Online Learning for Surrogate Model Maintenance

Abstract

In the process industry, flowsheet models are commonly used to create digital representations of real processes. While these models provide detailed simulations, they often struggle with computational demands in dynamic operating environments. Surrogate models offer a more efficient alternative, but their level of accuracy must be continuously aligned with actual system behavior. This paper presents an MLOps-aligned online surrogate calibration method that maintains and improves surrogate model accuracy by dynamically incorporating localized operational data. Unlike traditional global surrogates trained on static datasets, the proposed approach adapts to changing conditions while preserving previously learned knowledge, effectively addressing the challenge of catastrophic forgetting. Demonstrated on a heat exchanger network, the method significantly improves prediction accuracy in previously unmodeled operating regimes, enhancing the robustness and reliability of digital twins in industrial applications.