Predicting Melting Temperatures of Freeze Valve in Molten Salt Reactors using LSTM-Based Deep Learning Models Robi Dany Riupassa (a), Khairul Basar (b), Novitrian (b), and Abdul Waris (b,*)
(a) Department of Physics, Faculty of Mathematics and Natural Sciences, Institut Teknologi Bandung
Jalan Ganesha 10, Bandung 40132, Indonesia
(b) Nuclear Physics and Biophysics Research Division, Department of Physics, Faculty of Mathematics and Natural Sciences, Institut Teknologi Bandung
Jalan Ganesha 10, Bandung 40132, Indonesia
*awaris[at]itb.ac.id
Abstract
The realm of molten salt reactors (MSRs) presents unique challenges and opportunities in the pursuit of advancing nuclear energy technology. One pivotal aspect is the development of safety mechanisms, including freeze valves, aimed at enhancing the security and operational integrity of these reactors. Freeze valves, designed to halt the flow of molten salt in emergency scenarios, play a critical role in averting potential hazardous situations. This research paper investigates the application of deep learning models for predicting melting temperatures, leveraging time-series temperature data, specifically targeting the freeze valve technology within the context of molten salt reactors.
The research methodology encompasses the creation of a synthetic dataset representative of freeze valve conditions in MSRs. Each dataset entry comprises a time-series temperature profile associated with the freeze valve and its corresponding melting temperature. To capture the intricate temporal relationships inherent in these profiles, a specialized Recurrent Neural Network (RNN) architecture, incorporating Long Short-Term Memory (LSTM) cells, is developed and trained. The model^s training is augmented by preprocessing techniques, including the normalization of time-series temperature data.
Evaluation of the model^s predictive capabilities focuses on its performance in forecasting the melting temperatures of the freeze valve. Metrics such as mean squared error (MSE) and mean absolute error (MAE) are utilized to quantify the model^s accuracy and robustness. The study delves into the influence of architectural configurations, hyperparameter tuning, and dataset characteristics on the model^s predictions, shedding light on the factors that contribute to optimal predictive performance.
The outcomes of this research highlight the potential of deep learning models in predicting the melting temperatures of freeze valves in the context of MSRs. The developed model not only showcases an ability to anticipate impending safety-critical events but also underscores the significance of leveraging time-series temperature data to enhance the integrity of safety mechanisms in nuclear reactor systems. Ultimately, this research contributes to the pursuit of safer and more efficient nuclear energy solutions, offering valuable insights into predictive modeling techniques within the complex landscape of molten salt reactor technologies.
