Abstract
This paper presents an application of artificial intelligence techniques to the improvement of the operation of a thermoelectric unit. The capacity for empirical learning gained from artificial intelligence systems was utilized in the development of the strategy. A neuro-fuzzy model for the steam generator startup process is obtained from experimental data. Ultimately, the neuro-fuzzy model is combined with a predictive control algorithm to produce a control strategy for the heating stage of the steam generator. This provides the operators at the fossil power plant with the necessary information to efficiently accomplish the heating process. The information gained from the control strategy is not directly applied to an automatic control scheme; it is presented to the operator who then decides on its application. Therefore, in this way the information is used to develop a strategy that takes into consideration the personal capacity and the working routine of the operator. The simulation tests that were carried out demonstrated the feasibility and the beneficial results that can be obtained from the application of any of the three variants of predictive control proposed in this paper.
This is a preview of subscription content, log in via an institution to check access.
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
Clarke, D.W., Mohtadi, C., Tuffs, P.S., Generalized Predictive Control. Automatica, Vol. 23, No. 2, (1987) 137–160
Sousa, J.M., Babuška, R., Bruijn, P., Verbruggen, H.B., Comparison of Convencional and Fuzzy Predictive Control, Proceedings of the IEEE, (1996)
Babuška, R., An Overview of Fuzzy Modeling and Model-Based Fuzzy Control, World Scientific Series in Robotics and Intelligent Systems-Vol. 23, Fuzzy Logic Control Advances in Applications, Part I: Tutorials pp. 3–36, World Scientific Publishing Co. Pte. Ltd., (1999)
Babuška, R., Verbruggen, H.B., An Overview of Fuzzy Modeling for Control, Control Eng. Practice, Vol. 4 No. 11, pp. 1593–1606, (1996)
Ruz, J.A., Sintonización Optima de un Controlador Difuso para el Calentamiento y Presurización del Generador de Vapor de una Unidad Termoeléctrica, Tesis de Maestria, Univ. Autónoma del Carmen, México, (2001)
Jang, J.-S.,R. ANFIS: Adaptive-Network-based Fuzzy Inference System, IEEE Transactions on Systems, Man and Cybernetics, Vol. 23, No. 3, May/June (1993)
Jang, J.-S.,R., Sun, Ch.-T., Neuro Fuzzy Modeling and Control, Proceedings of the IEEE, Vol. 83, No. 3, March. (1995)
Takagi, T., and Sugeno, M., Fuzzy Identification of systems and its applications to modeling and control, IEEE Trans. Syst., Man, Cybern., Vol 15, pp. 116–132, (1985)
Jang, J.-S.,R., Input Selection for ANFIS learning, Proceedings of the Fifth IEEE International Conference on Fuzzy Systems, Vol. 2, pp. 1493–1499, (1996)
Haykin, S., Neural Networks, Prentice-Hall, Inc., N.J., 2nd ed., (1999)
Nørgaard, M., Ravn, O., Poulsen, N.K., and Hansen, L.K., Neural Networks for Modelling and Control of Dynamic Systems, Springer-Verlag London Limited (2000)
Narendra, K.S., and Parthasarath, K., Identification and Control of Dynamical Systems Using Neural Networks, IEEE Trans. On Neural Networks, Vol. 1, No. 1, (1990)
Ruz H., J.A., Suárez, D.A. Shelomov, E., Control Predictivo basado en un modelo neurodifuso auto-regresivo aplicado al proceso de arranque del generador de vapor de una unidad termoel’ectrica, VIII Seminario Anual de Automática, Electrónica Industrial e Instrumentación SAAEI’ 2001, Matanzas Cuba, 17-19 Sept. (2001)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2002 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Antonio Ruz Hernández, J., Suárez Cerda, D.A., Shelomov, E., Villavicencio Ramírez, A. (2002). Predictive Control Based on an Auto-regressive Neuro-fuzzy Model Applied to the Steam Generator Startup Process at a Fossil Power Plant. In: Coello Coello, C.A., de Albornoz, A., Sucar, L.E., Battistutti, O.C. (eds) MICAI 2002: Advances in Artificial Intelligence. MICAI 2002. Lecture Notes in Computer Science(), vol 2313. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-46016-0_45
Download citation
DOI: https://doi.org/10.1007/3-540-46016-0_45
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-43475-7
Online ISBN: 978-3-540-46016-9
eBook Packages: Springer Book Archive