Nonlinear state estimation as tool for online monitoring and adaptive feed in high throughput cultivations

Robotic facilities that can perform advanced cultivations (e.g., fed-batch or continuous) in high throughput have drastically increased the speed and reliability of the bioprocess development pipeline. Still, developing reliable analytical technologies, that can cope with the throughput of the cultivation system, has proven to be very challenging. On the one hand, the analytical accuracy suffers from the low sampling volumes, and on the other hand, the number of samples that must be treated rapidly is very large. These issues have been a major limitation for the implementation of feedback control methods in miniaturized bioreactor systems, where observations of the process states are typically obtained after the experiment has finished. In this work, we implement a Sigma-Point Kalman Filter in a high throughput platform with 24 parallel experiments at the mL-scale to demonstrate its viability and added value in high throughput experiments. The filter exploits the information generated by the ammonia-based pH control to enable the continuous estimation of the biomass concentration, a critical state to monitor the specific rates of production and consumption in the process. The objective in the selected case study is to ensure that the selected specific substrate consumption rate is tightly controlled throughout the complete Escherichia coli cultivations for recombinant production of an antibody fragment.