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Received October 10, 2007
Accepted February 5, 2008
- This is an Open-Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/bync/3.0) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.
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Model predictive control of condensate recycle process in a cogeneration power station: Controller design and numerical application
Department of Chemical and Biomolecular Engineering, Sogang University, Shinsoodong-1, Mapogu, Seoul 121-742, Korea 1LS Industrial System Co., Ltd., Anyang, Gyeonggido 431-749, Korea 2East-West Power Co., Ltd., Goyang, Gyeonggido 410-771, Korea
kslee@sogang.ac.kr
Korean Journal of Chemical Engineering, September 2008, 25(5), 972-979(8), 10.1007/s11814-008-0157-4
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Abstract
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
Morari M, Lee JH, Comput. Chem. Eng., 23(4-5), 667 (1999)
Qin SJ, Badgwell TA, Control Eng. Practice, 11, 733 (2003)
Hogg BW, El-Rabaie NM, IEEE Trans. Energy Convers., 5, 485 (1990)
Lu S, Hogg BW, Control Eng. Practice, 5, 79 (1997)
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)
Kawai K, Takizawa Y, Watanabe S, Control Eng. Practice, 7, 1405 (1999)
Lopez AS, Figueroa GA, Ramirez AV, Electrical Power and Energy Systems, 26, 779 (2004)
Majanne Y, Control Eng. Practice, 13, 1499 (2005)
Jurado F, Carpio J, Energy Conv. Manag., 47(18-19), 2961 (2006)
Moon CJ, Choi JJ, KOPEC Trans., 2, 68 (1991)
Silva RN, Shirley PO, Lemos JM, Goncalves AC, Control Eng. Practice, 8, 1404 (2000)
Marco AD, Poncia G, Control Eng. Practice, 7, 483 (1999)
Shin JY, Jeon YJ, Maeng DJ, Kim JS, Ro ST, Energy, 27(12), 1085 (2002)
Zafiriou E, Chiou HW, Output constraint softening for SISO model predictive control, In Proceedings of ACC, San Francisco, California (1993)
Lee JH, Chikkula Y, Yu ZH, Kantor JC, Int. J. Control, 61(4), 859 (1995)
Kim SH, Moon HJ, Lee KS, ICASE Trans., 4, 413 (1998)
Boyd SP, Vandenberghe L, Convex optimization, 1st ed., Cambridge, New York, NY (2004)
Wills AG, Heath WP, Automatica, 40(8), 1415 (2004)
Garcia CE, Morshedi AM, Chem. Eng. Commun., 46, 73 (1984)
Lee JH, Model predictive control, CRC Industrial Electronics Handbook, 515-521 (1996)
Grewal MS, Andrews AP, Kalman filtering theory and practice, Prentice-Hall, New York, NY (1993)
Qin SJ, Badgwell TA, Control Eng. Practice, 11, 733 (2003)
Hogg BW, El-Rabaie NM, IEEE Trans. Energy Convers., 5, 485 (1990)
Lu S, Hogg BW, Control Eng. Practice, 5, 79 (1997)
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)
Kawai K, Takizawa Y, Watanabe S, Control Eng. Practice, 7, 1405 (1999)
Lopez AS, Figueroa GA, Ramirez AV, Electrical Power and Energy Systems, 26, 779 (2004)
Majanne Y, Control Eng. Practice, 13, 1499 (2005)
Jurado F, Carpio J, Energy Conv. Manag., 47(18-19), 2961 (2006)
Moon CJ, Choi JJ, KOPEC Trans., 2, 68 (1991)
Silva RN, Shirley PO, Lemos JM, Goncalves AC, Control Eng. Practice, 8, 1404 (2000)
Marco AD, Poncia G, Control Eng. Practice, 7, 483 (1999)
Shin JY, Jeon YJ, Maeng DJ, Kim JS, Ro ST, Energy, 27(12), 1085 (2002)
Zafiriou E, Chiou HW, Output constraint softening for SISO model predictive control, In Proceedings of ACC, San Francisco, California (1993)
Lee JH, Chikkula Y, Yu ZH, Kantor JC, Int. J. Control, 61(4), 859 (1995)
Kim SH, Moon HJ, Lee KS, ICASE Trans., 4, 413 (1998)
Boyd SP, Vandenberghe L, Convex optimization, 1st ed., Cambridge, New York, NY (2004)
Wills AG, Heath WP, Automatica, 40(8), 1415 (2004)
Garcia CE, Morshedi AM, Chem. Eng. Commun., 46, 73 (1984)
Lee JH, Model predictive control, CRC Industrial Electronics Handbook, 515-521 (1996)
Grewal MS, Andrews AP, Kalman filtering theory and practice, Prentice-Hall, New York, NY (1993)