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

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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.

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Author notes

  1. Hava Siegelmann

    Present address: Department of Industrial Engineering and Management, The Technion, Haifa, Israel

  2. Alex Galperin

    Present address: Department of Nuclear Engineering, Ben-Gurion University of the Negev, Beer-Sheva, Israel

  3. Shuky Kimhi

    Present address: Kernforschungszentrum Karlsruhe, Institut für Neutronenphysik und Reaktortechnik, Postfach 3640, D-76021, Karlsruhe, Germany

Authors and Affiliations

  1. Department of Mathematics and Computer Science, Bar-Ilan University, Ramat-Gan, Israel

    Ephraim Nissan & Hava Siegelmann

  2. Department of Nuclear Engineering, Ben-Gurion University of the Negev, Beer-Sheva, Israel

    Shuky Kimhi

Authors
  1. Ephraim Nissan
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  2. Hava Siegelmann
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  3. Alex Galperin
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  4. Shuky Kimhi
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Correspondence to Ephraim Nissan.

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Nissan, E., Siegelmann, H., Galperin, A. et al. Upgrading automation for nuclear fuel in-core management: From the symbolic generation of configurations, to the neural adaptation of heuristics. Engineering with Computers 13, 1–19 (1997). https://doi.org/10.1007/BF01201857

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