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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Bibliographic Details
Published in:Energies (Basel) 2024-12, Vol.17 (24), p.6257
Main Authors: Appiah, Rita, Heifetz, Alexander, Kultgen, Derek, Tsoukalas, Lefteri H., Vilim, Richard B.
Format: Article
Language:English
Subjects:
Cold
Control algorithms
Controllers
Crystallization
Heat exchangers
Heat transfer
Hydrogen
Laws, regulations and rules
Liquid metals
LSTM-MPC
modeling and simulation
multi-step prediction
Neural networks
Nuclear energy
Nuclear reactors
Sensors
Sodium
sodium cold trap purification
sodium fast reactors
system identification
Temperature
Weather forecasting
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Summary: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.
ISSN:1996-1073
1996-1073
DOI:10.3390/en17246257