Hydrogen Concentration Prediction in a Passive Autocatalytic Recombiner Using Machine Learning Algorithms

Abstract

Monitoring hydrogen levels within Nuclear Power Plants (NPPs) is crucial to mitigate potential risks during severe accidental scenarios. Passive Autocatalytic Recombiners (PARs), which operate passively through catalytic reactions, are installed in the containment to reduce hydrogen concentration. This study numerically investigates hydrogen behavior within PAR under various accident conditions. As the inlet temperature and hydrogen concentration increase, the reaction rate and maximum catalyst plate temperature rise. As the inflow velocity increases, the reaction amount rises, but the residence time for the reactions decreases, leading to an increase in the outlet hydrogen concentration. Leveraging the operational characteristics of the PAR, the present study develops a data-driven model to identify the correlations among the parameters associated with the PAR's performance and to predict hydrogen concentration at the PAR's outlet by adopting four machine learning algorithms: Artificial Neural Networks (ANN), k-nearest Neighbor (k-NN), Random Forest (RF), and Support Vector Regression (SVR). Using the PAR's inlet variables of flow velocity, temperature, hydrogen concentration, and outlet temperature as input parameters, the ANN model demonstrates good predictive performance with R2 values of about 0.99. The predictive performance of the ANN model remains robust even without the inlet hydrogen information.