Digital Twin for Cryogenic Ejector Systems: Integrating Advanced Machine Learning and Dynamic Modeling

This paper presents an integrated Digital Twin (DT) framework for cryogenic ejector systems designed for Boil Off Gas (BOG) management in Liquefied Natural Gas (LNG) applications. Building on prior experimental and numerical studies, the proposed DT improves both predictive accuracy and dynamic adaptability by coupling Physics Informed Neural Networks (PINNs) with a transient dynamic model. The PINN integrates compressible flow conservation laws into its loss function, ensuring physical consistency during learning. A dataset of 1000 operating points was analyzed, revealing that the primary pressure (Pp) is the dominant factor influencing the entrainment ratio (ER). A baseline linear regression achieved an R2 equal to 0.791, while the PINN increased predictive accuracy to an R2 equal to 0.98. The dynamic model simulates the transient response of the ejector to sudden variations in BOG load, demonstrating the DT capability to anticipate system instability and enable real time control. Together, these components create a physically interpretable and computationally efficient digital framework capable of supporting the design, optimization, and operation of cryogenic ejectors. The results highlight the potential of the proposed DT to enhance energy efficiency, reliability, and safety in LNG processing systems through intelligent, physics based decision making.