Dynamic model-based control for process variations in ion-exchange chromatography

The capability of smart manufacturing to enable predictive and autonomous decision-making under uncertainty is highly valuable to ion-exchange chromatography (IEC), a critical purification step that is sensitive to variations in sample composition and loading density. However, conventional IEC operation relies on predefined elution conditions, including fixed elution gradient and collection window, which limits its adaptability to process variations. To address this limitation, a model-based control system was developed for the dynamic and autonomous optimization of IEC under process variations. The system incorporated the mechanistic models that combined equilibrium dispersive model and steric mass action model to quantitatively predict protein elution behavior. By integrating communication technologies, the model predictions were used to automatically determine and implement the optimal elution gradient and collection window, enabling model-based decision-making. The experimental validation demonstrated that the model-based control system consistently achieved purity above 96.0% and yield exceeding 88.0%. These results indicate that both product quality and process performance could be maintained despite process variability. Overall, the proposed model-based control system enabled the dynamic and simultaneous adjustment of elution gradient and collection window, transforming IEC operation from predefined conditions to a predictive and adaptive control approach, thereby enhancing process robustness and operational flexibility.