Dynamic Control of Sodium Cold Trap Purification Temperature Using LSTM System Identification
This study investigates the dynamic regulation of the sodium cold trap purification temperature at Argonne National Laboratory’s liquid sodium test facility, employing long short-term memory (LSTM) system identification techniques. The investigation introduces an innovative hybrid approach by integr...
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| Format: | Article |
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MDPI AG
2024-12-01
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| Series: | Energies |
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| Online Access: | https://www.mdpi.com/1996-1073/17/24/6257 |
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| author | Rita Appiah Alexander Heifetz Derek Kultgen Lefteri H. Tsoukalas Richard B. Vilim |
| author_facet | Rita Appiah Alexander Heifetz Derek Kultgen Lefteri H. Tsoukalas Richard B. Vilim |
| author_sort | Rita Appiah |
| collection | DOAJ |
| description | This study investigates the dynamic regulation of the sodium cold trap purification temperature at Argonne National Laboratory’s liquid sodium test facility, employing long short-term memory (LSTM) system identification techniques. The investigation introduces an innovative hybrid approach by integrating model predictive control (MPC) based on first principles dynamic models with a multi-step time–frequency LSTM model in predicting the temperature profiles of a sodium cold trap purification system. The long short-term memory–model predictive controller (LSTM-MPC) model employs a sliding window scheme to gather training samples for multi-step prediction, leveraging historical data to construct predictive models that capture the non-linearities of the complex system dynamics without explicitly modeling the underlying physical processes. The performance of the LSTM-MPC and MPC were evaluated through simulation experiments, where both models were assessed on their capacity to maintain the cold trap temperature within predefined set-points while minimizing deviations and overshoots. Results obtained show how the data-driven LSTM-MPC model demonstrates stability and adaptability. In contrast, the traditional MPC model exhibits irregularities, particularly evident as overshoots around set-point limits, which can potentially compromise its effectiveness over long prediction time intervals. The findings obtained offer valuable insights into integrating data-driven techniques for enhancing real-time monitoring systems. |
| format | Article |
| id | doaj-art-539f2cc9e9774b719aa0b38acff928f2 |
| institution | DOAJ |
| issn | 1996-1073 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Energies |
| spelling | doaj-art-539f2cc9e9774b719aa0b38acff928f22025-08-20T02:43:29ZengMDPI AGEnergies1996-10732024-12-011724625710.3390/en17246257Dynamic Control of Sodium Cold Trap Purification Temperature Using LSTM System IdentificationRita Appiah0Alexander Heifetz1Derek Kultgen2Lefteri H. Tsoukalas3Richard B. Vilim4Nuclear Science and Engineering Division, Argonne National Laboratory, Lemont, IL 60439, USANuclear Science and Engineering Division, Argonne National Laboratory, Lemont, IL 60439, USANuclear Science and Engineering Division, Argonne National Laboratory, Lemont, IL 60439, USASchool of Nuclear Engineering, Purdue University, West Lafayette, IN 47906, USANuclear Science and Engineering Division, Argonne National Laboratory, Lemont, IL 60439, USAThis study investigates the dynamic regulation of the sodium cold trap purification temperature at Argonne National Laboratory’s liquid sodium test facility, employing long short-term memory (LSTM) system identification techniques. The investigation introduces an innovative hybrid approach by integrating model predictive control (MPC) based on first principles dynamic models with a multi-step time–frequency LSTM model in predicting the temperature profiles of a sodium cold trap purification system. The long short-term memory–model predictive controller (LSTM-MPC) model employs a sliding window scheme to gather training samples for multi-step prediction, leveraging historical data to construct predictive models that capture the non-linearities of the complex system dynamics without explicitly modeling the underlying physical processes. The performance of the LSTM-MPC and MPC were evaluated through simulation experiments, where both models were assessed on their capacity to maintain the cold trap temperature within predefined set-points while minimizing deviations and overshoots. Results obtained show how the data-driven LSTM-MPC model demonstrates stability and adaptability. In contrast, the traditional MPC model exhibits irregularities, particularly evident as overshoots around set-point limits, which can potentially compromise its effectiveness over long prediction time intervals. The findings obtained offer valuable insights into integrating data-driven techniques for enhancing real-time monitoring systems.https://www.mdpi.com/1996-1073/17/24/6257sodium fast reactorssodium cold trap purificationmodeling and simulationLSTM-MPCsystem identificationmulti-step prediction |
| spellingShingle | Rita Appiah Alexander Heifetz Derek Kultgen Lefteri H. Tsoukalas Richard B. Vilim Dynamic Control of Sodium Cold Trap Purification Temperature Using LSTM System Identification Energies sodium fast reactors sodium cold trap purification modeling and simulation LSTM-MPC system identification multi-step prediction |
| title | Dynamic Control of Sodium Cold Trap Purification Temperature Using LSTM System Identification |
| title_full | Dynamic Control of Sodium Cold Trap Purification Temperature Using LSTM System Identification |
| title_fullStr | Dynamic Control of Sodium Cold Trap Purification Temperature Using LSTM System Identification |
| title_full_unstemmed | Dynamic Control of Sodium Cold Trap Purification Temperature Using LSTM System Identification |
| title_short | Dynamic Control of Sodium Cold Trap Purification Temperature Using LSTM System Identification |
| title_sort | dynamic control of sodium cold trap purification temperature using lstm system identification |
| topic | sodium fast reactors sodium cold trap purification modeling and simulation LSTM-MPC system identification multi-step prediction |
| url | https://www.mdpi.com/1996-1073/17/24/6257 |
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