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논문 기본 정보
- 자료유형
- 학술저널
- 저자정보
- 발행연도
- 2022.2
- 수록면
- 644 - 652 (9page)
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초록· 키워드
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During the pyroprocessing operation, various signals can be collected by process monitoring (PM). Thesesignals are utilized to diagnose process states. In this study, feasibility of using PM for nuclear safeguardsof electrorefining operation was examined based on the use of machine learning for detecting off-normaloperations. The off-normal operation, in this study, is defined as co-deposition of key elements throughreduction 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-tuningto minimize data requirement for training. The resulting models were found to classify the normal andthe off-normal operation states with a 95% accuracy. With the availability of more process data, theapproach is expected to have higher reliability
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