Investigation of neural network-based cathode potential monitoring to support nuclear safeguards of electrorefining in pyroprocessing
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 de...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2022-02-01
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| Series: | Nuclear Engineering and Technology |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S1738573321005088 |
| _version_ | 1819290656946782208 |
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| author | Young-Eun Jung Seong-Kyu Ahn Man-Sung Yim |
| author_facet | Young-Eun Jung Seong-Kyu Ahn Man-Sung Yim |
| author_sort | Young-Eun Jung |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-12-24T03:26:13Z |
| format | Article |
| id | doaj.art-756cd243bc0b4f159dd542da0652bf5b |
| institution | Directory Open Access Journal |
| issn | 1738-5733 |
| language | English |
| last_indexed | 2024-12-24T03:26:13Z |
| publishDate | 2022-02-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Nuclear Engineering and Technology |
| spelling | doaj.art-756cd243bc0b4f159dd542da0652bf5b2022-12-21T17:17:20ZengElsevierNuclear Engineering and Technology1738-57332022-02-01542644652Investigation of neural network-based cathode potential monitoring to support nuclear safeguards of electrorefining in pyroprocessingYoung-Eun Jung0Seong-Kyu Ahn1Man-Sung Yim2Department of Nuclear and Quantum Engineering, Korea Advanced Institute of Science and Technology, 291 Daehak-ro, Yuseong-gu, Daejeon, 34141, Republic of KoreaAdvanced Fuel Cycle System Research Division, Korea Atomic Energy Research Institute, 111 Daedeok-daero 989beon-gil, Yuseong-gu, 34057, Daejeon, Republic of KoreaDepartment of Nuclear and Quantum Engineering, Korea Advanced Institute of Science and Technology, 291 Daehak-ro, Yuseong-gu, Daejeon, 34141, Republic of Korea; Corresponding author.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.http://www.sciencedirect.com/science/article/pii/S1738573321005088PyroprocessingProcess monitoringSafeguardsElectrorefiningCathode potentialElectro-deposition |
| spellingShingle | Young-Eun Jung Seong-Kyu Ahn Man-Sung Yim Investigation of neural network-based cathode potential monitoring to support nuclear safeguards of electrorefining in pyroprocessing Nuclear Engineering and Technology Pyroprocessing Process monitoring Safeguards Electrorefining Cathode potential Electro-deposition |
| title | Investigation of neural network-based cathode potential monitoring to support nuclear safeguards of electrorefining in pyroprocessing |
| title_full | Investigation of neural network-based cathode potential monitoring to support nuclear safeguards of electrorefining in pyroprocessing |
| title_fullStr | Investigation of neural network-based cathode potential monitoring to support nuclear safeguards of electrorefining in pyroprocessing |
| title_full_unstemmed | Investigation of neural network-based cathode potential monitoring to support nuclear safeguards of electrorefining in pyroprocessing |
| title_short | Investigation of neural network-based cathode potential monitoring to support nuclear safeguards of electrorefining in pyroprocessing |
| title_sort | investigation of neural network based cathode potential monitoring to support nuclear safeguards of electrorefining in pyroprocessing |
| topic | Pyroprocessing Process monitoring Safeguards Electrorefining Cathode potential Electro-deposition |
| url | http://www.sciencedirect.com/science/article/pii/S1738573321005088 |
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