Nuclear Engineering and Technology

Korean Nuclear Society (한국원자력학회)
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Investigation of neural network-based cathode potential monitoring to support nuclear safeguards of electrorefining in pyroprocessing

  • Jung, Young-Eun (Department of Nuclear and Quantum Engineering, Korea Advanced Institute of Science and Technology) ;
  • Ahn, Seong-Kyu (Advanced Fuel Cycle System Research Division, Korea Atomic Energy Research Institute) ;
  • Yim, Man-Sung (Department of Nuclear and Quantum Engineering, Korea Advanced Institute of Science and Technology)
  • Received : 2021.05.06
  • Accepted : 2021.08.16
  • Published : 2022.02.25
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Abstract

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.

Keywords

Acknowledgement

This research was supported by National Nuclear R&D Program (Human Resources Program in Energy Technology) through the National Research Foundation of Korea (NRF) by the Ministry of Science, Science and Technology (NRF-2018M2C7A1A02071198).

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