Model predictive temperature control towards improving cement precalcination

Proceedings of the Institution of Mechanical Engineers. Part I: Journal of Systems and Control Engineering

Volumn 217, Issue 2, 2003, Pages 147-153

  Koumboulis, F N ^a   Kouvakas, N D ^a  

^a HALKIS INSTITUTE OF TECHNOLOGY   (Greece)

Author keywords

Artificial neural network identification; Cement industry; Model predictive control; PI controller; Temperature control

Indexed keywords

CALCINATION; CEMENT INDUSTRY; CLOSED LOOP CONTROL SYSTEMS; CONTROL SYSTEM SYNTHESIS; IDENTIFICATION (CONTROL SYSTEMS); MATHEMATICAL MODELS; NEURAL NETWORKS; POLYNOMIALS; TEMPERATURE CONTROL; TWO TERM CONTROL SYSTEMS;

ARTIFICIAL NEURAL NETWORK IDENTIFICATION; PRECALCINATION;

PREDICTIVE CONTROL SYSTEMS;

EID: 0038035483     PISSN: 09596518     EISSN: None     Source Type: Journal    
DOI: 10.1243/095965103321512855     Document Type: Article

References (17)

1. Duda, W. H. Cement Data Book, Vol. 1, International Process Engineering in the Cement Industry, 1985 (Bauverlag GmbH, Germany).
2. Duda, W. H. Cement Data Book, Vol. 2, Electrical Engineering Automation, Storage, Transportation, Dispatch, 1984 (Bauverlag GmbH, Germany).
3. Griparis, M. K., Koumboulis, F. N., Vlachos, N. S. and Marinos, J. Precalcination in cement plants (system descriptions and control trends). In IFAC Symposium on Manufacturing, Modeling, Management and Control, University of Patras, Rio, Greece, 12-14 July 2000, pp. 262-267.
4. Kouvakas, N. D. Identification and adaptive control of a precalciner for cement production. Post-graduate thesis. Department of Mechanical and Industrial Engineering, University of Thessaly, Greece, 2001.
5. Koumboulis, F. N. and Kouvakas, N. D. Indirect adaptive neural control for precalcination in cement plants. In Proceedings of the IFDICON and SERVICEROB Conference, Santorini, Greece, 25-27 June 2001
6. Koumboulis, F. N. and Kouvakas, N. D. Indirect adaptive neural control for precalcination in cement plants. Mathematics and Computers in Simulation, 2002, Vol. 60, pp. 325-334.
7. Clarke, D. W., Mohtadi, C. and Tuffs, P. S. Generalized predictive control-Part I. The basic algorithm. Automatica, 1987, 23(2), 137-148.
8. Clarke, D. W., Mohtadi, C. and Tuffs, P. S. Generalized predictive control-Part II. Extensions and interpretations. Automatica, 1987, 23(2), 149-160.
9. Rawlings, J. B. Tutorial overview of model predictive control. IEEE Control Systems Mag., June 2000, 38-52.
10. Miller, R. M., Shah, S. L., Wood, R. K. and Kwok, E. K. Predictive PID. ISA Trans., 1999, 38, 11-23.
11. Kwok, K. E., Ping, M. C. and Li, P. A model-based augmented PID algorithm. J. Process Control, 2000, 10, 9-18.
12. Lo, W. L., Rad, A. B. and Tsang, K. M. Auto-tuning of output predictive PI controller. ISA Trans., 1999, 38, 25-36.
13. Hu, J. Q. and Rose, E. Generalized predictive control using a neuro-fuzzy model. Int. J. Systems Sci., 1999, 30(1), 117-122.
14. Piché, S., Sayyar-Rodsari, B., Johnson, D. and Gerules, M. Nonlinear model predictive control using neural networks. IEEE Control Systems Mag., June 2000, 53-62.
15. Wang, H., Liu, G. P., Harris, C. J. and Brown, M. Advanced Adaptive Control, 1999 (Pergamon, Oxford).
16. Äström, K. J. and Hägglund, T. PID Controllers: Theory, Design and Tunning, 1995 (Instrument Society of America, USA).
17. King, R. E. and Koumboulis, F. N. Industrial Digital Controllers, 2003 (Marcel Dekker, New York) (to appear).