Optimizing near-carbon-free nuclear energy systems: advances in reactor operation digital twin through hybrid machine learning algorithms for parameter identification and state estimation

Abstract

Accurate and efficient online parameter identification and state estimation are crucial for leveraging digital twin simulations to optimize the operation of near-carbon-free nuclear energy systems. In previous studies, we developed a reactor operation digital twin (RODT). However, non-differentiabilities and discontinuities arise when employing machine learning-based surrogate forward models, challenging traditional gradient-based inverse methods and their variants. This study investigated deterministic and metaheuristic algorithms and developed hybrid algorithms to address these issues. An efficient modular RODT software framework that incorporates these methods into its post-evaluation module is presented for comprehensive comparison. The methods were rigorously assessed based on convergence profiles, stability with respect to noise, and computational performance. The numerical results show that the hybrid KNNLHS algorithm excels in real-time online applications, balancing accuracy and efficiency with a prediction error rate of only 1% and processing times of less than 0.1 s. Contrastingly, algorithms such as FSA, DE, and ADE, although slightly slower (approximately 1 s), demonstrated higher accuracy with a 0.3% relative \(L_2\) error, which advances RODT methodologies to harness machine learning and system modeling for improved reactor monitoring, systematic diagnosis of off-normal events, and lifetime management strategies. The developed modular software and novel optimization methods presented offer pathways to realize the full potential of RODT for transforming energy engineering practices.