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)
  • Published : 2005.04.01

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

References

  1. 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
  2. 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
  3. Thie, J.S., 'Reactor Noise' Rowman and Littlefield, New York., 1963
  4. 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
  5. 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
  6. 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
  7. 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
  8. Mott, J., S., Young, and R.W. King, 'Pattern Recognition Software for Plant Surveillance', US DOE Report, 1987
  9. 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
  10. Upadhyaya, B.R., 'Sensor Failure Detection and Estimation', Nuclear Safety, 1985
  11. Upadhyaya, B.R., and E. Eryurek, 'Application of Neural Networks for Sensor Validation and Plant Monitoring,' Nuclear Technology, vol. 97, pp. 170-176, 1992
  12. 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
  13. 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
  14. 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
  15. Electric Power Research Institute EPRI, Instrument Calibration and Monitoring Program, Volume 1: Basis for the Method, EPRI, Palo Alto, CA: 103436-V1, 1993
  16. Electric Power Research Institute EPRI, EPRI Topical Report (TR) 104965, 'On-Line Monitoring of Instrument Channel Performance', Final Report, 1998
  17. 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
  18. 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
  19. 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
  20. 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
  21. 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
  22. Electric Power Research Institute EPRI, Implementation of On-Line Monitoring for Technical Specification Instruments, EPRI, Palo Alto, CA: 1006833, 2002
  23. 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
  24. Gross, K.C., K.K. Hoyer, 'Reactor Parameter Signal Simulator', Fourth International Conference on Simulation Methods in Nuclear Engineering, Montreal Canada, June,1993
  25. 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
  26. 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
  27. 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
  28. 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
  29. 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
  30. Wegerich, S, R. Singer, J. Herzog, and A. Wilks, 'Challenges Facing Equipment Condition Monitoring Systems', Proc. Maintenance and Reliability Conference, Gatlinburg, TN, 2001
  31. Coppock, S., and T. Holton, 'Advanced Software Detects Temperature Indicator Failure', Transactions of the American Nuclear Society, New Orleans, LA, 2003
  32. EPRI, On-Line Monitoring Cost Benefit Guide, EPRI, Palo Alto, CA: 1006777, 2003
  33. 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
  34. 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
  35. Hadamand, J., Lectures on 'Cauchy's Problem in Linear Partial Differential Equations,' Yale University Press, New Haven, CN, 1923
  36. 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
  37. Tikhonov, A.N., 'Solution of Incorrectly Formulated Problems and the Regularization Method, Doklady Akad. Nauk., USSR 151, pp 501-504, 1963
  38. 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
  39. 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
  40. 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 https://doi.org/10.13182/NT92-A34635
  41. 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
  42. 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
  43. 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
  44. 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
  45. 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.
  46. 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 https://doi.org/10.1002/int.10049
  47. 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
  48. 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 https://doi.org/10.13182/NT92-A34701
  49. 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
  50. 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 https://doi.org/10.1002/int.10047
  51. Box, G.E.P. and Draper, N.R., Empirical Model-Building and Response Surfaces, J.Wiley, New York, 1987
  52. 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
  53. 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
  54. 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 https://doi.org/10.1016/S0149-1970(03)00015-5
  55. 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
  56. 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
  57. 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
  58. Agent Working Group, 'Agent Technology Green Paper', OMG Document ec/2000-08-01, Version 1.0, Object Management Group, Needham, MA, 2000
  59. 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