Model-based scaling of preparative chromatographic separation of sucrose and glucose using bilevel parameter...

Preparative chromatography for purification of sugars has found numerous applications in food processing and biochemical industries. Parameter estimation leading to robust modelling and model-based design are essential for transferring this technology into industrial practice. This study examines the Equilibrium Dispersive model with a non-linear isotherm and the formulation of apparent dispersion based on the Bodenstein number. A parameter estimation workflow is proposed, incorporating chromatography-specific algorithmic data preprocessing and a curve uncertainty scoring system, enabling the simultaneous utilization of data from pulse-feed experiments conducted under varying conditions. A bilevel optimization scheme is introduced, leading to increased performance and robustness. The introduction of parameter bounding and initialization eliminates arbitrariness in the process. Experiments on the chromatographic separation of a d-glucose and sucrose mixture, performed under different flow rates and feed loads using an anion-exchange resin, were conducted. Lab-scale experiments were used for parameter estimation, supported by subsequent identifiability and sensitivity analyses. Scaling-up predictions of the calibrated model were evaluated by experimental data from a 2-meter-long pilot-scale column. The results demonstrate the benefits of the proposed modeling and parameter estimation framework, as well as the sufficient predictive accuracy of the calibrated model under the conditions of scaled-up flow rates and column dimensions.