Issue
Korean Journal of Chemical Engineering,
Vol.25, No.5, 972-979, 2008
Model predictive control of condensate recycle process in a cogeneration power station: Controller design and numerical application
Abstract.A model predictive control (MPC) system has been developed for application to the condensate recycle process of a 300 MW cogeneration power station of the East-West Power Plant, Gyeonggido, Korea. Unlike other industrial processes where MPC has been predominantly applied, the operation mode of the cogeneration power station changes continuously with weather and seasonal conditions. Such characteristic makes it difficult to find the process model for controller design through identification. To overcome the difficulty, process models for MPC design were derived for each operation mode from the material balance applied to the pipeline network around the concerned process. The MPC algorithm has been developed so that the controller tuning is easy with one tuning knob for each output and the constrained optimization is solved by an interior-point method. For verification of the MPC system before process implementation, a process simulator was also developed. Performance of the MPC was investigated first with a process simulator against various disturbance scenarios.
[References]
  1. Morari M, Lee JH, Comput. Chem. Eng., 23(4-5), 667, 1999
  2. Qin SJ, Badgwell TA, Control Eng. Practice, 11, 733, 2003
  3. Hogg BW, El-Rabaie NM, IEEE Trans. Energy Convers., 5, 485, 1990
  4. Lu S, Hogg BW, Control Eng. Practice, 5, 79, 1997
  5. Son WK, Kwon OK, Lee ME, Fault tolerant model based predictive control with application to boiler systems, In Proceedings of IFAC safeprocess’97, Hull, United Kingdom, 1997
  6. Kawai K, Takizawa Y, Watanabe S, Control Eng. Practice, 7, 1405, 1999
  7. Lopez AS, Figueroa GA, Ramirez AV, Electrical Power and Energy Systems, 26, 779, 2004
  8. Majanne Y, Control Eng. Practice, 13, 1499, 2005
  9. Jurado F, Carpio J, Energy Conv. Manag., 47(18-19), 2961, 2006
  10. Moon CJ, Choi JJ, KOPEC Trans., 2, 68, 1991
  11. Silva RN, Shirley PO, Lemos JM, Goncalves AC, Control Eng. Practice, 8, 1404, 2000
  12. Marco AD, Poncia G, Control Eng. Practice, 7, 483, 1999
  13. Shin JY, Jeon YJ, Maeng DJ, Kim JS, Ro ST, Energy, 27(12), 1085, 2002
  14. Zafiriou E, Chiou HW, Output constraint softening for SISO model predictive control, In Proceedings of ACC, San Francisco, California, 1993
  15. Lee JH, Chikkula Y, Yu ZH, Kantor JC, Int. J. Control, 61(4), 859, 1995
  16. Kim SH, Moon HJ, Lee KS, ICASE Trans., 4, 413, 1998
  17. Boyd SP, Vandenberghe L, Convex optimization, 1st ed., Cambridge, New York, NY, 2004
  18. Wills AG, Heath WP, Automatica, 40(8), 1415, 2004
  19. Garcia CE, Morshedi AM, Chem. Eng. Commun., 46, 73, 1984
  20. Lee JH, Model predictive control, CRC Industrial Electronics Handbook, 515-521, 1996
  21. Grewal MS, Andrews AP, Kalman filtering theory and practice, Prentice-Hall, New York, NY, 1993