[KSCI] Korea Science Citation Index Service

COMPUTATIONAL INTELLIGENCE IN NUCLEAR ENGINEERING

UHRIG ROBERT E. (Department of Nuclear Engineering, University of Tennessee)
HINES J. WESLEY (Department of Nuclear Engineering, University of Tennessee)

Publication Information

Nuclear Engineering and Technology / v.37, no.2, 2005 , pp. 127-138 More about this Journal

Abstract

Approaches to several recent issues in the operation of nuclear power plants using computational intelligence are discussed. These issues include 1) noise analysis techniques, 2) on-line monitoring and sensor validation, 3) regularization of ill-posed surveillance and diagnostic measurements, 4) transient identification, 5) artificial intelligence-based core monitoring and diagnostic system, 6) continuous efficiency improvement of nuclear power plants, and 7) autonomous anticipatory control and intelligent-agents. Several changes to the focus of Computational Intelligence in Nuclear Engineering have occurred in the past few years. With earlier activities focusing on the development of condition monitoring and diagnostic techniques for current nuclear power plants, recent activities have focused on the implementation of those methods and the development of methods for next generation plants and space reactors. These advanced techniques are expected to become increasingly important as current generation nuclear power plants have their licenses extended to 60 years and next generation reactors are being designed to operate for extended fuel cycles (up to 25 years), with less operator oversight, and especially for nuclear plants operating in severe environments such as space or ice-bound locations.

Keywords

condition monitoring; sensor validation; anticipatory control; intelligent agents; ill-posed problem; noise analysis; surveillance;

Citations & Related Records

Reference

1	Fantoni, P., S. Figedy, A. Racz, 'A Neuro-Fuzzy Model Applied to Full Range Signal Validation of PWR Nuclear Power Plant Data', FLINS-98, Antwerpen, Belgium, 1998
2	Electric Power Research Institute EPRI, Instrument Calibration and Monitoring Program, Volume 1: Basis for the Method, EPRI, Palo Alto, CA: 103436-V1, 1993
3	Electric Power Research Institute EPRI, EPRI Topical Report (TR) 104965, 'On-Line Monitoring of Instrument Channel Performance', Final Report, 1998
4	NRC Memorandum to Stephen Dembek from Evangelos C. Marinos, Subject: The Staff Review of EPRI Topical Report (TR) 104965, 'On-Line Monitoring of InstrumentChannel Performance,' Final Report, November 1998, June 22, 2000
5	Hines, J. W. and Uhrig, R. E., 'Use of Autoassociative Neural Networks for Signal Validation', Journal of Intelligent and Robotic Systems, Kluwer Academic Press, 1997
6	Rasmussen, B., J.W., Hines, J.W., and R.E. Uhrig, 'Novel Approach to Process Modeling for Instrument Surveillance and Calibration Verification', Third American Nuclear Society International Topical Meeting on Nuclear Plant Instrumentation and Control and Human-Machine Interface Technologies, Washington DC, 2000
7	Singer, R.M., K.C. Gross, J.P. Herzog, R.W. King and S.W. Wegerich, 'Model-Based Nuclear Power Plant Monitoring and Fault Detection: Theoretical Foundations, Proc. 9th Intl. Conf. on Intelligent Systems Applications to Power Systems, Seoul, Korea, 1996
8	Upadhyaya, B.R., 'Sensor Failure Detection and Estimation', Nuclear Safety, 1985
9	Upadhyaya, B.R., and E. Eryurek, 'Application of Neural Networks for Sensor Validation and Plant Monitoring,' Nuclear Technology, vol. 97, pp. 170-176, 1992
10	Wach, D., 'Condition Monitoring for Improved Performance in German NPPs', 4th International Topical Meeting on Nuclear Plant Instrumentation, Control and Human Machine Interface Technology (NPIC&HMIT '04), Columbus Ohio, 2004
11	Sunder R., D. Wach, R. Heinbuch, J., Irlbeck, 'Main Coolant Pump Shaft Crack Detection in the Isar-2 Nuclear Power Plant', VGB-Kraftwerkstechnik, Vol. 4, 1989
12	Olma, B.J., 'Experience and Standardization in Loose Parts Monitoring of German NPPs', 4th International Topical Meeting on Nuclear Plant Instrumentation, Control and Human Machine Interface Technology (NPIC&HMIT'04), Columbus OH, 2004
13	Mott, J., S., Young, and R.W. King, 'Pattern Recognition Software for Plant Surveillance', US DOE Report, 1987
14	Uhrig, R.E., Editor, 'Proceedings of the Symposium on 'Noise Analysis in Nuclear Systems', U.S.A.E.C. Symposium Series, Number 4, TID-7679, 1964
15	Uhrig, R.E., Editor, 'Proceedings of the Symposium on 'Neutron Noise, Waves, and Pulse Propagation,'' U. S. A. E. C. Symposium Series, Number 9, CONF 660206, 1967
16	Thie, J.S., 'Reactor Noise' Rowman and Littlefield, New York., 1963
17	Uhrig, R.E., 'Random Noise Techniques in Nuclear Reactor Systems, Ronald Press, New York, (Translated into Russian and published in 1974 by OCKBAATOM AT), 1970
18	Roverso, D., 'Multivariate Temporal Classification by Windowed Wavelet Decomposition and Recurrent Neural Networks,' Proceedings of the 3rd ANS International Topical Meeting on Nuclear Plant Instrumentation, Control and Human-Machine Interface Technologies, NPIC&HMIT 2000, Washington D.C.
19	Bartal, Y., J. Lin, and R. E. Uhrig, 'Nuclear Power Plant Transient Diagnostics Using Artificial Neural Networks, that Allow 'Don't Know'; Classifications,' Nuclear Technology, Vol 110, No. 3, pp 436-449, June 1995
20	Attieh, I., J. W. Hines, and R. E. Uhrig, 'Transient Detection in Nuclear Power Plants, Proceedings of the Expanded Halden Programme Group Meeting on Instrumentation and control, Lillihammer, Norway, March 28-31, 2001
21	Bartlett, E., and R. E. Uhrig, 'Nuclear Power Plant Status Diagnostics Using an Artificial a Neural Network,' Nuclear Technology, Vol. 97, pp 272-281, March 1992 DOI
22	Gou, Z., and R. E. Uhjrig, 'Use of Artificial Neural Networks to Analyze Nuclear Power Plant Performance,' Nuclear Technology, Vol. 99, pp 36-42, July 1992
23	Uhrig, R.E. and L.H. Tsoukalas, 'Soft Computing Technologies in Nuclear Engineering Application,' Invited State-of-the-Art Paper, Advances in Nuclear Science and Technology, Volume 34, pp 13-75, Plenum Press, 1998
24	Gribok, A. V., Attieh, I., Hines, J, W., and Uhrig, R. E., 'Stochastic Regularization of Feedwater Flow Rate Evaluation for Venturi Meter Fouling Problems in Nuclear Power Plants,' Inverse Problems in Engineering. Vol.0, pp.1-26, 2001
25	Hadamand, J., Lectures on 'Cauchy's Problem in Linear Partial Differential Equations,' Yale University Press, New Haven, CN, 1923
26	Gross, K. C., Singer, R. M., Wegerich, S. W., Herzog, J. P., Van Alstine, R., and Bockhorst, F. K., Application of a Model-based Fault Detection System to Nuclear Plant Signals, Proc. of the International Conference. on Intelligent System Application to Power Systems, Seoul, Korea. pp.60-65, 1997
27	Tikhonov, A.N., 'Solution of Incorrectly Formulated Problems and the Regularization Method, Doklady Akad. Nauk., USSR 151, pp 501-504, 1963
28	Uhrig, R. E. and Z. Guo, 'Use of Neural Networks in Nuclear Power Plant Diagnostics,' Proceedings of the 'International Conference on Availability Improvements in Nuclear Power Plants,' Madrid, Spain, April 10-14, 1989
29	Weiss, L. G. and Nicholas, N. C., 'Intelligent Controller Architecture for Full Autonomy and HMI,' 10th International Conference on Robotics and Remote Systems for Hazardous Environments,' Gainesville, FL, 2004
30	Agent Working Group, 'Agent Technology Green Paper', OMG Document ec/2000-08-01, Version 1.0, Object Management Group, Needham, MA, 2000
31	Favennec, J.M., A. Viette, S. Szaleniec, P. Ambos, and M. Kuntz, 'Improving power plant performance with process data reconciliation', International Atomic Energy Agency Technical Meeting on 'Increasing instrument calibration interval through on-line calibration technology', OECD Halden Reactor Project, Halden, Norway, September 2004
32	Irvine, C., 'A Study of Nuclear Plant Heat Rate Optimization Using Nonlinear Artificial Intelligence and Linear Statistical Analysis Models', PhD Dissertation, The University of Tennessee, Nuclear Engineering Department, Knoxville, TN, 2000
33	Uhrig, R.E., 'Trends in Computational Intelligence in Nuclear Engineering', 5th International Conference on Fuzzy Logic and Intelligent Technologies in Nuclear Science (FLINS), Gent, Belgium, 2002
34	Uhrig, R.E., 'Multi-Agent Based Anticipatory Control for Enhancing the Safety and Performance of Generation IV Nuclear Power Plants during Long-Term, Semi-Autonomous Operation,' SMORN-8 (Special Meeting on Reactor Noise), Goteborg, Sweden, 2002
35	Sunde, S., Berg, O., 'Data reconciliation and fault detection by means of plant-wide mass and energy balances', Progress in Nuclear Energy, Vol 43, no , 2003 DOI ScienceOn
36	Hines, J.W and A. Usynin, 'On-Line Thermodynamic Efficiency Monitoring and Optimization Using Empirical Modeling Techniques', 15th Annual Joint ISA POWID/EPRI Controls and Instrumentation Conference 48th Annual ISA POWID Symposium, Nashville, TN, June 2005
37	Sunde, S., Berg, O., Jonson, A., Sonesson L.P., and Hafstad, S., Optimizing 'Nuclear Steam-Turbine Cycles: Data Reconciliation and Real-Time Optimization', Proceedings of the 3rd ANS International Topical Meeting on Nuclear Plant Instrumentation, Control and Human-Machine Interface Technologies, NPIC&HMIT 2000, Washington DC. 2000
38	Hines, J.W, A. Gribok, A. Urmanov and R.E. Uhrig, 'Heuristic, Systematic, and Informational Regularization for Process Monitoring', International Journal of Intelligent Systems on Intelligent Systems for Process Monitoring, Wiley Publishers, Vol. 17, No. 8, pp. 723-750, 2002 DOI ScienceOn
39	Box, G.E.P. and Draper, N.R., Empirical Model-Building and Response Surfaces, J.Wiley, New York, 1987
40	Hashemian, H.M., G.W. Morton, B.D. Shumaker, D. Lillis, S. Orme, Calibration Reduction System at the Sizewell B Nuclear Power Plant, 4th International Topical Meeting on Nuclear Plant Instrumentation, Control and Human Machine Interface Technology (NPIC&HMIT'04), Columbus Ohio, 2004
41	Gribok, A. V., Attieh, I., Hines, J, W., and Uhrig, R. E., 'Regularization of Feedwater Flow Rate Evaluation for Venturi Meter Fouling Problems in Nuclear Power Plants,'Nuclear Technology, vol. 134, pp.3-14, 2001
42	EPRI, On-Line Monitoring Cost Benefit Guide, EPRI, Palo Alto, CA: 1006777, 2003
43	Rasmussen, B. and J.W. Hines, 'Uncertainty Estimation Techniques for Empirical Model Based Condition Monitoring', 6th International FLINS Conference on Applied Computational Intelligence, Blankenberge, Belgium, 2004
44	Wegerich, S, R. Singer, J. Herzog, and A. Wilks, 'Challenges Facing Equipment Condition Monitoring Systems', Proc. Maintenance and Reliability Conference, Gatlinburg, TN, 2001
45	Coppock, S., and T. Holton, 'Advanced Software Detects Temperature Indicator Failure', Transactions of the American Nuclear Society, New Orleans, LA, 2003
46	Gross, K.C., K.K. Hoyer, 'Spectrum Transformed Sequential Testing Method for Signal Validation Application', 8th Power Plant Dynamics Control and Testing Symposium, Knoxville, TN, 1992
47	Miron, A., Ph.D. Dissertation, 'A Wavelet Approach for Development and Application of a Stochastic Parameter Simulation System,' University of Cincinnati, Cincinnati,Ohio, August 2001
48	Miron A. and J. Christenson, 'The stochastic parameter simulation system: a wavelet-based methodology for perfect signal reconstruction,' ANS Topical Meeting in Mathematics and Computations, Gatlinburg, TN, 2003
49	Rasmussen, B., J.W. Hines, 'Uncertainty Estimation for Empirical Signal Validation Modeling', 4th International Topical Meeting on Nuclear Plant Instrumentation, Control and Human Machine Interface Technology (NPIC&HMIT'04), Columbus Ohio, 2004
50	Guo, Z. and R.E. Uhrig, 'Use of Artificial Neural Networks to Analyze Nuclear Power Plant Performance,' Nuclear Technology, Vol. 99, pp 36-42., 1992 DOI
51	Roverso, D., 'Plant Diagnostics by Transient Classification: the ALADDIN Approach,' Special Issue on Intelligent Systems for Plant Surveillance and Diagnostics, International Journal of Intelligent Systems, Vol. 17, No. 8, pp. 767-790, Wiley Periodicals Inc., July 31, 2002 DOI ScienceOn
52	Uhrig, R.E.; Tsoukalas, L.H.; Ikonomopoulos, A., Essawy, M., Black, C.; and Yancey, S., 'Using Neural Networks to Monitor the Operability of Check Valves,' Proceedings of the Conference on Expert System Applications for the Electric Power Industry, Phoenix, AZ, Dec. 8-10, 1993
53	Electric Power Research Institute EPRI, Implementation of On-Line Monitoring for Technical Specification Instruments, EPRI, Palo Alto, CA: 1006833, 2002
54	Gross, K.C., K.K. Hoyer, 'Reactor Parameter Signal Simulator', Fourth International Conference on Simulation Methods in Nuclear Engineering, Montreal Canada, June,1993
55	Hines, J. W., Gribok, A. V., Attieh, I., and Uhrig, R. E., 'Regularization Methods for Inferential Sensing in Nuclear Power Plants', Fuzzy Systems and Soft Computing in Nuclear Engineering, Ed. Da Ruan, Springer, 1999
56	Gribok, A.V.; Hines, J. W.; Urmanov, A. M.; and Uhrig, R. E., 'Regularization of Ill-Posed Surveillance and Diagnostic Measurements,' Workshop on Power PlantSurveillance and Diagnostics, Halden, Norway, September 3-4, 2001. Chapter in Intelligent Systems for Process Monitoring and Diagnostics, Physica-Verlag, D. Ruan and P. F. Fontoni, Editors, 2002
57	Zavaljevski, N., A. Miron, C. Yu, and E. Davis, 'A Study Of On-Line Monitoring Uncertainty Based On Latin Hypercube Sampling And Wavelet De-Noising', 4th Int. Topical Meeting on Nuclear Plant Instrumentation, Control and Human Machine Interface Technology (NPIC&HMIT'04), Columbus Ohio, 2004
58	Zavaljevski, N., K.C. Gross, and S.W. Wegerich, Regularization Methods for the Multivariate State Estimation Technique (MSET), Proc. of the Int. Conf. on Mathematics and Computations, Reactor Physics and Environmental Analysisin Nuclear Applications, Madrid Spain, pp.720-729, 1999
59	Hines, J.W., Gribok, A.V., Attieh, I.,and Uhrig. R.E., 'Neural Network Regularization Techniques for a Sensor Validation System', Transactions of the American Nuclear Society Annual Meeting, San Diego, California, June 4-8, 2000