Dynamic Control of Sodium Cold Trap Purification Temperature Using LSTM System Identification

Rita Appiah (), Alexander Heifetz, Derek Kultgen, Lefteri H. Tsoukalas and Richard B. Vilim
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Rita Appiah: Nuclear Science and Engineering Division, Argonne National Laboratory, Lemont, IL 60439, USA
Alexander Heifetz: Nuclear Science and Engineering Division, Argonne National Laboratory, Lemont, IL 60439, USA
Derek Kultgen: Nuclear Science and Engineering Division, Argonne National Laboratory, Lemont, IL 60439, USA
Lefteri H. Tsoukalas: School of Nuclear Engineering, Purdue University, West Lafayette, IN 47906, USA
Richard B. Vilim: Nuclear Science and Engineering Division, Argonne National Laboratory, Lemont, IL 60439, USA

Energies, 2024, vol. 17, issue 24, 1-25

Abstract: 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.

Keywords: sodium fast reactors; sodium cold trap purification; modeling and simulation; LSTM-MPC; system identification; multi-step prediction; thermal hydraulics (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2024
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