Accelerated Process Optimization of Chromatographic Separations Using a Hybrid Modeling Approach

Chromatographic separation processes are essential for achieving high-purity products in industries such as pharmaceuticals and biotechnology, where complex mixtures such as monoclonal antibodies require precise purification. These processes, such as the twin-column Multicolumn Countercurrent Solvent Gradient Purification (MCSGP), are typically described by nonlinear partial differential and algebraic equations, leading to high computational demands that limit their feasibility for real-time optimization. In this work, we develop a hybrid modeling approach that combines artificial neural networks (ANNs) with process knowledge to capture the nonlinear dynamics of the twin-column MCSGP system efficiently. By retaining the mechanistic separation isotherm while eliminating the need for spatial discretization, the model reduces computational effort substantially, achieving cyclic steady state (CSS) predictions in a fraction of the time required by the respective high-fidelity model. The hybrid model is integrated within a Bayesian optimization (BO) framework to maximize process yield while meeting stringent product purity requirements. A comparative analysis with both data-driven and high-fidelity models demonstrates that the hybrid model provides a computationally efficient, accurate alternative suitable for real-time applications in continuous chromatography.