The large-scale blast furnace ironmaking system, characterized by extremely complicated mechanism, multiphase/field coupling, dynamical working circumstances and unbalanced data set, is facing several problems in information detecting, object modelling, safety manufacturing and operation controlling. How to keep blast furnace in a secure and steady status, i.e., ensuring high efficiency and safety of ironmaking process under various conditions has become a major issue in operational control of industrial system. Many scholars have tried to improve the operation control level of large-scale blast furnace. However, the existing research mainly focuses on individual processes of the blast furnace, lacking studies on intelligent coordinated optimization of the entire ironmaking process, including raw material yard, sintering, and blast furnace operations. In order to help researchers to have a better understanding of the ironmaking process, we have made a comprehensive review of the current developments and future trends in the research of large-scale blast furnace. In this paper, we first introduce the backgrounds and characteristics of ironmaking process, as well as analyze the challenges in different research fields. Then, key technologies and current progress of information perception, feature modelling, fault diagnosis and optimal control in large-scale blast furnace are summarized. Furthermore, the future developments and potential applications of blast furnace ironmaking process are outlined in the end.
Published in |
Industrial Engineering (Volume 7, Issue 1)
This article belongs to the Special Issue Intelligent Optimization of High Energy Consumption Processes |
DOI | 10.11648/j.ie.20230701.12 |
Page(s) | 7-20 |
Creative Commons |
This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited. |
Copyright |
Copyright © The Author(s), 2023. Published by Science Publishing Group |
Blast Furnace, Information Perception, Process Modelling, Fault Diagnosis, Optimal Control
[1] | Zhou H, Yang C, Sun Y. Intelligent ironmaking optimization service on a cloud computing platform by digital twin [J]. Engineering, 2021, 7 (9): 1274-1281. doi: 10.1016/j.eng.2021.04.022. |
[2] | Zhou H, Yang C, Liu W, et al. A sliding-window ts fuzzy neural network model for prediction of silicon content in hot metal [J]. IFAC-PapersOnLine, 2017, 50 (1): 14988-14991. doi: 10.1016/j.ifacol.2017.08.2564. |
[3] | Rieger J, Colla V, Matino I, et al. Residue valorization in the iron and steel industries: sustainable solutions for a cleaner and more competitive future Europe [J]. Metals, 2021, 11 (8): 1202. doi: 10.3390/met11081202. |
[4] | Langsdorf S. EU Energy Policy: from the ECSC to the Energy Roadmap 2050. Green European Foundation. 2019. |
[5] | Karakaya E, Nuur C, Assbring L. Potential transitions in the iron and steel industry in Sweden: towards a hydrogen-based future? [J]. Journal of cleaner production, 2018, 195: 651-663. doi: 10.1016/j.jclepro.2018.05.142. |
[6] | Noh I, Jeong C, Choi Y J, et al. Automatic level and bender control for hot finishing mill using flatness measurement of steel strip [C]. IEEE Conference on Control Technology and Applications (CCTA). 2017: 1218-1222. doi: 10.1109/CCTA.2017.8062625. |
[7] | Li Y, Yang C. Domain knowledge based explainable feature construction method and its application in ironmaking process [J]. Engineering Applications of Artificial Intelligence, 2021, 100: 104197. doi: 10.1016/j.engappai.2021.104197. |
[8] | Huang J, Jiang Z, Gui W, et al. Depth estimation from a single image of blast furnace burden surface based on edge defocus tracking [J]. IEEE Transactions on Circuits and Systems for Video Technology, 2022, 32 (9): 6044-6057. doi: 10.1109/TCSVT.2022.3155626. |
[9] | Li J, Hua C, Yang Y, et al. A novel MIMO T–S fuzzy modeling for prediction of blast furnace molten iron quality with missing outputs [J]. IEEE Transactions on Fuzzy Systems, 2020, 29 (6): 1654-1666. doi: 10.1109/TFUZZ.2020.2983667. |
[10] | Zhou B, Ye H, Zhang H, et al. Process monitoring of iron-making process in a blast furnace with PCA-based methods [J]. Control engineering practice, 2016, 47: 1-14. doi: 10.1016/j.conengprac.2015.11.006. |
[11] | Zhou H, Zhang H, Yang C. Hybrid-model-based intelligent optimization of ironmaking process [J]. IEEE Transactions on Industrial Electronics, 2019, 67 (3): 2469-2479. doi: 10.1109/TIE.2019.2903770. |
[12] | Zhou P, Li W, Wang H, et al. Robust online sequential RVFLNs for data modeling of dynamic time-varying systems with application of an ironmaking blast furnace [J]. IEEE transactions on cybernetics, 2019, 50 (11): 4783-4795. doi: 10.1109/TCYB.2019.2920483. |
[13] | Chen I W, Wang X H. Sintering dense nanocrystalline ceramics without final-stage grain growth [J]. Nature, 2000, 404 (6774): 168-171. doi: 10.1038/35004548. |
[14] | Madduri A V R, Landis C R, Blackmon M B, et al. Enhanced binders for iron ore pelleting and cement adhesive materials: U.S. Patent Application 16/099, 558 [P]. 2019-5-23. |
[15] | Sanderson R A. Optimising blends of blast furnace slag for the immobilisation of nuclear waste [D]. University of Sheffield, 2019. |
[16] | Mousa E, Lundgren M, Sundqvist Ökvist L, et al. Reduced carbon consumption and CO 2 emission at the blast furnace by use of briquettes containing torrefied sawdust [J]. Journal of Sustainable Metallurgy, 2019, 5: 391-401. doi: 10.1007/s40831-019-00229-7. |
[17] | Nie H, Li Z, Kuang S, et al. Numerical investigation of oxygen-enriched operations in blast furnace ironmaking [J]. Fuel, 2021, 296: 120662. doi: 10.1016/j.fuel.2021.120662. |
[18] | Jiang Z, Yin J, Gui W, et al. Prediction for blast furnace silicon content in hot metal based on composite differential evolution algorithm and extreme learning machine [J]. Control Theory & Applications, 2016, 33 (8): 1089-1095. |
[19] | Zhang H, Chen M, Xi X, et al. Remaining useful life prediction for degradation processes with long-range dependence [J]. IEEE Transactions on Reliability, 2017, 66 (4): 1368-1379. doi: 10.1109/TR.2017.2720752. |
[20] | Usamentiaga R, Molleda J, Garcia D F, et al. Temperature measurement of molten pig iron with slag characterization and detection using infrared computer vision [J]. IEEE Transactions on Instrumentation and Measurement, 2011, 61 (5): 1149-1159. doi: 10.1109/TIM.2011.2178675. |
[21] | Zhou H, Zhang H, Yang C, et al. Deep learning based silicon content estimation in ironmaking process [J]. IFAC-PapersOnLine, 2020, 53 (2): 10737-10742. doi: 10.1016/j.ifacol.2020.12.2854. |
[22] | Zhang L, Zhang J, Zuo H, et al. Temperature field distribution of a dissected blast furnace [J]. ISIJ International, 2019, 59 (6): 1027-1032. doi: 10.2355/isijinternational.ISIJINT-2018-753. |
[23] | Nogami H, Chu M, Yagi J. Multi-dimensional transient mathematical simulator of blast furnace process based on multi-fluid and kinetic theories [J]. Computers & chemical engineering, 2005, 29 (11-12): 2438-2448. doi: 10.1016/j.compchemeng.2005.05.024. |
[24] | Yang K, Choi S, Chung J, et al. Numerical modeling of reaction and flow characteristics in a blast furnace with consideration of layered burden [J]. ISIJ international, 2010, 50 (7): 972-980. doi: 10.2355/isijinternational.50.972. |
[25] | Zhou P, Guo D, Wang H, et al. Data-driven robust M-LS-SVR-based NARX modeling for estimation and control of molten iron quality indices in blast furnace ironmaking [J]. IEEE transactions on neural networks and learning systems, 2017, 29 (9): 4007-4021. doi: 10.1109/TNNLS.2017.2749412. |
[26] | Zhang T, Ye H, Zhang H, et al. PCA-LMNN-based fault diagnosis method for ironmaking processes with insufficient faulty data [J]. ISIJ International, 2016, 56 (10): 1779-1788. doi: 10.2355/isijinternational.ISIJINT-2016-101. |
[27] | Zhang H, Shang J, Zhang J, et al. Nonstationary process monitoring for blast furnaces based on consistent trend feature analysis [J]. IEEE Transactions on Control Systems Technology, 2021, 30 (3): 1257-1267. doi: 10.1109/TCST.2021.3105540. |
[28] | Huang H, Luo C, Han B. Prescribed performance fuzzy back-stepping control of a flexible air-breathing hypersonic vehicle subject to input constraints [J]. Journal of Intelligent Manufacturing, 2022, 33 (3): 853-866. doi: 10.1007/s10845-020-01656-0. |
[29] | Kharade S, Sutavani S, Wagh S, et al. Optimal control of probabilistic Boolean control networks: A scalable infinite horizon approach [J]. International Journal of Robust and Nonlinear Control, 2023, 33 (9): 4945-4966. doi: 10.1002/rnc.5909. |
[30] | Kishida M, Cetinkaya A. Risk-aware linear quadratic control using conditional value-at-risk [J]. IEEE Transactions on Automatic Control, 2022, 68 (1): 416-423. doi: 10.1109/TAC.2022.3142131. |
[31] | Agrawal A, Agarwal M K, Kothari A K, et al. A mathematical model to control thermal stability of blast furnace using proactive thermal indicator [J]. Ironmaking & Steelmaking, 2019, 46 (2): 133-140. doi: 10.1080/03019233.2017.1353765. |
[32] | Zhang Y, Zhou P, Lv D, et al. Inverse calculation of burden distribution matrix using B-spline model based PDF control in blast furnace burden charging process [J]. IEEE Transactions on Industrial Informatics, 2022, 19 (1): 317-327. doi: 10.1109/TII.2022.3157641. |
[33] | Zhou H, Yang C, Zhuang T, et al. Multi-objective optimization of operating parameters based on neural network and genetic algorithm in the blast furnace [C]. 36th Chinese Control Conference (CCC). IEEE, 2017: 2607-2610. doi: 10.23919/ChiCC.2017.8027755. |
[34] | Yang C, Zhou H, Li Z. A multi-objective optimization model based on long short-term memory and non-dominated sorting genetic algorithm II [C]. 2017 Chinese Automation Congress (CAC). IEEE, 2017: 1635-1640. doi: 10.1109/CAC.2017.8243030. |
[35] | Wang Z, Wang L. Optimization of Convolutional Long Short-Term Memory Hybrid Neural Network Model Based on Genetic Algorithm for Weather Prediction [C]. 2021 4th International Conference on Information Systems and Computer Aided Education. 2021: 2488-2494. doi: 10.1145/3482632.3487456. |
[36] | Saxen H, Gao C, Gao Z. Data-driven time discrete models for dynamic prediction of the hot metal silicon content in the blast furnace—A review [J]. IEEE Transactions on Industrial Informatics, 2012, 9 (4): 2213-2225. doi: 10.1109/TII.2012.2226897. |
[37] | Tu Z H. Research on quick replacement of cross temperature measuring gun for blast furnace. Machine China, 2015, 22: 185−186. |
[38] | Tang Z H, Tang L X, Yang Y. Blast furnace cross temperature prediction based on data-driven and intelligent optimization. Information and Control, 2014, 43 (3): 355−360. |
[39] | Shi L, Wen Y, Zhao G, et al. Recognition of blast furnace gas flow center distribution based on infrared image processing [J]. Journal of Iron and Steel Research International, 2016, 23 (3): 203-209. doi: 10.1016/S1006-706X(16)30035-8. |
[40] | Gao Z, Gao T. Innovation and practices of blast furnace visualization and simulation technology. China Metallurgy, 2013, 23 (2): 8−14. |
[41] | Deng H, Lu X, Huang Y. Application of color controllable infrared camera monitoring system in the blast furnace. China Instrumentation, 2013, 11: 33−36. |
[42] | Chen X, Ding A, Wu Y. Design and implementation of radar burden imaging system in blast furnace. Metallurgical Industry Automation, 2009, 33 (2): 52−56. |
[43] | Xue Q. Application of three-dimensional laser scanning technology in blast furnace material level measurement. Ironmaking, 2016, 35 (3): 56−59. |
[44] | Kaushik P, Fruehan R J. Mixed burden softening and melting phenomena in blast furnace operation Part 1–X-ray observation of ferrous burden [J]. Ironmaking & steelmaking, 2006, 33 (6): 507-519. doi: 10.1179/174328106X118107. |
[45] | Siddiqui S I, Drnevich V P, Deschamps R J. Time domain reflectometry development for use in geotechnical engineering [J]. Geotechnical Testing Journal, 2000, 23 (1): 9-20. doi: 10.1520/GTJ11119J. |
[46] | Narita K, Inaba S, Shimizu M, Okimoto K, Kobayashi I. Method for estimating geographical distribution of cohesive zone in blast furnace: U. S. Patent 4,378,994. 1983-4-5. |
[47] | Ding Z M, Jiang X, Wei G, Shen F M. Numerical simulation for the influence of cohesive zone shape on distribution of unburned pulverized coal in blast furnace. Journal of Northeastern University (Natural Science), 2018, 39 (9): 1242−1247. |
[48] | Dong X F, Yu A B, Chew S J, et al. Modeling of blast furnace with layered cohesive zone [J]. Metallurgical and Materials Transactions B, 2010, 41: 330-349. doi: 10.1007/s11663-009-9327-y. |
[49] | Zhao H, Huo S, Cheng S. Study on the early warning mechanism for the security of blast furnace hearths [J]. International Journal of Minerals, Metallurgy, and Materials, 2013, 20: 345-353. doi: 10.1007/s12613-013-0733-4. |
[50] | Li Q, Feng M, Chu W, Zou Z. Hearth erosion monitoring model of blast furnace based on boundary movement method. Journal of Northeastern University (Natural Science), 2015, 36 (1): 57−62. |
[51] | Kai Y, Yonglong J, Zhijun H. Coke Ratio Prediction Based on Immune Particle Swarm Neural Networks [J]. The Open Cybernetics & Systemics Journal, 2015, 9 (1). doi: 10.2174/1874110X01509011576. |
[52] | Wang Y H, Zhang H, Jiang Z G, et al. The Research on Iron and Steel Enterprises Operation Model Based on the Characteristics of the Accessories Resources [J]. Applied Mechanics and Materials, 2013, 291: 2955-2959. doi: 10.4028/www.scientific.net/AMM.291-294.2955. |
[53] | Zaïmi S A, Akiyama T, Guillot J B, et al. Sophisticated multi-phase multi-flow modeling of the blast furnace [J]. ISIJ international, 2000, 40 (4): 322-331. doi: 10.2355/isijinternational.40.322. |
[54] | De Castro J A, Da Silva A J, Sasaki Y, et al. A six-phases 3-D model to study simultaneous injection of high rates of pulverized coal and charcoal into the blast furnace with oxygen enrichment [J]. ISIJ international, 2011, 51 (5): 748-758. doi: 10.2355/isijinternational.51.748. |
[55] | Zeng J, Gao C, Liu X, et al. Using non-linear GARCH model to predict silicon content in blast furnace hot metal [J]. Asian Journal of Control, 2008, 10 (6): 632-637. doi: 10.1002/asjc.64. |
[56] | Zhou H, Yang C, Sun Y. A collaborative optimization strategy for energy reduction in ironmaking digital twin [J]. IEEE Access, 2020, 8: 177570-177579. doi: 10.1109/ACCESS.2020.3027544. |
[57] | Xu X, Hua C, Tang Y, et al. Modeling of the hot metal silicon content in blast furnace using support vector machine optimized by an improved particle swarm optimizer [J]. Neural Computing and Applications, 2016, 27: 1451-1461. doi: 10.1007/s00521-015-1951-7. |
[58] | Lin S H I, Li Z, Tao Y U, et al. Model of hot metal silicon content in blast furnace based on principal component analysis application and partial least square [J]. Journal of Iron and Steel Research, International, 2011, 18 (10): 13-16. doi: 10.1016/S1006-706X(12)60015-6. |
[59] | Li J, Hua C, Yang Y, et al. Fuzzy classifier design for development tendency of hot metal silicon content in blast furnace [J]. IEEE Transactions on Industrial Informatics, 2017, 14 (3): 1115-1123. doi: 10.1109/TII.2017.2770177. |
[60] | Gao C, Jian L, Luo S. Modeling of the thermal state change of blast furnace hearth with support vector machines [J]. IEEE Transactions on Industrial Electronics, 2011, 59 (2): 1134-1145. doi: 10.1109/TIE.2011.2159693. |
[61] | Wang W. Application of Bayesian Network to tendency prediction of blast furnace silicon content in hot metal [C]//International Conference on Life System Modeling and Simulation. Berlin, Heidelberg: Springer Berlin Heidelberg, 2007: 590-597. |
[62] | Li Y, Zhang S, Zhang J, et al. Data-driven multiobjective optimization for burden surface in blast furnace with feedback compensation [J]. IEEE Transactions on Industrial Informatics, 2019, 16 (4): 2233-2244. doi: 10.1109/TII.2019.2908989. |
[63] | Zhu Q, Lü C L, Yin Y, et al. Burden distribution calculation of bell-less top of blast furnace based on multi-radar data [J]. Journal of Iron and Steel Research International, 2013, 20 (6): 33-37. doi: 10.1016/S1006-706X(13)60108-9. |
[64] | Fu D, Chen Y, Zhou C Q. Mathematical modeling of blast furnace burden distribution with non-uniform descending speed [J]. Applied mathematical modelling, 2015, 39 (23-24): 7554-7567. doi: 10.1016/j.apm.2015.02.054. |
[65] | Liu S, Zhou Z, Dong K, et al. Numerical investigation of burden distribution in a blast furnace [J]. Steel research international, 2015, 86 (6): 651-661. doi: 10.1002/srin.201400360. |
[66] | Kaushik P, Fruehan R J. Mixed burden softening and melting phenomena in blast furnace operation Part2 - Mechanism of softening and melting and impact on cohesive zone. Ironmaking & Steelmaking, 2006, 33 (6): 520−528. |
[67] | Dong X F, Pinson D, Zhang S J, et al. Gas-powder flow in blast furnace with different shapes of cohesive zone [J]. Applied mathematical modelling, 2006, 30 (11): 1293-1309. doi: 10.1016/j.apm.2006.03.004. |
[68] | Nath N K. Simulation of gas flow in blast furnace for different burden distribution and cohesive zone shape [J]. Materials and manufacturing processes, 2002, 17 (5): 671-681. doi: 10.1081/AMP-120016090. |
[69] | Fu D, Chen Y, Zhao Y, et al. CFD modeling of multiphase reacting flow in blast furnace shaft with layered burden [J]. Applied Thermal Engineering, 2014, 66 (1-2): 298-308. doi: 10.1016/j.applthermaleng.2014.01.065. |
[70] | Li F. Research on expert system for prediction of abnormal blast furnace conditions [Master dissertation], Chongqing University, 2007. |
[71] | Bi X G, Yang X P, Li H Y, Li P. Study on the prediction expert system for abnormal furnace conditions. Henan Metallurgy, 2011, 19 (4): 5−11. |
[72] | Yang T J, Zhang K Q, Zhou Y S, Zuo G Q, Xu J W. An expert system for abnormal status diagnosis of a blast furnace. Journal of University of Science and Technology Beijing, 1991, 13 (2): 104−109. |
[73] | Dong X C, Tu Z Y. Expert system for detection and diagnosis of blast furnace conditions. Industrial Instruments and Automation Devices, 1994, 5: 3−7. |
[74] | Yang X P. Expert system for diagnosis of abnormal blast furnace conditions [Master dissertation], Wuhan University of Science and Technology, 2011. |
[75] | Yi S, Xu Y M, Ma Z W. An expert system for abnormal status diagnosis on blast furnace. Metallurgy Industry Automation, 2002, 1: 15−1. |
[76] | Tian H, Wang A. A novel fault diagnosis system for blast furnace based on support vector machine ensemble [J]. ISIJ international, 2010, 50 (5): 738-742. doi: 10.2355/isijinternational.50.738. |
[77] | Liu L, Wang A, Sha M, et al. Multi-class classification methods of cost-conscious LS-SVM for fault diagnosis of blast furnace [J]. Journal of iron and steel research international, 2011, 18 (10): 17-23. doi: 10.1016/S1006-706X(12)60016-8. |
[78] | Vannucci M, Colla V. Novel classification method for sensitive problems and uneven datasets based on neural networks and fuzzy logic [J]. Applied Soft Computing, 2011, 11 (2): 2383-2390. doi: 10.1016/j.asoc.2010.09.001. |
[79] | Zuo G, Björkman B. Monitoring the blast furnace process using neural networks and knowledge-based system [J]. Steel research, 2001, 72 (4): 115-124. doi: 10.1002/srin.200100094. |
[80] | Wang X, Tang X Y, Hao Z, et al. Real-time Blast Furnace Monitoring based on Temporal Sub-mode Recognition [C]. 2022 IEEE International Instrumentation and Measurement Technology Conference (I2MTC). IEEE, 2022: 1-6. doi: 10.1109/I2MTC48687.2022.9806482. |
[81] | Tong C, Lan T, Yu H, et al. Decentralized modified autoregressive models for fault detection in dynamic processes [J]. Industrial & Engineering Chemistry Research, 2018, 57 (46): 15794-15802. doi: 10.1021/acs.iecr.8b03463. |
[82] | Lu H S, Gao B, Zhao L G, Guo H W, Yang T J. Neural network expert system for blast furnace condition judgment. Journal of University of Science and Technology Beijing, 2002, 24 (3): 276−27. |
[83] | Wang L, Yang C, Sun Y. Multimode process monitoring approach based on moving window hidden Markov model [J]. Industrial & Engineering Chemistry Research, 2018, 57 (1): 292-301. doi: 10.1021/acs.iecr.7b03600. |
[84] | Wang L, Yang C, Sun Y, et al. Effective variable selection and moving window HMM-based approach for iron-making process monitoring [J]. Journal of process control, 2018, 68: 86-95. doi: 10.1016/j.jprocont.2018.04.008. |
[85] | An R, Yang C, Pan Y. Unsupervised change point detection using a weight graph method for process monitoring [J]. Industrial & Engineering Chemistry Research, 2019, 58 (4): 1624-1634. doi: 10.1021/acs.iecr.8b02455. |
[86] | Pan Y, Yang C, An R, et al. Robust principal component pursuit for fault detection in a blast furnace process [J]. Industrial & Engineering Chemistry Research, 2018, 57 (1): 283-291. doi: 10.1021/acs.iecr.7b03338. |
[87] | Vanhatalo E. Multivariate process monitoring of an experimental blast furnace [J]. Quality and reliability engineering international, 2010, 26 (5): 495-508. doi: 10.1002/qre.1070. |
[88] | Shang J, Chen M, Zhang H, et al. Increment-based recursive transformed component statistical analysis for monitoring blast furnace iron-making processes: An index-switching scheme [J]. Control Engineering Practice, 2018, 77: 190-200. doi: 10.1016/j.conengprac.2018.05.012. |
[89] | Radhakrishnan V R, Ram K M. Mathematical model for predictive control of the bell-less top charging system of a blast furnace [J]. Journal of Process Control, 2001, 11 (5): 565-586. doi: 10.1016/S0959-1524(00)00026-3. |
[90] | Wu M, Zhang K, An J, et al. An energy efficient decision-making strategy of burden distribution for blast furnace [J]. Control Engineering Practice, 2018, 78: 186-195. doi: 10.1016/j.conengprac.2018.06.019. |
[91] | Yang Y, Yin Y, Wunsch D, et al. Development of blast furnace burden distribution process modeling and control [J]. ISIJ International, 2017, 57 (8): 1350-1363. doi: 10.2355/isijinternational.ISIJINT-2017-002. |
[92] | Li X L, Liu D X, Jia C, et al. Multi-model control of blast furnace burden surface based on fuzzy SVM [J]. Neurocomputing, 2015, 148: 209-215. doi: 10.1016/j.neucom.2013.09.067. |
[93] | Murao A, Kashihara Y, Oyama N, et al. Development of control techniques for mixing small coke at bell-less top blast furnace [J]. ISIJ International, 2015, 55 (6): 1172-1180. doi: 10.2355/isijinternational.55.1172. |
[94] | Zhou P, Sun X, Chai T. Enhanced NMPC for Stochastic Dynamic Systems Driven by Control Error Compensation With Entropy Optimization [J]. IEEE Transactions on Control Systems Technology, 2023. doi: 10.1109/TCST.2023.3291552. |
[95] | Radhakrishnan V R, Mohamed A R. Neural networks for the identification and control of blast furnace hot metal quality [J]. Journal of process control, 2000, 10 (6): 509-524. doi: 10.1016/S0959-1524(99)00052-9. |
[96] | Gao C, Jian L, Liu X, et al. Data-driven modeling based on volterra series for multidimensional blast furnace system [J]. IEEE transactions on neural networks, 2011, 22 (12): 2272-2283. doi: 10.1109/TNN.2011.2175945. |
[97] | Wen L, Zhou P, Wang H, et al. Model free adaptive predictive control of multivariate molten iron quality in blast furnace ironmaking [C]. 2018 IEEE Conference on Decision and Control (CDC). IEEE, 2018: 2617-2622. doi: 10.1109/CDC.2018.8619757. |
[98] | Barbasova T A, Filimonova A A. Predictive control of thermal state of blast furnace [C]//Journal of Physics: Conference Series. IOP Publishing, 2018, 1015 (3): 032012. doi: 10.1088/1742-6596/1015/3/032012. |
[99] | Zhang X, Kano M, Matsuzaki S. Pattern trees modeling for prediction and control of hot metal temperature in blast furnace ironmaking [C]. 2017 11th Asian Control Conference (ASCC). IEEE, 2017: 2292-2297. doi: 10.1109/ASCC.2017.8287532. |
[100] | Naito M, Okamoto A, Yamaguchi K, et al. Improvement of blast furnace reaction efficiency by the temperature control of thermal reserve zone [J]. Shinnittetsu Giho, 2006, 384: 95. |
[101] | Wu P, Yang C J. Identification and control of blast furnace gas top pressure recovery turbine unit [J]. ISIJ international, 2012, 52 (1): 96-100. doi: 10.2355/isijinternational.52.96. |
[102] | An J, Yang J, Wu M, et al. Decoupling control method with fuzzy theory for top pressure of blast furnace [J]. IEEE Transactions on Control Systems Technology, 2018, 27 (6): 2735-2742. doi: 10.1109/TCST.2018.2862859. |
[103] | Li X, Wang K, Jia C. Data-driven control of ground-granulated blast-furnace slag production based on ioem-elm [J]. IEEE Access, 2019, 7: 60650-60660. doi: 10.1109/ACCESS.2019.2915925. |
[104] | Wang H, Sheng C, Lu X. Knowledge-based control and optimization of blast furnace gas system in steel industry [J]. IEEE Access, 2017, 5: 25034-25045. doi: 10.1109/ACCESS.2017.2763630. |
[105] | Rieger J, Weiss C, Rummer B. Modelling and control of pollutant formation in blast stoves [J]. Journal of Cleaner Production, 2015, 88: 254-261. doi: 10.1016/j.jclepro.2014.07.028. |
[106] | Agrawal A, Kor S C, Nandy U, et al. Real-time blast furnace hearth liquid level monitoring system [J]. Ironmaking & Steelmaking, 2016, 43 (7): 550-558. doi: 10.1080/03019233.2015.1127451. |
[107] | Liu X, Feng H, Chen L, et al. Hot metal yield optimization of a blast furnace based on constructal theory [J]. Energy, 2016, 104: 33-41. doi: 10.1016/j.energy.2016.03.113. |
[108] | Wu D, Zhou P, Zhou C Q. Evaluation of pulverized coal utilization in a blast furnace by numerical simulation and grey relational analysis [J]. Applied Energy, 2019, 250: 1686-1695. doi: 10.1016/j.apenergy.2019.05.051. |
[109] | Zhou P, Wang C, Li M, et al. Modeling error PDF optimization based wavelet neural network modeling of dynamic system and its application in blast furnace ironmaking [J]. Neurocomputing, 2018, 285: 167-175. doi: 10.1016/j.neucom.2018.01.040. |
[110] | Liu X, Qin X, Chen L, et al. CO2 emission optimization for a blast furnace considering plastic injection [J]. International Journal of Energy and Environment, 2015, 6 (2): 175. |
[111] | Zhang Y, Zhou P, Cui G. Multi-model based PSO method for burden distribution matrix optimization with expected burden distribution output behaviors [J]. IEEE/CAA Journal of Automatica Sinica, 2018, 6 (6): 1506-1512. doi: 10.1109/JAS.2018.7511090. |
[112] | Hsu K W, Ko Y C. Analysis of Operation Performance of Blast Furnace With Machine Learning Methods [M]. Utilizing Big Data Paradigms for Business Intelligence. IGI Global, 2019: 242-269. doi: 10.4018/978-1-5225-4963-5.ch008. |
[113] | Wang H, Cao S, Dong Q, et al. Optimization and control of working parameters of hot blast furnace [C]. MATEC Web of Conferences. EDP Sciences, 2018, 175: 02030. doi: 10.1051/matecconf/201817502030. |
[114] | Mahanta B K, Chakraborti N. Evolutionary data driven modeling and multi objective optimization of noisy data set in blast furnace iron making process [J]. Steel research international, 2018, 89 (9): 1800121. doi: 10.1002/srin.201800121. |
[115] | Chen L, Feng H, Xie Z. Generalized thermodynamic optimization for iron and steel production processes: Theoretical exploration and application cases [J]. Entropy, 2016, 18 (10): 353. doi: 10.3390/e18100353. |
[116] | Yao S, Wu S, Song B, et al. Multi-objective optimization of cost saving and emission reduction in blast furnace ironmaking process [J]. Metals, 2018, 8 (12): 979. doi: 10.3390/met8120979. |
[117] | Zhou H, Li Y, Yang C, et al. Mixed-framework-based energy optimization of chemi-mechanical pulping [J]. IEEE Transactions on Industrial Informatics, 2019, 16 (9): 5895-5904. doi: 10.1109/TII.2019.2963347. |
[118] | Li Z, Yang C, Liu W, et al. Research on hot metal Si-content prediction based on LSTM-RNN [J]. Computers & Industrial Engineering-Journal, 2018, 69 (3): 992-997. doi: 10.11949/j.issn.0438-1157.20171534. |
[119] | Wei L, et al. Insights into Active Sites and Mechanisms of Benzyl Alcohol Oxidation on Nickel–Iron Oxyhydroxide Electrodes. ACS Catalysis, 2023, 13 (7): 4272-4282. doi: 10.1021/acscatal.2c05656. |
[120] | Dong H, Wei L, Tarpeh WA. Electro-assisted regeneration of pH-sensitive ion exchangers for sustainable phosphate removal and recovery. Water Research, 2020, 184: 116167. doi: 10.1016/j.watres.2020.116167. |
[121] | Wei L, et al. Using 2D-Phthalocyanine Metal Organic Framework-Based Catalysts for Oxygen Reduction Reaction in Alkaline Media. Electrochemical Society Meeting Abstracts 242, 2022, 43: 1618-1618. doi: 10.1149/MA2022-02431618mtgabs. |
[122] | Zheng Y, Yao Z, Zhou H, et al. Power generation forecast of top gas recovery turbine unit based on Elman model. 37th Chinese Control Conference (CCC), 2018, 7498-7501. doi: 10.23919/ChiCC.2018.8483666. |
APA Style
Heng Zhou. (2023). Developments and Challenges in High-Performance Operation Control of Large-Scale Blast Furnace. Industrial Engineering, 7(1), 7-20. https://doi.org/10.11648/j.ie.20230701.12
ACS Style
Heng Zhou. Developments and Challenges in High-Performance Operation Control of Large-Scale Blast Furnace. Ind. Eng. 2023, 7(1), 7-20. doi: 10.11648/j.ie.20230701.12
AMA Style
Heng Zhou. Developments and Challenges in High-Performance Operation Control of Large-Scale Blast Furnace. Ind Eng. 2023;7(1):7-20. doi: 10.11648/j.ie.20230701.12
@article{10.11648/j.ie.20230701.12, author = {Heng Zhou}, title = {Developments and Challenges in High-Performance Operation Control of Large-Scale Blast Furnace}, journal = {Industrial Engineering}, volume = {7}, number = {1}, pages = {7-20}, doi = {10.11648/j.ie.20230701.12}, url = {https://doi.org/10.11648/j.ie.20230701.12}, eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ie.20230701.12}, abstract = {The large-scale blast furnace ironmaking system, characterized by extremely complicated mechanism, multiphase/field coupling, dynamical working circumstances and unbalanced data set, is facing several problems in information detecting, object modelling, safety manufacturing and operation controlling. How to keep blast furnace in a secure and steady status, i.e., ensuring high efficiency and safety of ironmaking process under various conditions has become a major issue in operational control of industrial system. Many scholars have tried to improve the operation control level of large-scale blast furnace. However, the existing research mainly focuses on individual processes of the blast furnace, lacking studies on intelligent coordinated optimization of the entire ironmaking process, including raw material yard, sintering, and blast furnace operations. In order to help researchers to have a better understanding of the ironmaking process, we have made a comprehensive review of the current developments and future trends in the research of large-scale blast furnace. In this paper, we first introduce the backgrounds and characteristics of ironmaking process, as well as analyze the challenges in different research fields. Then, key technologies and current progress of information perception, feature modelling, fault diagnosis and optimal control in large-scale blast furnace are summarized. Furthermore, the future developments and potential applications of blast furnace ironmaking process are outlined in the end.}, year = {2023} }
TY - JOUR T1 - Developments and Challenges in High-Performance Operation Control of Large-Scale Blast Furnace AU - Heng Zhou Y1 - 2023/09/06 PY - 2023 N1 - https://doi.org/10.11648/j.ie.20230701.12 DO - 10.11648/j.ie.20230701.12 T2 - Industrial Engineering JF - Industrial Engineering JO - Industrial Engineering SP - 7 EP - 20 PB - Science Publishing Group SN - 2640-1118 UR - https://doi.org/10.11648/j.ie.20230701.12 AB - The large-scale blast furnace ironmaking system, characterized by extremely complicated mechanism, multiphase/field coupling, dynamical working circumstances and unbalanced data set, is facing several problems in information detecting, object modelling, safety manufacturing and operation controlling. How to keep blast furnace in a secure and steady status, i.e., ensuring high efficiency and safety of ironmaking process under various conditions has become a major issue in operational control of industrial system. Many scholars have tried to improve the operation control level of large-scale blast furnace. However, the existing research mainly focuses on individual processes of the blast furnace, lacking studies on intelligent coordinated optimization of the entire ironmaking process, including raw material yard, sintering, and blast furnace operations. In order to help researchers to have a better understanding of the ironmaking process, we have made a comprehensive review of the current developments and future trends in the research of large-scale blast furnace. In this paper, we first introduce the backgrounds and characteristics of ironmaking process, as well as analyze the challenges in different research fields. Then, key technologies and current progress of information perception, feature modelling, fault diagnosis and optimal control in large-scale blast furnace are summarized. Furthermore, the future developments and potential applications of blast furnace ironmaking process are outlined in the end. VL - 7 IS - 1 ER -