Observer design and tuning for biomass growth and kLa using online and offline measurements

Measurement of the key process variables is essential during biopharmaceutical production. These measurements are often not available online. This work combines frequent online measurements (oxygen uptake rate) with infrequent offline measurements (biomass) to estimate the specific growth rate, biomass, and the oxygen transfer coefficient (kLa) online. The system consists of an extended Kalman filter and parameter adaptation for the time-varying kLa. Tuning is based on minimization of the error between the simulation and the estimation. Although the process itself is not stable, stability of the observer is evaluated heuristically by application of the Routh criterion. Performance and convergence of the observer are shown in both simulations and experiments in continuous and fed-batch cultivations of Bordetella pertussis.