Surrogate-based flowsheet model maintenance for Digital Twins

Abstract

Digital Twins (DTs) are transforming industrial processes by providing virtual models that mirror physical systems, enabling real-time monitoring and optimization. A major challenge in DTs in process industry, is maintaining the accuracy of flowsheet simulation models due to changes like equipment degradation and operational shifts. This study proposes a novel surrogate-based approach for the automated calibration of these models, which reduces reliance on manual adjustments and adapts to changes in the physical system. This study leverages surrogate models and particle swarm optimization to incorporate modeling considerations and measurement uncertainties, thereby automating model calibration and reducing manual interventions. In a refinery case study, our approach reduced calibration time for the sour water stripper Hysys model by 80% while maintaining the desired accuracy. These results highlight the method’s potential to enhance flowsheet model accuracy in digital twin systems and to support more robust and adaptable DT applications.