Development of Neural Network Model With Explainable AI for Measuring Uranium Enrichment

Abstract

In this work, we have developed a neural network (NN) model that can analyze enrichment from depleted (0.2%) to low enriched uranium (4.5%) when UO2 waste with very low radioactivity was contained in a 1-L Marinelli beaker, even when the measurement time is as short as 10 s using a low-resolution detector. The average count rate was about 3800 cps. Measurement of uranium enrichment is necessary for quantitative analysis of uranium radioactivity for disposal of uranium waste. Previously studied uranium enrichment methods (infinite thickness (IT) method, peak ratio (PR) method, and relative-efficiency (RE) method) are difficult to use for field measurement due to many limitations of the algorithms. Among existing methods, the RE method is accurate but requires a long measurement time; there is also a limitation in which a high-resolution detector is essential. In this work, we proposed a model to predict uranium enrichment using a low-resolution detector and an artificial NN model. Furthermore, we validated the results of the NN models using an explainable AI algorithm and principal component analysis (PCA). When the measurement time was less than 60 s, the existing method failed to analyze uranium enrichment, but the proposed model can predict enrichment of uranium within 5% of relative error when 5 g of uranium powder was mixed with various waste (ash, soil, and concrete).