Real-time monitoring of gradient chromatography using dual Kalman-filters

Real-time state estimation in chromatography is a useful tool to improve monitoring of biopharmaceutical downstream processes, combining mechanistic model predictions with real-time data acquisition to obtain an estimation that surpasses that of either approach individually. One common technique for real-time state estimation is Kalman filtering. However, non-linear adsorption isotherms pose a significant challenge to Kalman filters, which are dependent on fast algorithm execution to function. In this work, we apply Kalman filtering of non-constant elution conditions using a non-linear adsorption isotherm using a novel approach where dual Kalman filters are used to estimate the states of the adsorption modifier, salt, and the components to be separated. We performed offline tuning of the Kalman filters on real chromatogram data from a linear gradient, ionexchange separation of two proteins. The tuning was then validated by running the Kalman filters in parallel with a chromatographic separation in real time. The resulting, tuned, dual Kalman filters improved the L2 norm by 53 % over the open-loop model prediction, when compared to the true elution profiles. The Kalman filters were also applicable in real-time with a signal sampling frequency of 5 s, enabling accurate and robust estimation and paving the way for future applications beyond monitoring, such as real-time optimal pooling control.