Predicting BWR Criticality with Data-Driven Machine Learning Model

TL;DR

This paper introduces a data-driven deep learning approach to predict the criticality of boiling water reactors, aiming to optimize fuel usage and improve operational efficiency in nuclear power plants.

Contribution

The paper presents a novel machine learning model specifically designed for estimating reactor criticality, enhancing accuracy over traditional methods.

Findings

Deep learning model accurately predicts reactor criticality.

Potential for optimizing fuel cycle management.

Improves operational safety and economic efficiency.

Abstract

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.