Identification and predictive control of a multistage evaporator

Control Engineering Practice

Volumn 18, Issue 12, 2010, Pages 1418-1428

  Atuonwu, J C ^a   Cao, Y ^a   Rangaiah, G P ^b   Tade M O ^c  

^a CRANFIELD UNIVERSITY   (United Kingdom)

^b NATIONAL UNIVERSITY OF SINGAPORE   (Singapore)

^c CURTIN UNIVERSITY   (Australia)

Author keywords

Automatic differentiation; Multiple effect evaporators; Nonlinear model predictive control; Nonlinear system identification; Recurrent neural networks

Indexed keywords

AUTOMATIC DIFFERENTIATIONS; CONTROL PERFORMANCE; DISTURBANCE REJECTION PROBLEM; IN-CONTROL; INPUT-OUTPUT DATA; LEVENBERG-MARQUARDT ALGORITHM; LINEAR MODEL; MECHANISTIC MODELS; MULTIPLE-EFFECT EVAPORATOR; NONLINEAR MODEL PREDICTIVE CONTROL; NONLINEAR SYSTEM IDENTIFICATION; OPTIMIZATION ALGORITHMS; PI CONTROL; PI CONTROLLER; PREDICTIVE CONTROL; SET-POINT TRACKING; SIMULATION MODEL; SYSTEM IDENTIFICATIONS;

COMPUTER SIMULATION; CONTROLLERS; DISTURBANCE REJECTION; EVAPORATORS; IDENTIFICATION (CONTROL SYSTEMS); NONLINEAR SYSTEMS; PREDICTIVE CONTROL SYSTEMS; RECURRENT NEURAL NETWORKS; REINFORCEMENT LEARNING; TWO TERM CONTROL SYSTEMS;

MODEL PREDICTIVE CONTROL;

EID: 78349311627     PISSN: 09670661     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.conengprac.2010.08.002     Document Type: Article

References (47)

1. Al-Seyab R.K., Cao Y. Nonlinear system identification for predictive control using continuous-time recurrent neural networks. Journal of Process Control 2008, 18(6):568-581.
2. Al-Seyab R.K., Cao Y. Differential recurrent neural network based predictive control. Computers and Chemical Engineering 2008, 32(7):1533-1545.
3. Atuonwu, J.C. , Cao, Y. , Rangaiah, G.P. , Tadé, M.O. Nonlinear Model Predictive of Control of a Multistage Evaporator System using Recurrent Neural Networks. 2009 4th IEEE Conference on Industrial Electronics and Applications, ICIEA 2009 (2009),vol:2009, Article number 5138477, Pages 1662-1667, DOI: 10.1109/ICIEA.2009.5138477
4. Balbis, L., Katebi, R., & Ordys, A. (2006). Model predictive control design for industrial applications. In International Control Conference (ICC2006), Glasgow, UK.
5. Bhat N.V., McAvoy T.J. Determining model structure for neural models by network stripping. Computers and Chemical Engineering 1992, 16(4):271-281.
6. Bulut, B., Katebi, M. R., & Johnson, M. A. (2000). Industrial predictive control for hot rolling mill co-ordination. In Proceedings of the American Control Conference.
7. Cao Y. A formulation of nonlinear model predictive control using automatic differentiation. Journal of Process Control 2005, 15(8):851-858.
8. Cao, Y., & Al-Seyab, R. K. (2003). Nonlinear model predictive control using automatic differentiation. In European Control Conference'2003 (CDROM), Cambridge, UK.
9. Chen J., Yea Y. Neural network based predictive control for multivariable processes. Chemical Engineering Communications 2002, 189:865-894.
10. Colannino J. Modeling of combustion systems: A practical approach 2006, CRC Press, Taylor and Francis Group, USA.
11. Draeger A., Engell S., Ranke H. Model predictive control using neural networks. IEEE Control Systems Magazine 1995, 61-66.
12. Ekawati, E., Bahri, P. A., Lepage, G., & Noraii, A. (2003). Controllability analysis of industrial five-effect evaporator system. In The Foundations of Computer Aided Process Operations (FOCAPO) 2003 Conference, Coral Springs, Florida, US.
13. Funahashi K.L., Nakamura Y. Approximation of dynamical systems by continuous-time recurrent neural networks. Neural Networks 1993, 6:183-192.
14. Galvan I.M., Zaldivar J.M. Application of recurrent neural networks for nonlinear inverse and predictive control of the heat transfer fluid temperature in a batch reactor. Chemical Engineerng and Processing 1998, 37:149-161.
15. Goldberge D.E. Genetic algorithms in search, optimization and machine learning 1989, Addison-Wesley, Reading, MA.
16. Harnischmacher G., Marquardt W. Nonlinear model predictive control of multivariable processes by block-structured models. Control Engineering Practice 2007, 15:1238-1256.
17. Henson M.A. Nonlinear model predictive control: Current status and future directions. Computers and Chemical Engineering 1998, 23(2):187-202.
18. Hush D.R., Horne B.G. Progress in supervised neural networks. IEEE Signal Processing Magazine 1993, 10(1):8-39.
19. Jazayeri-Rad H. The nonlinear model predictive control of a chemical plant using multiple neural networks. Neural Computers and Applications 2004, 13:2-15.
20. Kam K.M., Saha P., Tadé M.O., Rangaiah G.P. Models of an industrial evaporator for education and research in process control. Developments in Chemical Engineering and Mineral Processing 2001, 10:105-127.
21. Kam, K. M., & Tadé, M. O. (1997). Dynamic modeling and geometric analysis of an industrial multi-stage evaporation unit, Technical Report 1/97. Western Australia: School of Chemical Engineering, Curtin University of Technology.
22. Kambhampati C., Craddock R.J., Tham M., Warwick K. Inverse model control using recurrent networks. Mathematics and Computers in Simulation 2000, 51:181-199.
23. Leonard J.A., Kramer M.A. Improvement of the back-propagation algorithm for training neural networks. Computers and Chemical Engineering 1990, 14:337-341.
24. Marquardt D. An algorithm for least-squares estimation of nonlinear parameters. SIAM Journal of Applied Mathematics 1963, 11:431-441.
25. Mjalli F.S., Hussain M.A. Approximate predictive versus adaptive control strategies of biodiesel reactors. Industrial Engineering Chemistry Research 2009, 48(24):11034-11047.
26. Mohanty S. Artificial neural network based system identification and model predictive control of a flotation column. Journal of Process Control 2009, 19:991-999.
27. Nagy Z.K. Model based control of a yeast fermentation bioreactor using optimally designed artificial neural networks. Chemical Engineering Journal 2007, 127:95-109.
28. Nagy Z., Mahn B., Franke R., Allgöwer F. Evaluation study of an efficient output feedback nonlinear model predictive control for temperature tracking in an industrial batch reactor. Control Engineering Practice 2007, 15:839-850.
29. Nørgaard M., Ravn O., Poulsen N.K., Hansen L.K. Neural networks for modeling and control of nonlinear systems 2000, Springer-Verlag London Ltd., London.
30. Norquay S.J., Palazoglu A., Romagnoli J.A. Model predictive control based on Wiener models. Chemical Engineering Science 1998, 53(1):75-84.
31. Ou J., Rhinehart R.R. Grouped neural network modeling for model predictive control. ISA Transactions 2002, 41:195-202.
32. Pearson R.K., Ogunnaike B.A. Nonlinear process identification. Nonlinear process control 1997, Prentice-Hall, New Jersey, Englewood Cliffs. M.A. Henson, D.E. Seborg (Eds.).
33. Piché S., Sayyar-Rodsari B., Johnson D., Gerules M. Nonlinear model predictive control using neural networks. IEEE Control Systems Magazine 2000, 20(3):53-62.
34. Puskorius G.V., Feldkamp L.A. Neurocontrol of nonlinear dynamic systems with Kalman filter trained recurrent neural networks. IEEE Transactions on Neural Networks 1994, 5(2):279-297.
35. Qin J.S., Badgwell T.A. A survey of industrial model predictive control technology. Control Engineering Practice 2003, 11(7):733-764.
36. Rangaiah G.P., Saha P., Tadé M.O. Nonlinear model predictive control of an industrial four-stage evaporator system via simulation. Chemical Engineering. Journal 2002, 87(3):285-299.
37. Rumelhart, D. E., Hinton, G. E., & Williams, R. J. (1986). Learning internal representations by error propagation. In: Parallel Distributed Processing. Cambridge, MA: MIT Press.
38. Schenker B., Agarwal M. Online-optimized feed switching in semi-batch reactors using semi-empirical dynamic models. Control Engineering Practice 2000, 8:1393-1403.
39. Sentoni G.B., Biegler L.T., Guiver J.B., Zhao H. State space nonlinear process modeling: Identification and uncertainty. AIChE Journal 1998, 44(10):2229-2239.
40. Sjoberg J., Zhang Q., Ljung L., Benveniste A., Delyon B., Glorennec Y., Hjalmarsson H., Juditsky A. Nonlinear black-box modeling in system identification: A unified review. Automatica 1995, 31(12):1691-1724.
41. Su H.T., McAvoy T.J. Artificial neural networks for nonlinear process identification. Nonlinear process control 1997, Prentice-Hall, New Jersey, Englewood Cliffs. M.A. Henson, D.E. Seborg (Eds.).
42. Temeng K.O., Schenelle P.D., McAvoy T.J. Model predictive control of an industrial packed reactor using neural networks. Journal of Process Control 1995, 5(1):19-28.
43. Wanas N., Auda G., Kamel M.S., Karray F. On the optimal number of hidden nodes in a neural network. IEEE Canadian Conference on Electrical and Computer Engineering 1998, 2:918-921.
44. Werbos P.J. Backpropagation through time: What it does and how to do it. Proceedings of the IEEE 1990, 78(10):1550-1560.
45. Yu D.L., Gomm J.B. Implementation of neural network predictive control to a multivariable chemical reactor. Control Engineering Practice 2003, 11:1315-1323.
46. Zamarreno J.M., Vega P. State-space neural network properties and applications. Neural Networks 1998, 11:1099-1112.
47. Zhu, X., & Seborg, D. E. (1994). Nonlinear predictive control based on Hammerstein models. In Proceedings: Process Systems Engineering (pp. 995-1000).