During the pyroprocessing operation, various signals can be collected by process monitoring (PM). These signals are utilized to diagnose process states. In this study, feasibility of using PM for nuclear safeguards of electrorefining operation was examined based on the use of machine learning for detecting off-normal operations. The off-normal operation, in this study, is defined as co-deposition of key elements through reduction on cathode. The monitored process signal selected for PM was cathode potential. The necessary data were produced through electrodeposition experiments in a laboratory molten salt system. Model-based cathodic surface area data were also generated and used to support model development. Computer models for classification were developed using a series of recurrent neural network architectures. The concept of transfer learning was also employed by combining pre-training and fine-tuning to minimize data requirement for training. The resulting models were found to classify the normal and the off-normal operation states with a 95% accuracy. With the availability of more process data, the approach is expected to have higher reliability.

在高温处理操作期间，可以通过过程监控 (PM) 收集各种信号。这些信号用于诊断过程状态。在这项研究中，基于使用机器学习检测异常操作，检查了使用 PM 进行电解精炼操作的核保障的可行性。在这项研究中，非正常操作被定义为通过在阴极上还原来共沉积关键元素。为 PM 选择的监测过程信号是阴极电位。必要的数据是通过在实验室熔盐系统中的电沉积实验产生的。还生成了基于模型的阴极表面积数据并用于支持模型开发。用于分类的计算机模型是使用一系列循环神经网络架构开发的。通过将预训练和微调相结合，还采用了迁移学习的概念，以最大限度地减少训练的数据需求。发现生成的模型以 95% 的准确度对正常和非正常操作状态进行分类。随着更多过程数据的可用性，该方法有望具有更高的可靠性。