WP , et al. , 2018a . Autoencoder and PCA based RVFLNs modeling for multivariate molten iron quality in blast furnace ironmaking . Acta Autom Sin , 44 ( 10 ): 1799 - 1811 (in Chinese) . doi: 10.16383/j.aas.2018.c170299 http://doi.org/10.16383/j.aas.2018.c170299

Zhou P , Guo DW , Chai TY , 2018b . Data-driven predictive control of molten iron quality in blast furnace ironmaking using multi-output LS-SVR based inverse system identification . Neurocomputing , 308 : 101 - 110 . doi: 10.1016/j.neucom.2018.04.060 http://doi.org/10.1016/j.neucom.2018.04.060

Zhou P , Guo DW , Wang H , et al. , 2018c . Data-driven robust M-LS-SVR-based NARX modeling for estimation and control of molten iron quality indices in blast furnace ironmaking . IEEE Trans Neur Netw Learn Syst , 29 ( 9 ): 4007 - 4021 . doi: 10.1109/TNNLS.2017.2749412 http://doi.org/10.1109/TNNLS.2017.2749412

Zhou P , Wang CY , Li MJ , et al. , 2018d . Modeling error PDF optimization based wavelet neural network modeling of dynamic system and its application in blast furnace ironmaking . Neurocomputing , 285 : 167 - 175 . doi: 10.1016/j.neucom.2018.01.040 http://doi.org/10.1016/j.neucom.2018.01.040

Zhou P , LI JL , Wen QQ , et al. , 2018e . Soft-sensing method of cohesive zone shape and position in blast furnace shaft . IFAC-PapersOnLine , 51 ( 21 ): 48 - 52 . doi: 10.1016/j.ifacol.2018.09.391 http://doi.org/10.1016/j.ifacol.2018.09.391

Zhou P , Jiang Y , Wen CY , et al. , 2019 . Data modeling for quality prediction using improved orthogonal incremental random vector functional-link networks . Neurocomputing , 365 : 1 - 9 . doi: 10.1016/j.neucom.2019.06.062 http://doi.org/10.1016/j.neucom.2019.06.062

Zhou P , Li WP , Wang H , et al. , 2020 . Robust online sequential RVFLNs for data modeling of dynamic time-varying systems with application of an ironmaking blast furnace . IEEE Trans Cybern , 50 ( 11 ): 4783 - 4795 . doi: 10.1109/TCYB.2019.2920483 http://doi.org/10.1109/TCYB.2019.2920483

Zhou P , Chen WQ , Yi CM , et al. , 2021a . Fast just-in-time-learning recursive multi-output LSSVR for quality prediction and control of multivariable dynamic systems . Eng Appl Artif Intell , 100 : 104168 . doi: 10.1016/j.engappai.2021.104168 http://doi.org/10.1016/j.engappai.2021.104168

Zhou P , Jiang Y , Wen CY , et al. , 2021b . Improved incremental RVFL with compact structure and its application in quality prediction of blast furnace . IEEE Trans Ind Inform , 17 ( 12 ): 8324 - 8334 . doi: 10.1109/TII.2021.3069869 http://doi.org/10.1109/TII.2021.3069869

Zhu HY , He BC , Zhang XM , 2022 . Multi-gate mixture-of-experts stacked autoencoders for quality prediction in blast furnace ironmaking . ACS Omega , 7 ( 45 ): 41296 - 41303 . doi: 10.1021/acsomega.2c05029 http://doi.org/10.1021/acsomega.2c05029

浏览量

239

Downloads

CSCD

文章被引用时，请邮件提醒。

Submit

工具集

关联资源

Advances and challenges in artificial intelligence text generation

A review on cyber security named entity recognition

A review of computer graphics approaches to urban modeling from a machine learning perspective

MltAuxTSPP: a unified benchmark for deep learning-based traffic state prediction with multi-source auxiliary data

Q-space-coordinate-guided neural networks for high-fidelity diffusion tensor estimation from minimal diffusion-weighted images