Modular modelling of an evaporator for long-range prediction

Artificial Intelligence in Engineering

Volumn 11, Issue 4, 1997, Pages 347-355

  Russell, N T ^a   Bakker, H H C ^a  

^a MASSEY UNIVERSITY   (New Zealand)

Author keywords

Evaporators; Model predictive control; Modular modelling; Neural networks

Indexed keywords

COMPUTATIONAL METHODS; COMPUTER SIMULATION; MATHEMATICAL MODELS; NEURAL NETWORKS; PREDICTIVE CONTROL SYSTEMS; TOPOLOGY;

MODEL DECOMPOSITION; MODULAR EVAPORATOR NEURAL NETWORK MODELS; THREE EFFECT FALLING FILM EVAPORATOR;

EVAPORATORS;

EID: 0031259121     PISSN: 09541810     EISSN: None     Source Type: Journal    
DOI: 10.1016/S0954-1810(96)00053-2     Document Type: Article

References (15)

1. Garcia, C. E., Prett, D. M. and Morari, M. Model predictive control: theory and practice - a survey. Automatica, 1989, 25, 335-348.
2. Richalet, J., Industrial applications of model based predictive control. Automatica, 1993, 29, 1251-1274.
3. Austin, P. C. and Bozin, A. S. Applying dynamic matrix control in the process industries. Trans. Inst. Measurement & Control. 1996, 18, 32-41.
4. Hunt, K. J., Sbarbaro, D., Zbikowski, R. and Gawthrop, P. J. Neural networks for control systems - a survey. Automatica, 1992, 10, 1083-1112.
5. Morris, A. J., Montague, G. A. and Willis, M. J. Artificial neural networks: studies in processing modelling and control. Chem. Engng. Res. and Des., 1994, 72, 3-19.
6. Quaak, P. and Gerritsen, J. B. M. Modelling dynamic behaviour of multiple-effect falling-film evaporators. In Computer Applications in Chemical Engineering, ed. H. T. Bussemaker and P. D. Iedema. Elsevier, Amsterdam, 1990, pp. 59-64.
7. Narendra, K. S. and Parthasarathy, K. Identification and control of dynamical systems using neural networks. IEEE Trans. Neural Networks, 1990, 1, 4-27.
8. Happel, B. L. M. and Murre, J. M. J. Design and evolution of modular neural network architectures. Neural Networks, 1994, 7, 985-1004.
9. Ronco, E. and Gawthrop, P. Modular neural networks: a state of the art. Technical Report: CSC-95026, University of Glasgow, 1995.
10. Mavrovouniotis, M. L. and Chang, S. Hierarchical neural networks. Computers Chem. Engng, 1992, 16, 347-369.
11. Qin, S.-Z., Su, H.-T. and McAvoy, T. J. Comparison of four neural net learning methods for dynamic system identification. IEEE Trans. Neural Networks, 1992, 3, 122-130.
12. Su, H.-T., McAvoy, J. and Werbos, P. Long-term predictions of chemical processes using recurrent neural networks: A parallel training approach. Ind. Engng Chem. Res., 1992, 31, 1338-1352.
13. Williams, R. J. and Zipser, D. A learning algorithm for continually running fully recurrent neural networks. Neural Computation, 1992, 56, 319-346.
14. Marquardt, D. W. An algorithm for least-squares estimation of nonlinear parameters. J. Soc. Indust. Appl. Math., 1863, 11, 431-441.
15. Gomm, J. B., Lisboa, P. J. G., Williams, D. and Evans, J. T. Accurate multi-step-ahead prediction of non-linear systems using the MLP neural network with spread encod-ing. Trans. Inst. Measurement & Control, 1994, 16, 203-213.