Energy Saving in a Copper Smelter by means of Model Predictive Control

  • Carlos Bordons
  • Manuel R. Arahal
  • Eduardo F. Camacho
  • José M. Tejera

Abstract

This chapter presents an application of advanced control techniques on a copper smelter. The main objective of the control strategy is to keep the gas-circuit pressure at its desired value while achieving energy saving. Another objective of the control strategy is to reduce the risk of emissions. This chapter describes the design and implementation of the gas-circuit control.

The design phase includes an identification procedure. This is a multivariable process where a thorough analysis is needed for input-output matching. The identification phase included the determination of the best input-output pairing.

The control strategy has been devised taking into account not only system performance but also implementation issues. The designed controller runs on a distributed control system (DCS) using the available single-loop blocks and is able to perform a predictive control strategy with feedforward action using existing PID and lead-lag blocks.

Keywords

Energy Saving Model Predictive Control Copper Smelter Disturbance Rejection Predictive Controller 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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References

  1. [1]
    Bergh, L., Chacana, P., Achurra, G., and Delgado, P. (2002). Improvements in the control of the injection system of copper concentrate in teniente converters. Minerals Engineering 15, 369–372.CrossRefGoogle Scholar
  2. [2]
    Bordons, C. and Camacho, E. (1998). Generalized Predictive Controller for a Wide Class of Industrial Process. IEEE Transaction on Control Systems Technology 6(3), 372–387.CrossRefGoogle Scholar
  3. [3]
    Camacho, E. and Bordons, C. (2004). Model Predictive Control in the Process Industry. Second Edition. Springer-Verlag, London.Google Scholar
  4. [4]
    Clarke, D., Mohtadi, C., and Tuffs, P. (1987). Generalized Predictive Control. Part I. The Basic Algorithm. Automatica 23(2), 137–148.MATHCrossRefGoogle Scholar
  5. [5]
    Jämsä-Jounela, S. (2001). Current status and future trends in the automation of mineral and metal processing. Control Engineering Practice 9(9), 1019–1024.CrossRefGoogle Scholar
  6. [6]
    Maciejowski, J. (2001). Predictive Control with Constraints. Prentice Hall, Harlow. ISBN 0-201-39823-0.MATHGoogle Scholar
  7. [7]
    Moskalyk, R. and Alfantazi, A. (2003). Review of copper pyrometallurgical practice: today and tomorrow. Minerals Engineering 16, 893–919.CrossRefGoogle Scholar
  8. [8]
    Qin, S. and Badgwell, T. (2001). A survey of industrial model predictive control technoloy. Control Engineering Practice 11, 733–764.CrossRefGoogle Scholar
  9. [9]
    Richet, D., Cotaina, N., Gabriel, M., and O’Reilly, K. (1995). Application of reliability centred maintenance in the foundry sector. Control Engineering Practice Issue 3, 1029–1034.CrossRefGoogle Scholar
  10. [10]
    Tenno, R. and Jämsä-Jounela, S.-L. (1996). Copper flotation profit and control-system accuracy. Control Engineering Practice 4, 1545–1551.CrossRefGoogle Scholar

Copyright information

© Springer-Verlag London Limited 2007

Authors and Affiliations

  • Carlos Bordons
    • 1
  • Manuel R. Arahal
    • 1
  • Eduardo F. Camacho
    • 1
  • José M. Tejera
    • 2
  1. 1.Atlantic Copper. Departamento de Ingeniería de Sistemas y AutomáticaUniversidad de SevillaSpain
  2. 2.Departamento de Servicios GeneralesElectricidad e InstrumentaciónHuelvaSpain

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