Application of recurrent neural networks in batch reactors Part II: Nonlinear inverse and predictive control of the heat transfer fluid temperature

Chemical Engineering and Processing: Process Intensification

Volumn 37, Issue 2, 1998, Pages 149-161

  Galvan I M ^a   Zaldivar J M ^b  

^a UNIVERSIDAD CARLOS III DE MADRID   (Spain)

^b JOINT RESEARCH CENTRE   (Italy)

Author keywords

Batch reactors; Heat transfer; Neural networks

Indexed keywords

HEAT TRANSFER; NEURAL NETWORKS; NONLINEAR CONTROL SYSTEMS; PREDICTIVE CONTROL SYSTEMS; REAL TIME SYSTEMS;

BATCH REACTORS; INVERSE CONTROL SYSTEMS;

CHEMICAL REACTORS;

BATCH PROCESS; HEAT TRANSFER; NEURAL NETWORK;

EID: 0346856460     PISSN: 02552701     EISSN: None     Source Type: Journal    
DOI: 10.1016/S0255-2701(97)00046-9     Document Type: Article

References (33)

1. A. Isidori, Nonlinear Control Systems, An introduction. 2nd ed., Springer-Verlag, Berlin, 1989.
2. M. Agarwal, D.E. Seborg, Self-tunning controllers for nonlinear systems, Automatica 23 (1987) 209-214.
3. M. Agarwal, D.E. Seborg, Multivariable nonlinear self-tunning controller, AIChE J. 33 (1987) 1379-1386.
4. J.D. Morningred, B.E. Paden, D.E. Seborg, D.A. Mellichamp, An adaptive nonlinear predictive controller, Chem. Eng. Sci. 47 (1992) 755-762.
5. J. Alvarez, E. Aranda, Nonlinear adaptive control of a chemical reactor using singular perturbation techniques. Dynamics and Control of Chemical Reactors (DYCORD +'92), Maryland, USA, 1992, pp. 125-247.
6. J. Villermaux, C. Georgakis, Current problems concerning batch reactions, Int. Chem. Eng. 31 (1991) 434-440.
7. K.J. Åström, T.J. McAvoy, Intelligent control, An overview and evaluation, in: D.A. White, D.A. Sofge (Eds.), Handbook of Intelligent Control, Van Nostrand Reinhold, New York, 1992.
8. N. Bhat, T.J. McAvoy, Use of neural nets for dynamic modelling and control of chemical process systems, Comput. Chem. Eng. 14 (1990) 573-583.
9. D.C. Psichogios, L.H. Ungar, Direct and indirect model based control using artificial neural networks, Ind. Eng. Chem. Res. 30 (1991) 2564-2573.
10. E.P. Nahas, M.A. Henson, D.E. Seborg, Nonlinear internal model control strategy for neural network models, Comput. Chem. Eng. 16 (1992) 1039-1057.
11. J.L. Dirion, B. Ettedgui, M. Cabassud, M.V. Le Lann, G. Casamatta, Elaboration of a neural network system for semi-batch reactor temperature control: an experimental study, Chem. Eng. Process. 35 (1996) 225-234.
12. B. Widrow, J. McCool, B. Medoff, Adaptive control by inverse modelling. Twelfth Asimolar Conference on Circuits, Systems and Computers, 1978.
13. D. Psaltis, A. Sideris, A.A. Yamamura, A multi-layered neural network controller, IEEE Control Syst. Mag. 8 (1988) 17-20.
14. B. Widrow, Adaptive inverse control. Proc. Second IFAC Workshop on Adaptive Systems in Control and Signal Processing, Lund Institute of Technology, Lund, Sweden, 1986, pp. 1-5.
15. M.I. Jordan, Generic constraints on underspecified target trajectories. IJCNN Proc., IEE, New York, June 1989, pp. 217-225.
16. K.S. Narendra, K. Pathasarthy, Identification and control of dynamical systems using neural networks, IEEE Trans. Neural Networks 1 (1990) 4-27.
17. J. Tanomaru, S. Omatu, Process control by on-line trained neural controllers, IEEE Trans. Ind. Electron. 39 (1992) 511-521.
18. Low Teck-Seng, Tong-Heng Lee, Hock-Koon Lim, A Methodology for neural network training for control of drives with nonlinearities, IEEE Trans. Ind. Electron. 39 (1993) 243-249.
19. J.M. Martín Sánchez, Adaptive predictive control systems, EEUU Patent no. 4 197 576, 1976.
20. J.W. Eaton, J.B. Rawlings, Model-predictive control of chemical processes, Chem. Eng. Sci. 47 (1992) 705-720.
21. C.E. García, D.M. Prett, M. Morari, Model predictive control, theory and practice - a survey, Automatica 25 (1989) 335-348.
22. D.W. Clarke, C. Mohtadi, P.S. Tuffs, Generalized predictive control - Part I. the basic algoritnm, Automatica 23 (1987) 137-148.
23. D.W. Clarke, C. Mohtadi, P.S. Tuffs, Generalized predictive control - Part II. Extensions and interpretations, Automatica 23 (1987) 149-160.
24. B.W. Bequette, Nonlinear predictive control using multi-rate sampling, Can. J. Chem. Eng. 69 (1991) 136-147.
25. T. Pröll, M.N. Karim, Model-predictive pH control using realtime NARX approach, AIChE J. 40 (1994) 269-282.
26. M.J. Willis, G.A. Montague, C. Massimo, M.T. Tham, A.J. Morris, Artificial neural network based predictive control. IFAC Advanced Control of Chemical Processes, Toulouse, France, 1991, pp. 261-266.
27. C. Kambhampati, K. Warwick, Use of stochastic neural networks for process control, IFAC Dynamics and Control of Chemical Reactors (DYCORD +'92), Maryland, USA, 1992, pp. 111-116.
28. D.G. Luenberger, Linear and Nonlinear Progamming, Addison-Wesley, Reading, MA, 1973.
29. J.M. Zaldívar, H. Hernández, H. Nieman, E. Molga, C. Bassani, The FIRES project: experimental study of thermal runaway due to agitation problems during toluene nitration, J. Loss Prev. Process Ind. 6 (1993) 319-326.
30. I.M. Galván, J.M. Zaldívar, Application of recurrent neural networks in batch reactors: Part I. NARMA modelling of the dynamic behaviour of the heat transfer fluid temperature, Chem. Eng. Process. 36 (1997) 505-518.
31. I.J. Leontaritis, S.A. Billings, Input-ouput parametric models for nonlinear systems - Part I. Deterministic nonlinear systems, Int. J. Control 41 (1985) 303-328.
32. M. Saerens, A. Soquet, A neural controller based on backpropagation algorithm. Technical report IRIDIA-NN 005, Université Libre of Bruxelles, 1989, p. 23.
33. F.C. Chen, Y.H. Pao, Learning control with neural networks, Proc. IEEE Cont. Robot. Autom. 3 (1989) 1448-1453.