The cell utilized partitioning model as a predictive tool for optimizing counter-current chromatography processes

Abstract

Counter-current chromatography (CCC) is capable of unique elution modes that isolate analytes using the movement of the stationary phase in addition to moving the mobile phase. These modes include elution-extrusion CCC (EECCC) and dual-mode CCC (DM CCC) that are not possible in traditional solid-liquid chromatography systems. Although EECCC and DM CCC are widely used to recover highly retained components, to our knowledge, optimizing the elution process in these modes with predictive models has not been reported. To address this gap, we developed a predictive model for CCC dubbed the Cell Utilized Partitioning (CUP) model. The CUP model accurately predicts the effluents of multicomponent separations in EECCC and DM CCC modes when compared to experimental data. Furthermore, CUP model simulations were extended to investigate the influence of operating and intrinsic parameters on the yield and productivity, and to compare the separation performances of EECCC and DM CCC in various conditions. The results demonstrate that low distribution constants, usually a KD less than 1, and a selectivity > 1.3, under specific flowrate ranges, increase both productivity and yield. From these results, generalized optimization and scaleup guidelines are proposed that can apply to research settings and to industrial processes to maximize preparative CCC performance.