Moisture-carryover performance optimization using physics-constrained machine learning

Abstract A data-driven model for predicting moisture carryover (MCO) in the General Electric Type-4 boiling water reactor (BWR) was constructed using a physics-constrained artificial intelligence technique. An accurate prediction of the MCO is of great value for commercial BWR operators as it can be used to modify the operational plan during a power cycle to mitigate high MCO, thereby avoiding elevated dose to on-site personnel and damage to turbine components. Using data from operational plants and preliminary features selected through physics and engineering analyses, a neural network based model for predicting MCO was built. A final feature set was then obtained through a hyperspace optimization performed using a genetic algorithm. Multiple neural network models possessing good generalization were obtained, the best of these having a mean-square error (MSE) of 9.69E-5 for prediction of an un-seen cycle, which is in agreement with the uncertainty in the measured MCO data. This predictive capability is of great value for the planning of a power generation cycle, and for scheduling of operations for cycles already underway.