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

Ephraim Nissan, Hava Siegelmann, Alex Galperin, Shuky Kimhi

Research output: Contribution to journalArticlepeer-review

12 Scopus citations

Abstract

FUELCOS 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 FUELCOS 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.

Original languageEnglish
Pages (from-to)1-19
Number of pages19
JournalEngineering with Computers
Volume13
Issue number1
DOIs
StatePublished - 1 Jan 1997

Keywords

  • Allocation
  • Design
  • Downtime
  • Expert systems
  • In-core fuel management
  • Machine learning
  • Neural networks
  • Nuclear engineering
  • Refueling
  • Reload

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