Optimization of simulated moving bed and column chromatography for a plasmid DNA purification step and for a chiral separation

This work analyzes the performance of the SMB and the column chromatography processes for two different case studies: the first stage of the plasmid DNA (pDNA) polishing, and the Tr?ger's base enantiomer separation, in which the adsorption isotherms are linear and non-linear, respectively. Simulation tools are used together with an optimization routine (Non-Sorting Genetic Algorithm (NSGA)) in order to find the optimum operating conditions leading to maximum productivity and minimum solvent consumption; the optimum solution for each of the processes is a curve on the productivity-solvent consumption plane, the so-called Pareto set. The comparison between the column and the SMB processes is based on the relative position of the two Pareto sets calculated at equal conditions and for the same final purity and recovery of the target species. The results show that SMB is superior to column chromatography in the two case studies investigated, i.e. in the case of the linear isotherm (pDNA), the productivity gain is up to a factor two for a given value of the solvent consumption. Furthermore, the flexibility of the SMB operation is larger, since the Pareto sets are flatter and they prolong into regions of the productivity-solvent consumption plane that are not accessible with the column chromatography process.