Optimisation of used nuclear fuel canister loading using a neural network and genetic algorithm

• Published in: Neural computing & applications Vol. 33; no. 23; pp. 16627 - 16639
• Main Authors: Solans, Virginie, Rochman, Dimitri, Brazell, Christian, Vasiliev, Alexander, Ferroukhi, Hakim, Pautz, Andreas
• Format: Journal Article
• Language: English
• Published: London Springer London 01.12.2021; Springer Nature B.V
• Subjects: Artificial Intelligence; Artificial neural networks; Assemblies; Computational Biology/Bioinformatics; Computational Science and Engineering; Computer Science; Data Mining and Knowledge Discovery; Depletion; Genetic algorithm; Genetic algorithms; Geology; High-level nuclear waste; Image Processing and Computer Vision; Multiplication; Neural network; Neural networks; Nuclear fuels; Nuclear reactors; Nuclear safety; Optimization; Original Article; Pressurized water reactors; Probability and Statistics in Computer Science; Reactor cores; Reprocessing; Spent nuclear fuels; Genetic algorithm; High-level nuclear waste; Neural network
• ISSN: 0941-0643; 1433-3058; 1433-3058
• DOI: 10.1007/s00521-021-06258-2

This paper presents an approach for the optimisation of geological disposal canister loadings, combining high resolution simulations of used nuclear fuel characteristics with an articial neural network and a genetic algorithm. The used nuclear fuels (produced in an open fuel cycle without reprocessing) considered in this work come from a Swiss Pressurised Water Reactor, taking into account their realistic lifetime in the reactor core and cooling periods, up to their disposal in the final geological repository. The case of 212 representative used nuclear fuel assemblies is analysed, assuming a loading of 4 fuel assemblies per canister, and optimizing two safety parameters: the fuel decay heat (DH) and the canister effective neutron multiplication factor k eff . In the present approach, a neural network is trained as a surrogate model to evaluate the k eff value to substitute the time-consuming-code Monte Carlo transport & depletion SERPENT for specific canister loading calculations. A genetic algorithm is then developed to optimise simultaneously the canister k eff and DH values. The k eff computed during the optimisation algorithm is using the previously developed artificial neural network. The optimisation algorithm allows (1) to minimize the number of canisters, given assumed limits for both DH and k eff quantities and (2) to minimize DH and k eff differences among canisters. This study represents a proof-of-principle of the neural network and genetic algorithm capabilities, and will be applied in the future to a larger number of cases.