Abstract
Multistage flash (MSF) desalinization plants are a major means of desalting scawater for human use in several arid regions of the world in the present times. The MSF plants are physically large and their control usually involves more than twenty control loops. According to the present practice, the controllers are of the PI or PID type and their tuning is largely based on experience rather than on systematic modeling of the plant. Plant modeling based on physical principles gives rise to a large and complex set of coupled nonlinear differential equations which has to be linearized about a chosen set of operating conditions. As the operating point changes, the resulting linearized model also changes. This requires retuning of the controllers in certain loops depending on the changing linear plant model in the related loops. The linearized model happens to be enormously large in size requiring reduction for controller design and practical implementation. There exist several model reduction methods and they have to be chosen to meet the objectives of adequate modeling. In a nonlinear plant, the linearized model parameters vary with the operating conditions. To make a controller to simultaneously meet the demands of model reduction and variable operating conditions, the conventional approaches of control are either inadequate or too involved. In this chapter, a technique based on artificial neural networks (ANN) for model reduction under plant parameter perturbations is proposed. The complexity of analysis, reduction, computation and controller design in the variable conditions of operation is avoided by simply training an ANN to give the parameters of a reduced model for use in controller design. An automated decision support may be provided to choose the best ANN configuration for reduced order modeling of a large, complex and variable plant which provides the basis for a robust design of a simple controller. Certain well established model reduction methods are employed in the mainstream of the procedure and the related results are impressive. Based on these results, a scheme based on an added ANN, for direct controller implementation under the discussed conditions is proposed. The results of this modest attempt point out to the strong possibility of more intelligent control of large complex plants under uncertainty and/or variable plant dynamics. The present discussion is centred on SISO loop designs, which does not rule out the possibility of simple extensions to MIMO designs.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Al-Gobaisi D.M.K., A.S. Barakzai, and A.M. El-Nashar, “An overview of modern control strategies for optimizing thermal desalinization plants”. in Desalinization and water Re-use, Proceedings of the 12th International Symposium, (Miriam Balaban, ed.), (Malta), 15–18 April 1991.
Davison E.J., “A method for simplifying linear dynamic systems”, Transactions of IEEE on Automatic Control, Vol. AC-11, pp. 93, 101, 1966.
Wilson D.A., “Optimum solution of model reduction problem”, Proceedings of IEE, Vol. 117, pp. 1161, 1165, 1970.
Sinha N.K. and G.T. Bereznai, “Optimum approximation of high order systems by low order models”, International Journal of Control, Vol. 14, pp. 951, 959, 1971.
Luus R. and G.D. Howitt, “Model reduction by optimization”, Hungarian Journal of Industrial Chemistry, Vol. 16, pp. 29, 38, 1988.
Kokotovic P.V., R.E. O’ Malley, Jr, and P. Sannuti, “Singular perturbations and order reduction in control theory-an overview”, Automatica, Vol. 12, pp. 123, 132, 1976.
Moore B.C., “Principal component analysis in linear systems: controllability, observability and model reduction”, Transactions of IEEE on Automatic Control, Vol. AC-26, pp. 17, 32, 1981.
Shamash Y., “Stable reduced order models using pad é-type approximations”, Transactions of IEEE on Automatic Control, Vol. AC-19, pp. 615, 617, 1974.
Pal J., “Improved padé approximants using stability equation method”, Electronics Letters, Vol. 19, pp. 426, 427, 1983.
Glover K., “All optimal hankel-norm approximations of linear multivariable systems and their I∞-error bounds”, International Journal of Control, Vol. 39, no.6, pp. 1115, 1193, 1984.
Chen C.F. and L.S. Shieh, “A novel approach to linear model simplification”, International Journal of Control, Vol. 8, pp. 561, 570, 1968.
Pal J., “System reduction by a mixed method”, Transactions of IEEE on Automatic Control, Vol. AC-25,no. 5, pp. 973, 976, 1990.
Bosley M.J. and F.P. Lees, “A survey of simple transfer function derivations from high order state-variable models”, Automatica, Vol. 8, pp. 765, 775, 1972.
Pal J., “An algorithmic method for the simplification of linear dynamic scalar systems”, International Journal of Control, Vol. 43, pp. 257, 269, 1986.
Hutton M.F. and Friedland B., “Routh approximation for reducing order of linear, time-invariant systems”, IEEE Transactions of Automatic Control, Vol. AC-20. pp. 329, 337, 1975.
Pal J., “Stable reduced order padé approximants using the routh-hurwitz array”,Electronics Letters, Vol. 15, pp. 225–226, 1979.
Sinha A.K. and J. Pal, “Simulation based reduced order modeling using aclustering technique”, Computers and Electrical Engineering, Vol. 16, no. 3, pp.159, 169, 1990.
D.T. Pham and X. Liu, “State-space identification of dynamic systems using neural networks”, Engineering Applications of AI, Vol. 3, pp. 198, 203, 1990.
Chu, S.R., R. Shoureshi, and M. Tenorio, “Neural networks for system identification”, IEEE Control Systems Magazine, pp. 31, 35, 1990.
Narendra K.S. and K. Parthasarathy, “Identification and control of dynamical systems using neural networks”, IEEE Tranasactions of Neural Networks, Vol. 1,no. 1, pp. 4, 27, 1990.
Liang Jin, Peter N. Nikiforuk, and Madan M Gupta, “Model matching control of unknown nonlinear systems using recurrent neural networks”, in IFAC 11th Triennial World Congress, (Sydney, Australia), pp. 337, 344, 1993.
Anderson B.D.O. and J.B. Moore, Optimal Control: Linear Quadratic Methods. Prentice-Hall of India Ltd., 1991.
Safanov M., “Future directions in the robust control theory”, in IFAC 11th Triennial World Congress, (Tallin, Estoria), pp. 171, 175, 1990.
Author information
Authors and Affiliations
Rights and permissions
Copyright information
© 1995 Kluwer Academic Publishers
About this chapter
Cite this chapter
Ramamurthy, S., Pal, J., Sinha, A., Gobaisi, D.A., Rao, G. (1995). Model Reduction and Control of Multistage Flash (MSF) Desalinization Plants. In: Fuzzy Logic and Intelligent Systems. International Series in Intelligent Technologies, vol 3. Springer, Dordrecht. https://doi.org/10.1007/978-0-585-28000-4_15
Download citation
DOI: https://doi.org/10.1007/978-0-585-28000-4_15
Publisher Name: Springer, Dordrecht
Print ISBN: 978-0-7923-9575-1
Online ISBN: 978-0-585-28000-4
eBook Packages: Springer Book Archive