Online state prediction of S. cerevisiae cultivation purely based on ethanol gas sensors and a state observer

The accurate estimation of the current state of a bioprocess is important for a proper process control scheme. As a lot of variables of interest require substantial effort and time to measure or sometimes are not measurable at all, a direct measurement is not always an option. Instead an indirect chemometric approach based on some other easier to measure variable such as spectroscopy is commonly used to estimate the state of a bioprocess.

In this contribution we present another much cheaper solution for S. cerevisiae cultivations where the only direct measurement were ethanol measurements in the headspace of the bioreactor based on metal oxide gas sensors. For the current state prediction, a process model and an extended Kalman filter was used. The basic idea is to apply the model to predict the process state, and then use the ethanol measurements to correct and change the model prediction online. The main advantage of this approach is, that metal oxide gas sensors are dead cheap and in contrast to spectroscopic approaches, no expensive calibration is required. The knowledge required is the process model and a rough estimation of the kinetic parameter values.