Multivariate Parameter Determination of Multi-Component Isotherms for Chromatography Digital Twins

Abstract

Many fundamental decisions in the process design of a separation task are conducted in an early stage where, unfortunately, process simulation does not have the highest priority. Subsequently, during the setup of the digital twin, dedicated experiments are carried out in the design space that was established earlier. These experiments are most often too complicated to conduct directly. This paper addresses the idea of a combined approach. The early-stage buffer screening and optimization experiments were planned with the Design of Experiments, carried out and then analyzed statistically to extract not only the best buffer composition but also the crucial model parameters, in this case the isotherm dependency on the buffer composition. This allowed the digital twin to predict the best buffer composition, and if the model-predicted control was applied to keep the process at the optimal productivity at a predetermined purity. The methodology was tested with an industrial peptide purification step.