Predicting BWR Criticality with Data-Driven Machine Learning Model

One of the challenges in operating nuclear power plants is to decide the amount of fuel needed in a cycle. Large-scale nuclear power plants are designed to operate at base load, meaning that they are expected to always operate at full power. Economically, a nuclear power plant should burn enough fue...

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Bibliographic Details
Published in:arXiv.org 2024-11
Main Authors: Muhammad Rizki Oktavian, Tunga, Anirudh, Nistor, Jonathan, Tusar, James, Gruenwald, J Thomas, Xu, Yunlin
Format: Article
Language:English
Subjects:
Boiling water reactors
Deep learning
Fuels
Machine learning
Nuclear power plants
Plant layout
Online Access:Get full text
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Summary:One of the challenges in operating nuclear power plants is to decide the amount of fuel needed in a cycle. Large-scale nuclear power plants are designed to operate at base load, meaning that they are expected to always operate at full power. Economically, a nuclear power plant should burn enough fuel to maintain criticality until the end of a cycle (EOC). If the reactor goes subcritical before the end of a cycle, it may result in early coastdown as the fuel in the core is already depleted. On contrary, if the reactor still has significant excess reactivity by the end of a cycle, the remaining fuels will remain unused. In both cases, the plant may lose a significant amount of money. This work proposes an innovative method based on a data-driven deep learning model to estimate the excess criticality of a boiling water reactor.
ISSN:2331-8422
DOI:10.48550/arxiv.2411.07425