Model-based identification of optimal operating conditions for amino acid simulated moving bed enantioseparation using a macrocyclic glycopeptide stationary phase

Teicoplanin aglycone columns allow efficient separation of amino acid enantiomers in aqueous mobile phases and enable robust and predictable simulated moving bed (SMB) separation of racemic methionine despite a dependency of the adsorption behavior on the column history (memory effect). In this work we systematically investigated the influence of the mobile phase (methanol content) and temperature on SMB performance using a model-based optimization approach that accounts for methionine solubility, adsorption behavior and back pressure. Adsorption isotherms became more favorable with increasing methanol content but methionine solubility was decreased and back pressure increased. Numerical optimization suggested a moderate methanol content (25–35%) for most efficient operation. Higher temperature had a positive effect on specific productivity and desorbent requirement due to higher methionine solubility, lower back pressure and virtually invariant selectivity at high loadings of racemic methionine. However, process robustness (defined as a difference in flow rate ratios) decreased strongly with increasing temperature to the extent that any significant increase in temperature over 32 °C will likely result in operating points that cannot be realized technically even with the lab-scale piston pump SMB system employed in this study.