An intelligent system for interpreting the nuclear refuelling process within an advanced gas-cooled reactor

Abstract

Evaluation of the data produced during the refuelling process in a nuclear power plant is required to ensure proper ‘set-down’ of the fuel assembly, thereby allowing the continued and safe operation of the station. The process of evaluating the data can be time consuming owing to the large amounts of data requiring considerable domain experience and interpretation. This paper presents an intelligent system (IS) to automate the process of data analysis, thereby shortening the evaluation time and providing an explanation of the reasoning behind its conclusions. The intelligent system utilizes a knowledge-based system (KBS), neural network based classification, K-means clustering techniques and rule induction methods to evaluate the data and inform the operator of any errors encountered.

Keywords

intelligent systems knowledge based systems neural network nuclear fuels nuclear power generation pattern classification

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References

Kohonen

The self-organising map. Proc. IEEE, 1990, 78, 1464–1480.

Quinlan

J. R.

C4.5-Programs for Machine Learning, 1993 (Morgan Kaufmann).

Tarassenko

Guide to Neural Computing Applications, 1998 (Butterworth-Heinemann).

Luger

Stubblefield

Artificial Intelligence: Stuctures and Strategies for Complex Problem Solving, 1997, p. 219 (Benjamin/Cummings).

Beck

C. E.

Behera

A. K.

A bibliography of technical papers relating expert systems to nuclear power plants. IEEE Trans. Energy Conversion, March 1993, 8(1), p. 130.

Galperin

Kimhi

Nissan

FUELCON: An expert system for assisting the practice and research of in-core fuel management and optimal design in nuclear engineering. Computers and Artif. Intell., 1993, 12(4), 369–414.

Nissan

(Ed.) Forum on refuelling techniques for nuclear power plants (5 papers). The New Rev. Appl. Expert Syst., 1998, 4, 139–194.

Zhao

Nissan

Knight

Soper

FUELGEN: Effective evolutionary design of refuellings for pressurised water reactors In Intelligent Technologies for Electric and Nuclear Power Systems (Ed. Nissan

), special issue, Computers and Artif. Intell., 1998, 17(2/3), 105–125.

Kang

S. J.

Moon

J. C.

Choi

D. H.

Choi

S. S.

Woo

H. G.

A distributed and intelligent system approach for the automatic inspection of steam-generator tubes in nuclear power plants. IEEE Trans. Nucl. Sci., June 1998, 45(3), Pt 4, 1713.

10.

Moyes

Burt

G. M.

McDonald

J. R.

Capener

J. R.

Dray

J. N.

Goodfellow

Combining design and operational knowledge to enhance generator plant diagnostics. IEEE Proc. Generation, Transm. and Distribution, May 1996, 143(3), 300.

11.

Steele

J. A.

Martin

L. A.

Moyes

McArthur

S. D. J.

McDonald

J. R.

Young

Elrick

Howie

Yule

I. Y.

Towards an esitmation aid for nuclear power plant refuelling operations. In Proceedings of 13th International Conference on Industrial and Engineering Applications of Artificial Intelligence and Expert Systems, IEA/AIE 2000, New Orleans, Louisiana, June 2000, pp. 56–67.

12.

Furukawa

Ueda

Kitamura

A systematic method for rational definition of plant diagnostic symptoms by self-organizing neural networks. Neurocomputing, October 1996, 13(2-4), 171–183.

13.

Avizienis

Fault-tolerant systems. IEEE Trans. Computers, December 1976, C-25(12), 1304–1312.

14.

The use of computers in safety-critical applications. Health and Safety Commission, 1998.

15.

Available at http://www.w3c.org.

16.

Sammon

J. W.

Jr. , A nonlinear mapping for data structure analysis. IEEE Trans. Computers, May 1969, 18(5), 401–409.

17.

Kraaijveld

M. A.

Mao

Jain

A. K.

A nonlinear projection method based on Kohonen's topology preserving maps. IEEE Trans. Neural Networks, May 1995, 6(3), 548–559.