Online learning supported surrogate-based flowsheet model maintenance

Surrogate-based flowsheet model calibration is a critical extension of using flowsheet models in Digital Twin (DT) systems. However, maintaining accurate surrogates over time is increasingly challenging, especially when models are deployed in real-time or near-real-time environments, where continuous changes in the physical systems can lead to model drift. To address this challenge, this study introduces a novel online learning–inspired framework to support the continuous maintenance of surrogate-based model calibration. This methodology bridges the gap between offline surrogate development and adaptive model maintenance. By embedding the surrogate in an online learning loop, the framework enables continuous calibration while minimizing reliance on resource-intensive flowsheet simulations. When applied to an industrial flowsheet calibration case, the approach reduced the number of direct calibration steps by up to 94% while preserving global model accuracy. The proposed method offers a scalable, automated, and resilient solution for maintaining surrogate and flowsheet model performance in dynamic industrial environments. • Online learning reduce direct calibration runs by up to 94%. • Replay buffers prevent catastrophic forgetting in surrogates retraining. • The initial accuracy of the surrogate is less critical with online updates. • Realistic accuracy targets avoid overfitting and improve stability.