A journal of IEEE and CAA , publishes high-quality papers in English on original theoretical/experimental research and development in all areas of automation
Advanced Search
Volume 10 Issue 1
Jan.  2023

IEEE/CAA Journal of Automatica Sinica

  • JCR Impact Factor: 11.8, Top 4% (SCI Q1)
    CiteScore: 17.6, Top 3% (Q1)
    Google Scholar h5-index: 77， TOP 5
Turn off MathJax
Article Contents
Article Navigation >  IEEE/CAA Journal of Automatica Sinica  > 2023  >  10(1): 203-215
I. Birs, C. Muresan, D. Copot, and C. Ionescu, “Model identification and control of electromagnetic actuation in continuous casting process with improved quality,” IEEE/CAA J. Autom. Sinica, vol. 10, no. 1, pp. 203–215, Jan. 2023. doi: 10.1109/JAS.2023.123027
Citation: I. Birs, C. Muresan, D. Copot, and C. Ionescu, “Model identification and control of electromagnetic actuation in continuous casting process with improved quality,” IEEE/CAA J. Autom. Sinica, vol. 10, no. 1, pp. 203–215, Jan. 2023. doi: 10.1109/JAS.2023.123027
shu

Model Identification and Control of Electromagnetic Actuation in Continuous Casting Process With Improved Quality

doi: 10.1109/JAS.2023.123027
  • 1.

    Research Group of Dynamical Systems and Control, Faculty of Engineering and Architecture, Ghent University and with EEDT Decision & Control, Flanders Make Consortium, Tech Lane Science Park 125 and 131, B-9052 Ghent, Belgium. I. Birs and C. Ionescu are also with the Automation Department, Faculty of Automation and Applied Informatics, Technical University of Cluj-Napoca, Romania

  • 2.

    Automation Department, Faculty of Automation and Applied Informatics, Technical University of Cluj-Napoca, Romania

Funds:  This work was supported by Research Foundation Flanders (FWO) (1S04719N, 12X6819N), and partially supported by a grant of the Ministry of Research, Innovation and Digitization, CNCS - UEFISCDI, project number PN-III-P1-1.1-PD-2021-0204, within PNCDI III
More Information
    Abstract
  • This paper presents an original theoretical framework to model steel material properties in continuous casting line process. Specific properties arising from non-Newtonian dynamics are herein used to indicate the natural convergence of distributed parameter systems to fractional order transfer function models. Data driven identification from a real continuous casting line is used to identify model of the electromagnetic actuator device to control flow velocity of liquid steel. To ensure product specifications, a fractional order control is designed and validated on the system. A projection of the closed loop performance onto the quality assessment at end production line is also given in this paper.

     

    • FullText(HTML)
  • loading
    • References(67)
  • [1]
    B. G. Thomas, “Review on modeling and simulation of continuous casting,” Steel Research Int., vol. 89, p. 1700312, 2018.
    [2]
    M. Jeyakumar, M. Hamed, and S. Shankar, “Rheology of liquid metals and alloys,” Journal of Non-Newtonian Fluid Mechanics, vol. 166, no. 14, pp. 831–838, 2011.
    [3]
    R. Golani, V. Singh, S. K. Ajmani, and R. Ranjan, “Investigation of surface flow velocity and vortex formation flow patterns in continuous slab casting mold through physical modelling,” 2015, pp. 30–36.
    [4]
    H. Xiao, S. xiang Li, P. Wang, H. Y. Tang, and J. Q. Zhang, “Study on the in-mold flow behavior driven by a subsurface electromagnetic stirring for if steel slab casting,” vol. 5, 2021, pp. 590–601.
    [5]
    X. Deng, C. Ji, Y. Cui, L. Li, X. Yin, Y. Yang, and A. McLean, “Flow pattern control in continuous slab casting moulds: Physical modelling and plant trials,” Ironmaking and Steelmaking, vol. 44, pp. 461–471, 2017. doi: 10.1080/03019233.2016.1215666
    [6]
    I. Birs, C. Muresan, D. Copot, I. Nascu, and C. Ionescu, “Identification for control of suspended objects in non-newtonian fluids,” Fractional Calculus and Applied Analysis, vol. 22, pp. 1378–1394, 2020.
    [7]
    K. Jin, S. Vanka, R. Agarwal, and B. Thomas, “GPU accelerated simulations of three-dimensional flow of power-law fluids in a driven cube,” Int. Journal of Computational Fluid Dynamics, vol. 31, no. 1, pp. 36–56, 2017. doi: 10.1080/10618562.2016.1270449
    [8]
    Z. Wang, B. Mao, R. Zhu, X. Bai, and X. Han, “Fractional maxwell model for oscillating flow of a viscoelastic elastomer shock absorber,” Binggong Xuebao/Acta Armamentarii, vol. 41, pp. 984–995, 2020.
    [9]
    Y. Carrera, G. A. de la Rosa, E. J. Vernon-Carter, and J. Alvarez-Ramirez, “A fractional-order maxwell model for non-newtonian fluids,” Physica A: Statistical Mechanics and its Applications, vol. 482, pp. 276–285, 2017. doi: 10.1016/j.physa.2017.04.085
    [10]
    C. Ionescu and J. F. Kelly, “Fractional calculus for respiratory mechanics: Power law impedance, viscoelasticity, and tissue heterogeneity,” Chaos,Solitons and Fractals, vol. 102, pp. 433–440, 2017. doi: 10.1016/j.chaos.2017.03.054
    [11]
    D. Baleanu and R. P. Agarwal, “Fractional calculus in the sky,” p. 117, 2021.
    [12]
    V. Kiryakova, “A guide to special functions in fractional calculus,” Mathematics, vol. 9, p. 106, 2021.
    [13]
    G. Failla and M. Zingales, “Advanced materials modelling via fractional calculus: Challenges and perspectives,” Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, vol. 378, p. 2172, 2020.
    [14]
    C. A. Monje, Y. Q. Chen, B. M. Vinagre, D. Xue, and V. Feliu, Fractional-Order Systems and Control: Fundamentals and Applications. Springer, 2010.
    [15]
    J. Sabatier, P. Lanusse, P. Melchior, and A. Oustaloup, Fractional Order Differentiation and Robust Control Design. Springer, 2015.
    [16]
    F. Padula and A. Visioli, Advances in Robust Fractional Control. Springer, 2015.
    [17]
    A. Visioli, Practical PID Control. Springer, 2006.
    [18]
    R. Vilanova and A. Visioli, PID Control in the Third Millennium. Springer, 2012.
    [19]
    I. Birs, C. Muresan, I. Nascu, and C. Ionescu, “A survey of recent advances in fractional order control for time delay systems,” IEEE Access, vol. 7, pp. 30951–30965, 2019. doi: 10.1109/ACCESS.2019.2902567
    [20]
    O. Lefevre, “Arcelormittal – Annual report 2020,” 2020, Accessed: 2-1-2021.
    [21]
    S. Louhenkilpi, “Chapter 1.8 – Continuous casting of steel,” in Treatise on Process Metallurgy, S. Seetharaman, Ed. Boston: Elsevier, 2014, pp. 373−434.
    [22]
    J.-F. Domgin, M. Anderhuber, M. De Doncker, and A. De Paepe, “Optimization of an electromagnetic technology in arcelormittal gent for improving products quality in steel industry,” Journal for Manufacturing Science and Production, vol. 15, no. 1, pp. 105–117, 2015. doi: 10.1515/jmsp-2014-0043
    [23]
    M. De Doncker and J. F. Domgin, “Reduction of slivers by the use of an electromagnetic actuator,” Metallurgical Research and Technology, vol. 112, 2015.
    [24]
    S.-M. Cho and B. G. Thomas, “Electromagnetic forces in continuous casting of steel slabs,” Metals, vol. 9, no. 4, p. 471, 2019. doi: 10.3390/met9040471
    [25]
    K. Dekemele, C.-M. Ionescu, M. De Doncker, and R. De Keyser, “Closed loop control of an electromagnetic stirrer in the continuous casting process,” in Proc. European Control Conf., 2016, pp. 61–66.
    [26]
    S. Bouhouche, M. Lahreche, and J. Bast, “Control of heat transfer in continuous casting process using neural networks,” Acta Automatica Sinica, vol. 34, no. 6, pp. 701–706, 2008. doi: 10.1016/S1874-1029(08)60034-8
    [27]
    K. Mills, “Chapter 1.9 – How mold fluxes work,” in Treatise on Process Metallurgy, S. Seetharaman, Ed. Boston: Elsevier, 2014, pp. 435–475.
    [28]
    Q. Yuan, B. Zhao, S. Vanka, and B. Thomas, “Study of computational issues in simulation of transient flow in continuous casting,” Steel Research Int., vol. 76, 2005.
    [29]
    B. Launder and D. Spalding, “The numerical computation of turbulent flows,” Computer Methods in Applied Mechanics and Engineering, vol. 3, no. 2, pp. 269–289, 1974. doi: 10.1016/0045-7825(74)90029-2
    [30]
    R. Chaudhary, C. Ji, B. Thomas, and S. Vanka, “Transient turbulent flow in a liquid-metal model of continuous casting, including comparison of six different methods,” Metallurgical and Materials Transactions B, vol. 42, pp. 987–1007, 2011. doi: 10.1007/s11663-011-9526-1
    [31]
    A. Asad, C. Kratzsch, and R. Schwarze, “Numerical study of the fluid flow and the free surface behavior in a model mold,” 2015.
    [32]
    C. Kratzsch, K. Timmel, S. Eckert, and R. Schwarze, “Urans simulation of continuous casting mold flow: Assessment of revised turbulence models,” Steel Research Int., vol. 86, p. 400, 2014.
    [33]
    R. Liu, B. Thomas, L. Kalra, T. Bhattacharya, and A. Dasgupta, “Slidegate dithering effects on transient flow and mold level fluctuations,” AISTech - Iron and Steel Technology Conf. Proceedings, vol. 2, pp. 1351–1364, 2013.
    [34]
    N. Zeng, Z. Wang, Y. Li, M. Du, and X. Liu, “Identification of nonlinear lateral flow immunoassay state-space models via particle filter approach,” IEEE Trans. Nanotechnology, vol. 11, no. 2, pp. 321–327, 2012. doi: 10.1109/TNANO.2011.2171193
    [35]
    N. Zeng, Z. Wang, and H. Zhang, “Inferring nonlinear lateral flow immunoassay state-space models via an unscented Kalman filter,” Science China Information Sciences, vol. 59, 2016.
    [36]
    N. Zeng, Z. Wang, Y. Li, M. Du, and X. Liu, “Inference of nonlinear state-space models for sandwich-type lateral flow immunoassay using extended Kalman filtering,” Biomedical Engineering,IEEE Trans., vol. 58, pp. 1959–1966, 2011. doi: 10.1109/TBME.2011.2106502
    [37]
    S. Gu, G. Wen, J. Guo, Z. Wang, P. Tang, and Q. Liu, “Effect of shear stress on heat transfer behavior of non-newtonian mold fluxes for peritectic steels slab casting,” ISIJ Int., vol. 60, pp. 1179–1187, 2020. doi: 10.2355/isijinternational.ISIJINT-2019-609
    [38]
    K. C. Mills, “Structure and properties of slags used in the continuous casting of steel: Part 2 specialist mould powders,” ISIJ Int., vol. 56, pp. 14–23, 2016. doi: 10.2355/isijinternational.ISIJINT-2015-355
    [39]
    Y. Deng, Z. Dan, X. Yan, Q. Wang, and S. He, “Investigation of rheological behavior for commercial mold slags,” Journal of Materials Research and Technology, vol. 9, pp. 9568–9575, 2020. doi: 10.1016/j.jmrt.2020.06.082
    [40]
    X. Yan, M. Gan, H. Yuan, Q. Wang, S. He, and Q. Wang, “Study of non-newtonian behavior of cao-sio 2 -based mold slag and its effect on lubrication in continuous casting of steel,” Metallurgical and Materials Transactions B: Process Metallurgy and Materials Processing Science, vol. 50, 2019.
    [41]
    C. Ionescu, I. Birs, D. Copot, C. Muresan, and R. Caponetto, “Mathematical modelling with experimental validation of viscoelastic properties in non-newtonian fluids,” Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, vol. 378, p. 20190284, 2020.
    [42]
    L. Feng, F. Liu, I. Turner, and P. Zhuang, “Numerical methods and analysis for simulating the flow of a generalized Oldroyd-B fluid between two infinite parallel rigid plates,” Int. Journal of Heat and Mass Transfer, vol. 115, pp. 1309–1320, 2017. doi: 10.1016/j.ijheatmasstransfer.2017.08.105
    [43]
    Y. Luchko, “Operational calculus for the general fractional derivative and its applications,” Fractional Calculus and Applied Analysis, vol. 24, pp. 338–375, 2021. doi: 10.1515/fca-2021-0016
    [44]
    G. Ala, M. D. Paola, E. Francomano, Y. Li, and F. P. Pinnola, “Electrical analogous in viscoelasticity,” Communications in Nonlinear Science and Numerical Simulation, vol. 19, pp. 2513–2527, 2014. doi: 10.1016/j.cnsns.2013.11.007
    [45]
    C. Muresan, S. Folea, I. Birs, and C. Ionescu, “A novel fractional-order model and controller for vibration suppression in flexible smart beam,” Nonlinear Dynamics, vol. 93, pp. 525–541, 2018. doi: 10.1007/s11071-018-4207-0
    [46]
    Z. Ali, F. Rabiei, K. Shah, and T. Khodadadi, “Qualitative analysis of fractal-fractional order COVID-19 mathematical model with case study of wuhan,” Alexandria Engineering Journal, vol. 60, 2021.
    [47]
    D. Copot, R. L. Magin, R. D. Keyser, and C. Ionescu, “Data-driven modelling of drug tissue trapping using anomalous kinetics,” Chaos,Solitons and Fractals, vol. 102, pp. 441–446, 2017. doi: 10.1016/j.chaos.2017.03.031
    [48]
    R. Van Durme, G. crevecoeur, L. Dupre, and A. Coene, “Model based optimized steering and focusing of local magnetic particle concentrations for targeted drug delivery,” Drug Delivery, vol. 28, pp. 63–76, 2021. doi: 10.1080/10717544.2020.1853281
    [49]
    K. Saad, A. Atangana, and D. Baleanu, “New fractional derivatives with non-singular kernel applied to the burgers equation,” Chaos: An Interdisciplinary Journal of Nonlinear Science, vol. 28, 2018.
    [50]
    A. Oustaloup, Diversity and Non-integer Differentiation for System Dynamics. Wiley, 2014.
    [51]
    C. Ionescu, The Human Respiratory System: An Analysis of the Interplay Between Anatomy, Structure, Breathing and Fractal Dynamics. Springer, 2013.
    [52]
    F. Ge, Y. Chen, and C. Kou, Regional Analysis of Time-Fractional Diffusion Processes. Springer, 2018.
    [53]
    H. Sun, Y. Zhang, S. Wei, and J. Zhu, “Fractional constitutive equation (face) for non-newtonian fluid flow: Theoretical description,” Fluid Dynamics, pp. 1–17, 2016.
    [54]
    J.-B. Salmon, A. Colin, S. Manneville, and F. Molino, “Velocity profiles in shear-banding wormlike micelles,” Physical review letters, vol. 90, p. 228303, 2003. doi: 10.1103/PhysRevLett.90.228303
    [55]
    D. Geng, H. Lei, J. He, and H. Liu, “Effect of electromagnetic swirling flow in slide-gate SEN on flow field in square billet continuous casting mold,” Acta Mettalurgica Sinica, vol. 25, pp. 347–356, 2012.
    [56]
    K. Cao, Y. Chen, and D. Stuart, “A fractional micro-macro model for crowds of pedestrians based on fractional mean field games,” IEEE/CAA J. Autom. Sinica, vol. 3, no. 3, pp. 261–270, 2016. doi: 10.1109/JAS.2016.7508801
    [57]
    I. Birs, C. Muresan, O. Prodan, S. Folea, and C. Ionescu, “An experimental approach towards motion modeling and control of a vehicle transiting a non-newtonian environment,” Fractal and Fractional, vol. 5, 2021.
    [58]
    I. Schafer and K. Kruger, “Modelling of coils using fractional derivatives,” Journal of Magnetism and Magnetic Materials, vol. 307, 2006.
    [59]
    I. Schafer and K. Krüger, “Modelling of lossy coils using fractional derivatives,” Journal of Physics D: Applied Physics, vol. 41, p. 45001, 2008. doi: 10.1088/0022-3727/41/4/045001
    [60]
    M. Sowa and L. Majka, “Ferromagnetic core coil hysteresis modeling using fractional derivatives,” Nonlinear Dynamics, vol. 101, pp. 775–793, 2020. doi: 10.1007/s11071-020-05811-3
    [61]
    D. Ivanov and A. Zhdanov, “Symmetrical augmented system of equations for the parameter identification of discrete fractional systems by generalized total least squares,” Mathematics, vol. 9, no. 24, 2021.
    [62]
    P. Lino, G. Maione, F. Padula, S. Stasi, and A. Visioli, “Synthesis of fractional-order PI controllers and fractional-order filters for industrial electrical drives,” IEEE/CAA J. Autom. Sinica, vol. 4, no. 1, pp. 58–69, 2017. doi: 10.1109/JAS.2017.7510325
    [63]
    A. Ahmadi Dastjerdi, B. Vinagre, Y. Chen, and H. Hosseinnia, “Linear fractional order controllers: A survey in the frequency domain,” Annual Reviews in Control, pp. 51–70, 04, 2019.
    [64]
    C. I. Muresan, I. R. Birs, C. M. Ionescu, and R. De Keyser, “Tuning of fractional order proportional integral/proportional derivative controllers based on existence conditions,” Proc. the Institution of Mechanical Engineers,Part I: Journal of Systems and Control Engineering, vol. 233, no. 4, pp. 384–391, 2019.
    [65]
    I. Birs, I. Nascu, C. Ionescu, and C. Muresan, “Event-based fractional order control,” Journal of Advanced Research, pp. 191–203, 2020.
    [66]
    J. Sanchez, M. Guinaldo, A. Visioli, and S. Dormido, “Identification of process transfer function parameters in event-based PI control loops,” ISA Transactions, vol. 75, pp. 157–171, 2018. doi: 10.1016/j.isatra.2018.01.033
    [67]
    L. Merigo, M. Beschi, F. Padula, and A. Visioli, “A noise-filtering event generator for PIDplus controllers,” Journal of the Franklin Institute, vol. 355, pp. 774–802, 2018. doi: 10.1016/j.jfranklin.2017.11.041
    • Related Articles
    • Supplements(0)

Catalog

    通讯作者: 陈斌, bchen63@163.com
    • 1. 

      沈阳化工大学材料科学与工程学院 沈阳 110142

    1. 本站搜索
    2. 百度学术搜索
    3. 万方数据库搜索
    4. CNKI搜索

    Figures(17)

    Get Citation
    PDF
    XML

    Article Metrics

    Article views (532) PDF downloads(29) Cited by()

    Highlights

    • theoretical framework for modeling steel material properties in continuous casting line process
    • non-Newtonian dynamics captured using fractional order transfer function models
    • data driven identification using a real continuous casting line
    • design and validation of a fractional order controller to ensure product specifications
    • quality improvement of the final product using a fractional order event-based approach

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return
    Return