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Upgrading automation for nuclear fuel in-core management: from the symbolic generation of configurations to the neural adaptation of heuristics

Upgrading automation for nuclear fuel in-core management: from the symbolic generation of configurations to the neural adaptation of heuristics

Nissan, Ephraim, Siegelmann, Hava, Galperin, Alex and Kimhi, Shuky (1997) Upgrading automation for nuclear fuel in-core management: from the symbolic generation of configurations to the neural adaptation of heuristics. Engineering with Computers, 13 (1). pp. 1-19. ISSN 0177-0667 (Print), 1435-5663 (Online) (doi:10.1007/BF01201857)

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Abstract

FUELCON is an expert system in nuclear engineering. Its task is optimized refueling-design, which is crucial to keep down operation costs at a plant. FUELCON proposes sets of alternative configurations of fuel-allocation; the fuel is positioned in a grid representing the core of a reactor. The practitioner of in-core fuel management uses FUELCON to generate a reasonably good configuration for the situation at hand. The domain expert, on the other hand, resorts to the system to test heuristics and discover new ones, for the task described above. Expert use involves a manual phase of revising the ruleset, based on performance during previous iterations in the same session. This paper is concerned with a new phase: the design of a neural component to carry out the revision automatically. Such an automated revision considers previous performance of the system and uses it for adaptation and learning better rules. The neural component is based on a particular schema for a symbolic to recurrent-analogue bridge, called NIPPL, and on the reinforcement learning of neural networks for the adaptation.

Item Type: Article
Uncontrolled Keywords: allocation, design, downtime, expert systems, machine learning, neural networks, nuclear engineering, in-core fuel management, refueling, reload
Subjects: Q Science > QA Mathematics
Faculty / Department / Research Group: Faculty of Architecture, Computing & Humanities
Related URLs:
Last Modified: 14 Oct 2016 08:59
Selected for GREAT 2016: None
Selected for GREAT 2017: None
Selected for GREAT 2018: None
URI: http://gala.gre.ac.uk/id/eprint/50

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