Advertisement

Identification of Distillation Systems

  • R. C. McFarlane
  • D. E. Rivera

Abstract

In this chapter we address the problem of identification of distillation systems for the purpose of obtaining models for process control. Much of the control literature has focused on controller synthesis procedures that are derived under the assumption that suitable models are available. Significantly less attention has been paid to the specific problem of defining the requirements of models for process control purposes and how to make the best choices of design variables in identification to obtain them. Among the objectives of this chapter is to survey the available literature in this area and present some ideas and procedures that, when incorporated into the well established methodology for system identification, make the identification more relevant to the needs of process control.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Alsop, A. and Edgar, T. F. (1987). Nonlinear control of a high purity distillation column by the use of partially linearized control variables. AIChE Meeting, Paper 10b.Google Scholar
  2. Äström, K. J. and Hägglund, T. (1984). Automatic tuning of simple regulators with specification on phase amplitude margins. Automatica 20, 645–651.CrossRefGoogle Scholar
  3. Äström, K. J. and Wittenmark, B. (1984). Computer-Controlled Systems: Theory and Design. Englewood Cliffs, NJ: Prentice-Hall.Google Scholar
  4. Äström, K. J. and Wittenmark, B. (1989). Adaptive Control. Reading, MA: Addison-Wesley.Google Scholar
  5. Balhoff, R. and Lau, H. K. (1985). A transfer function form of dynamic matrix control and its relationship with some classical controllers. American Control Conference, Boston.Google Scholar
  6. Box, G. E. P. and Jenkins, G. (1976). Time Series Analysis. San Francisco: Holden-Day.Google Scholar
  7. Box, G. E. P. and MacGregor, J. F. (1974). The analysis of closed-loop dynamic stochastic systems. Technometrics 16, 391.CrossRefGoogle Scholar
  8. Briggs, P. A. N. and Godfrey, K. R. (1966). Pseudorandom signals for the dynamic analysis of multivariable systems. Proc. IEE 113, 1259–1267.Google Scholar
  9. Davies, W. D. T. (1970). System Identification for Self-Adaptive Control. London: Wiley Interscience.Google Scholar
  10. Draper, N. R. and Smith, H. (1966). Applied Regression Analysis. New York: Wiley.Google Scholar
  11. Eduljee, H. E. (1975). Equations replace Gilliland plot. Hydroc. Process. 54, 120.Google Scholar
  12. Eskinat, E., Johnson, S. H., and Luyben, W. L. (1991). The use of auxiliary information in system identification. Submitted to Ind. Eng. Chem. Research.Google Scholar
  13. Eykhoff, P. (1974). System Identification: Parameter and State Estimation. New York: Wiley.Google Scholar
  14. Geladi, P. (1988). Notes on the history and nature of partial least-squares modeling. J. Chemometrics 2, 231–246.CrossRefGoogle Scholar
  15. Geladi, P. and Kowalski, B. R. (1986). Partial least-squares regression: A tutorial. Anal. Chim. Acta 185, 19–32.CrossRefGoogle Scholar
  16. Georgiou, A., Georgakis, C., and Luyben, W. L. (1988). Nonlinear dynamic matrix control for high-purity distillation columns. AIChE J. 34, 1287–1298.CrossRefGoogle Scholar
  17. Gevers, M. and Wertz, V. (1987). Techniques for the selection of identifiable parametrizations for multivariable linear systems. Control and Dynamic Systems 26, 35–86.CrossRefGoogle Scholar
  18. Gustavsson, I., Ljung, L., and Söderström, T. (1977). Identification of processes in closed- loop: Identifiability and accuracy aspects. Automatica 13, 59.CrossRefGoogle Scholar
  19. Hoerl, A. E. and Kennard, R. W. (1970). Ridge regression: Biased estimators for non- orthogonal problems. Technometrics 12, 55–67.CrossRefGoogle Scholar
  20. Hoskuldsson, A. (1988). PLS regression methods. J. Chemometrics 2, 211–228.CrossRefGoogle Scholar
  21. Jenkins, G. and Watts, D. (1969). Spectral Analysis and its Applications. San Francisco: Holden-Day.Google Scholar
  22. Kosut, R. L. (1987). Adaptive uncertainty modeling: On-line robust control design. 1987 American Control Conference, Minneapolis.Google Scholar
  23. Koung, C. W. and Harris, T. J. (1987). Analysis and control of high-purity distillation columns using nonlinearity transformed composition measurements. Canadian Engng. Centennial Conf., Montreal.Google Scholar
  24. Koung, C. W. and MacGregor, J. F. (1991). Design of experiments for robust identification of MIMO systems. 1991 AIChE Annual Meeting, Los Angeles, CA.Google Scholar
  25. Kresta, J. V., MacGregor, J. F., and Marlin, T. E. (1991). Multivariate statistical monitoring of process operating performance. Can. J. Chem. Eng. 69, 35–47.CrossRefGoogle Scholar
  26. Lorber, A., Wangen, L. E., and Kowalski, B. (1987). A theoretical foundation for PLS algorithm. J. Chemometrics 1, 19.CrossRefGoogle Scholar
  27. Ljung, L. (1987). System Identification: Theory for the User. Englewood Cliffs, NJ: Prentice-Hall.Google Scholar
  28. Ljung, L. (1988). System Identification Toolbox, MATLAB. South Natick, MA: The Math-Works, Inc.Google Scholar
  29. Luyben, W. L. (1990). Process Modeling, Simulation and Control for Chemical Engineers, Ed. New York: McGraw-Hill.Google Scholar
  30. Luyben, W. L. (1987). Derivation of transfer functions of highly nonlinear distillation columns. Ind. Eng. Chem. Res. 26,2490–2495.CrossRefGoogle Scholar
  31. MacGregor, J. F., Harris, T. J., and Wright, J. D. (1984). Duality between the control of processes subject to randomly occurring deterministic disturbances and ARIMA disturbances. Technometrics 26, 389.CrossRefGoogle Scholar
  32. MATLAB (1989). South Natick, MA: The Math-Works, Inc.Google Scholar
  33. Morari, M. and Zafiriou, E. (1989). Robust Process Control. Englewood Cliffs, NJ: Prentice- Hall.Google Scholar
  34. Prett, D. M. and Garcia, C. E. (1988). Fundamental Process Control. Boston: Butterworths.Google Scholar
  35. Ricker, N. L. (1988). The use of biased least- squares estimators for parameters in discrete-time pulse response models. Ind. Eng. Chem. Res. 27, 343.CrossRefGoogle Scholar
  36. Rivera, D. E. (1991a). Control-relevant parameter estimation: A systematic procedure for prefilter design. 1991 American Control Conference, Boston.Google Scholar
  37. Rivera, D. E. (1991b). A re-examination of modeling requirements for process control. Unpublished.Google Scholar
  38. Rivera, D. E., Pollard, J. F., and Garcia, C. E. (1990). Control-relevant parameter estimation via prediction-error methods: implications for digital PID and QDMC control. Paper 4a, AIChE Annual Meeting, Chicago.Google Scholar
  39. Rivera, D. E., Pollard, J. F., Sterman, L. E., and Garcia, C. E. (1990). An industrial perspective on control-relevant identification. 1990 American Control Conference, San Diego.CrossRefGoogle Scholar
  40. Rivera, D. E., Webb, C., and Morari, M. (1987). A control-relevant identification methodology. Paper 82b, AIChE Annual Meeting, New York.Google Scholar
  41. Shinskey, F. G. (1988). Process Control Systems, 3rd ed. New York: McGraw-Hill.Google Scholar
  42. Smith, R. S. and Doyle, J. C. (1989). Model validation: A connection between robust control and identification. American Control Conference, Pittsburgh.Google Scholar
  43. Söderström, T., Ljung, L., and Gustavsson, I. (1976). Identifiability conditions for linear multivariable systems operating under feedback. IEEE Trans. Autom. Control AC-21, 837.CrossRefGoogle Scholar
  44. Webb, C., Budman, H., and Morari, M. (1989). Identifying frequency domain uncertainty bounds for robust controller design: Theory with application to a fixed-bed reactor. American Control Conference, Pittsburgh.Google Scholar
  45. Wei, L., Eskinat, E., and Luyben, W. L. (1991). An improved autotune identification method. Ind. Eng. Chem. Res. 30, 1530–1541.CrossRefGoogle Scholar

Copyright information

© Van Nostrand Reinhold 1992

Authors and Affiliations

  • R. C. McFarlane
    • 1
  • D. E. Rivera
    • 2
  1. 1.Amoco CorporationUSA
  2. 2.Control Systems Engineering Laboratory, Computer-Integrated Manufacturing Systems Research CenterArizona State UniversityUSA

Personalised recommendations