Abstract
This paper investigates the application of multivariable model-based control to improve the regulatory control of electrolyte temperature, aluminum fluoride concentration, liquidus temperature, superheat, and electrolyte height. Also examined are therappropriateness of different control structures and the possible inclusion of recently developed sensors for alumina concentration and individual cell duct flowrate, temperature, and heat loss. For the smelter in this study, the maximum improvement possible with a multivariable model-based controller is predicted to be 30–40% reduction in standard deviation in electrolyte temperature, aluminum fluoride concentration, liquidus temperature, and superheat, and around half this for electrolyte height. Three control structures were found to be appropriate; all are different than the existing control structure, which was found to be suboptimal. Linear Quadratic Gaussian controllers were designed for each control structure and their predicted performance compared.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
F.J. Stevens McFadden, “Multivariable Model-Based Control of the Non-Alumina Electrolyte Variables in Aluminium Smelting Cells” (Ph.D. thesis, University of Auckland, New Zealand, 2005).
V.K. Mann et al., Light Metals 1998, ed. L.G. Boxall (Warrendale, PA: TMS, 1998), pp. 371–377.
A.G. Barantsev et al., Light Metals 2000, ed. R.D. Peterson (Warrendale, PA TMS, 2000), pp. 315–321.
V. Yurkov et al., Light Metals 2002, ed. W. Schneider (Warrendale PA: TMS, 2002), pp. 383–388.
T. Rieck et al., Light Metals 2003, ed. P.N. Crepeau (Warrendale, PA: TMS, 2003), pp. 449–456.
P.M. Entner, Light Metals 1992, ed. E.R. Cutshall (Warrendale, PA: TMS, 1992), pp. 369–374.
P.M. Entner, Light Metals 1993, ed. S.K. Das (Warrendale, PA: TMS, 1993), pp. 257–263.
A. Meghlaoui, Y.A.A. Farsi, and N.H. Aljabri, Light Metals 2002, ed. W. Schneider (Warrendale, PA: TMS, 2002), pp. 283–287.
A. Meghlaoui and N.H. Aljabri, Light Metals 2003, ed. P.N. Crepeau (Warrendale, PA: TMS, 2003), pp. 425–429.
P.M. Entner, Light Metals 1995, ed. J.W. Evans (Warrendale, PA: TMS, 1995), pp. 227–230.
P.M. Entner and G.A. Guömundson, Light Metals 1996, ed. W. Hale (Warrendale, PA: TMS, 1996), pp. 445–449.
K.R. Kloetstra et al., Proceedings of the Seventh Australasian Aluminium Smelting Technology Conference and Workshop (Sydney NSW, Australia: Centre for Electrochemical and Minerals Processing, University of New South Wales, 2001).
R.G. Haverkamp, B.J. Welch, and A. McMullen, Light Metals 2001, ed. J.L. Anjier (Warrendale, PA: TMS, 2001), pp. 1193–1194.
M.D. Gadd, “Aluminium Smelter Cell Energy Flow Monitoring” (Ph.D. thesis, University of Auckland, New Zealand, 2003).
L. Ljung, System Identification: Theory for the User, 2 ed. (Upper Saddle River, NJ, USA: Prentice Hall PTR, 1999).
L. Ljung, J. System Identification Toolbox: For Use with Matlab: Users Guide v5 (Natick, MA: The Mathworks Inc., 2000).
B.D. Anderson and J.B. Moore, Linear Optimal Control (Englewood Cliffs, NJ, USA: Prentice-Hall Inc, 1971).
IEEE, Special issue on the LQG problem. IEEE Transactions on Automatic Control, (AC-16) (1971), pp. 527–869.
H. Kwakernaak and R. Sivan, Linear Optimal Control Systems (New York: Wiley-Interscience, 1972).
Author information
Authors and Affiliations
Additional information
Comalco Research.
Rights and permissions
About this article
Cite this article
McFadden, F.J.S., Welch, B.J. & Austin, P.C. The multivariable model-based control of the non-alumina electrolyte variables in aluminum smelting cells. JOM 58, 42–47 (2006). https://doi.org/10.1007/s11837-006-0008-x
Issue Date:
DOI: https://doi.org/10.1007/s11837-006-0008-x