Full Text:
<7235>
Summary: 
<551>
CLC number: TP277
On-line Access: 2024-08-27
Received: 2023-10-17
Revision Accepted: 2024-05-08
Crosschecked: 2022-12-15
Cited: 0
Clicked: 4219
Citations: Bibtex RefMan EndNote GB/T7714
Data-driven soft sensors in blast furnace ironmaking: a survey
| ||||||||||||||
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.
[1]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.
[2]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).
[3]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.
[4]Azadi P, Minaabad SA, Bartusch H, et al., 2020. Nonlinear prediction model of blast furnace operation status. Comput Aided Chem Eng, 48:217-222.
[5]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.
[6]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.
[7]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.
[8]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.
[9]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.
[10]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.
[11]Chu YX, Gao CH, 2014. Data-based multiscale modeling for blast furnace system. AIChE J, 60(6):2197-2210.
[12]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.
[13]Ding ZY, Zhang J, Liu Y, 2017. Ensemble non-Gaussian local regression for industrial silicon content prediction. ISIJ Int, 57(11):2022-2027.
[14]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.
[15]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.
[16]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.
[17]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.
[18]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.
[19]Fortuna L, Graziani S, Rizzo A, et al., 2007. Soft Sensors for Monitoring and Control of Industrial Processes. Springer, London, UK.
[20]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.
[21]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.
[22]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.
[23]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.
[24]Geerdes M, Chaigneau R, Lingiardi O, 2020. Modern Blast Furnace Ironmaking: an Introduction (4th Ed.). IOS Press, Amsterdam, the Netherlands.
[25]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.
[26]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.
[27]Hashimoto Y, Kitamura Y, Ohashi T, et al., 2019a. Transient model-based operation guidance on blast furnace. Contr Eng Pract, 82:130-141.
[28]Hashimoto Y, Okamoto Y, Kaise T, et al., 2019b. Practical operation guidance on thermal control of blast furnace. ISIJ Int, 59(9):1573-1581.
[29]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.
[30]He X, Ji J, Liu KX, et al., 2019. Soft sensing of silicon content via bagging local semi-supervised models. Sensors, 19(17):3814.
[31]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.
[32]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.
[33]Jian L, Gao CH, 2013. Binary coding SVMs for the multiclass problem of blast furnace system. IEEE Trans Ind Electron, 60(9):3846-3856.
[34]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.
[35]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.
[36]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.
[37]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.
[38]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.
[39]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.
[40]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.
[41]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.
[42]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.
[43]Kramer MA, 1991. Nonlinear principal component analysis using autoassociative neural networks. AIChE J, 37(2):233-243.
[44]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.
[45]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.
[46]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.
[47]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.
[48]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.
[49]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.
[50]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.
[51]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.
[52]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.
[53]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.
[54]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).
[55]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.
[56]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.
[57]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.
[58]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.
[59]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.
[60]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.
[61]Li YR, Yang CJ, 2021. Domain knowledge based explainable feature construction method and its application in ironmaking process. Eng Appl Artif Intell, 100:104197.
[62]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.
[63]Liu Y, Gao Z, 2015. Enhanced just-in-time modelling for online quality prediction in BF ironmaking. Ironmak Steelmak, 42(5):321-330.
[64]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.
[65]Liu Y, Fan Y, Chen JH, 2017. Flame images for oxygen content prediction of combustion systems using DBN. Energy Fuels, 31(8):8776-8783.
[66]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.
[67]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.
[68]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.
[69]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.
[70]Masson MH, Canu S, Grandvalet Y, et al., 1999. Software sensor design based on empirical data. Ecol Modell, 120(2-3):131-139.
[71]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.
[72]Papadopoulos G, Edwards PJ, Murray AF, 2001. Confidence estimation methods for neural networks: a practical comparison. IEEE Trans Neur Netw, 12(6):1278-1287.
[73]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.
[74]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.
[75]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.
[76]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.
[77]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.
[78]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).
[79]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.
[80]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.
[81]Sun QQ, Ge ZQ, 2021. A survey on deep learning for data-driven soft sensors. IEEE Trans Ind Inform, 17(9):5853-5866.
[82]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.
[83]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.
[84]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.
[85]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.
[86]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).
[87]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.
[88]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.
[89]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.
[90]Xie J, Zhou P, 2020. Robust stochastic configuration network multi-output modeling of molten iron quality in blast furnace ironmaking. Neurocomputing, 387:139-149.
[91]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.
[92]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.
[93]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.
[94]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.
[95]Yu X, Tan C, 2022. China's pathway to carbon neutrality for the iron and steel industry. Glob Environ Change, 76:102574.
[96]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.
[97]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.
[98]Zeng JS, Gao CH, Su HY, 2010b. Data-driven predictive control for blast furnace ironmaking process. Comput Chem Eng, 34(11):1854-1862.
[99]Zhai XY, Chen MT, Lu WC, 2020. Fuel ratio optimization of blast furnace based on data mining. ISIJ Int, 60(11):2471-2476.
[100]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.
[101]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.
[102]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.
[103]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.
[104]Zhang XM, Kano M, Matsuzaki S, 2019b. Ensemble pattern trees for predicting hot metal temperature in blast furnace. Comput Chem Eng, 121:442-449.
[105]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.
[106]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.
[107]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.
[108]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.
[109]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.
[110]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.
[111]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.
[112]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.
[113]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.
[114]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).
[115]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.
[116]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.
[117]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.
[118]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.
[119]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.
[120]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.
[121]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.
[122]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.
[123]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.
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"
}
%0 Journal Article
%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
%N 3
%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
Click to get updates and verify authenticity.
ORCID: 
Open peer comments: Debate/Discuss/Question/Opinion
<1>