Development and assessment of prognosis digital twin in a NAMAC system. (15th December 2022) - Lin, Linyu; Gurgen, Anil; Dinh, Nam
Highlights: Development of prognosis digital twin with LSTM recurrent network. Improvement of prognosis digital twin for better prediction and decision-making. Investigation of manual search, Bayesian approach, and physics-guided ML. Abstract: The nearly autonomous management and control (NAMAC) system is a comprehensive control system to assist plant operations by furnishing control recommendations to operators. Prognosis digital twin (DT-P) is a critical component in NAMAC for predicting action effects and supporting NAMAC decision-making during normal and accident scenarios. To quantifying and reducing uncertainty of machine-learning-based DT-Ps in multi-step predictions, this work investigates and derives insights from the application of three techniques for optimizing the performance of DT-P by long short-term memory recurrent neural networks, including manual search, sequential model-based optimization, and physics-guided machine learning. Sequential model-based optimization and physics-guide machine learning result in smallest errors when the predicting transients are similar to the training data.