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.

使用物理受限的人工智能技术，构建了一个数据驱动模型，用于预测通用电气4型沸水反应堆（BWR）中的水分携带量（MCO）。MCO的准确预测对于商用BWR运营商而言具有重要价值，因为它可用于在电源循环期间修改操作计划以减轻高MCO，从而避免增加对现场人员的剂量以及对涡轮机部件的损坏。利用来自运营工厂的数据以及通过物理和工程分析选择的初步特征，建立了基于神经网络的MCO预测模型。然后通过使用遗传算法执行的超空间优化获得最终特征集。获得了具有良好泛化能力的多个神经网络模型，其中最好的均方根误差（MSE）为9.69E-5，用于预测未见周期，这与测得的MCO数据的不确定性相符。这种预测能力对于发电周期的规划以及已经进行的周期的运行调度具有巨大的价值。