Abstract
Nuclear power plant operation often involves very important human decisions, such as actions to be taken after a nuclear accident/transient, or finding the best core reload pattern, a complex combinatorial optimization problem which requires expert knowledge. Due to the complexity involved in the decisions to be taken, computerized systems have been intensely explored in order to aid the operator. Following hardware advances, soft computing has been improved and, nowadays, intelligent technologies, such as genetic algorithms, neural networks and fuzzy systems, are being used to support operator decisions.
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.
Similar content being viewed by others
References
Alvarenga, M.A.B., Martinez, A.S. and Schirru, R. (1997): Adaptive Vector Quantization Optimized by Genetic Algorithms for Real-Time Diagnosis through Fuzzy Sets, Nuclear Technology, 120 (3), 188–197.
Bartal, Y., Lin, J. and Uhrig, R.E. (1995): Nuclear Power Plant Transient Diagnostics Using Artificial Neural Network that Allow “Don’t-Know” Classification, Nuclear Technology, 110, 436–449.
Bartlett, E.B. and Uhrig, R.E. (1992): Nuclear Power Plant Status Diagnostics Using an Artificial Neural Network, Nuclear Technology, 97, 272–281.
Bean, J.C. (1994): Genetic Algorithms and Random Keys for Sequencing and Optimization, ORSA, 6 (2).
Chapot, J. L., Da Silva, F. C. and Schirru, R. (1999): A New Approach to the Use of Genetic Algorithms to Solve the Pressurized Water Reactor’s Fuel Management Optimization Problem, Annals of Nuclear Energy, 26 (7), 641–655.
Darwing, C. (1859): The Origin of Species by Means of Natural Selection, John Murray, London.
Davis, L. (1991): Handbook of Genetic Algorithms, VNR, New York.
DeChaine, M.D. and Feltus, M. A. (1995): Nuclear Fuel Management Optimization Using Genetic Algorithms, Nuclear Technology, 111, 109–114.
Goldberg, D.E. (1989): Genetic Algorithms in Search Optimization and Machine Learning, Addison-Wesley.
Gray, P., Hart, W., Painton, L., Phillips, C., Trahan, M., Wagner, J. (1997): A Survey of Global Optimization Methods, Sandia National Laboratories, Albuquerque.
Grefenstette, J.J. (1990): A User’s Guide to Genesis Version 5. 0.
Holland, J.H. (1975): Adaptation in Natural and Artificial Systems, Ann Arbor, University of Michigan.
Kim, Y.S., and Mitras, S. (1994): An Adaptive Integrated Fuzzy Clustering Model for Pattern Recognition, Fuzzy Sets and Systems, 65, 297–310.
Kosko, B. (1992): Neural Networks and Fuzzy Systems, Prentice Hall.
Liu, Y.S. et al. (1985): ANC: A Westinghouse Advanced Nodal Computer Code. WCAP-10965, Westinghouse.
Oliver, I.M., Smith, D.J., Holland, J.R.C. (1987): A Study of Permutation Crossover Operator on th Traveling Salesman Problem. Procedings of The Second International Conference on Genetic Algorithms and Their Applicationa, 224–230.
Pereira, C.M.N.A., Schirru, R. and Martinez, A.S. (1998): Learning an Optimized Classification System From a Data Base of Time Series Patterns Using Genetic Algorithms, In: Ebecken, N.F.F. (ed), Data Mining, Computational Mechanics Publications, WIT Press, England.
Renders, J.M., Fiasse, S.P., Verstraete, M.M. and Nordwik, J.P. (1992): A Comparative Study of Optimization Methods for the Retrieval of Quantita- tive Information from Satellite Data, EUR 14851 EN, Joint Research Centre.
Schirru, R, Pereira, C.M.N.A., Chapot, L. Carvalho (1997): F. “A Genetic Algorithm Solution For Combinatorial Problems - The Nuclear Core Reload Example,” XI Encontro Nacional de Fsica de Reatores, 357–360, Brazil.
S.K. Rogers and M. Kabrisky (1991) An Introduction to Biological and Artificial Neural Networks for Pattern Recognition, Spie Optical Engineering Press.
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2000 Springer-Verlag Berlin Heidelberg
About this chapter
Cite this chapter
Schirru, R., Pereira, C.M.N.A., Martinez, A.S. (2000). Genetic Algorithms Applied to the Nuclear Power Plant Operation. In: Ruan, D. (eds) Fuzzy Systems and Soft Computing in Nuclear Engineering. Studies in Fuzziness and Soft Computing, vol 38. Physica, Heidelberg. https://doi.org/10.1007/978-3-7908-1866-6_15
Download citation
DOI: https://doi.org/10.1007/978-3-7908-1866-6_15
Publisher Name: Physica, Heidelberg
Print ISBN: 978-3-7908-2466-7
Online ISBN: 978-3-7908-1866-6
eBook Packages: Springer Book Archive