Binary separation control in preparative gradient chromatography using iterative learning control

Abstract

Purification of biopharmaceuticals has shifted toward continuous and integrated processes, in turn bringing along a need for monitoring and control to maintain a desired separation between the target pharmaceutical and any impurities it may carry. In this study, a cycle-to-cycle control of the retention volumes of two compounds in a chromatographic, ion exchange purification step was developed, allowing the process to maintain the desired retention volumes in the separation. The controller made use of a model-based, multivariate iterative learning control (ILC) algorithm that used a quadratic-criterion objective function for optimal set point control, along with feed-forward control based on direct model inversion for preemptive control of set point changes. The model was calibrated using 3 experiments, allowing for fast setup. The controller was tested by introducing three different disturbances to a sequence of otherwise identical ion exchange separation processes: a change in the salt concentration of the elution buffer, a change in set point, and a change in the pH of the elution buffer. It was capable of correcting for all disturbances within at most 3 cycles, proving its efficacy. The successful application of ILC for separation control in biopharmaceutical purification paves the way for the development of further ILC-based control strategies within the field, as well as combination with other control strategies.