Cover Feature: Parameter and state estimation of backers yeast cultivation with a gas sensor array and unscented Kalman filter.

DOI: 10.1002/elsc.202000058 Successful operation, control and optimization of biotechnological process depend on reliable real-time available measurements of the process variables. Although some hardware sensors are readily available, they often have several drawbacks: cost, sample destruction, discrete-time measurements, processing delay, sterilization, disturbances in the hydrodynamic conditions inside the bioreactor, etc. It is therefore of interest to use software sensors [29, 30]. The central idea behind a soft sensor is to use easily accessible on-line data for the estimation of other process variables that are either difficult to measure or only measured at low frequency [30]. The figure illustrates a software sensor for on-line monitoring of substrate and biomass production in backers yeast cultivation. For details see article DOI 10.1002/elsc.202000058 on page 169.

Parameter and state estimation of backers yeast cultivation with a gas sensor array and unscented Kalman filter.

Real-time information about the concentrations of substrates and biomass is the key to accurate monitoring and control of bioprocess. However, on-line measurement of these variables is a challenging task and new measurement systems are still required. An alternative are software sensors, which can be used for state and parameter estimation in bioprocesses. The software sensors predict the state of the process by using mathematical models as well as data from measured variables. The Kalman filter is a type of such sensors. In this paper, we have used the Unscented Kalman Filter (UKF) which is a nonlinear extension of the Kalman filter for on-line estimation of biomass, glucose and ethanol concentration as well as for estimating the growth rate parameters in S. cerevisiae batch cultivation, based on infrequent ethanol measurements. The UKF algorithm was validated on three different cultivations with variability of the substrate concentrations and the estimated values were compared to the off-line values. The results obtained showed that the UKF algorithm provides satisfactory results with respect to estimation of concentrations of substrates and biomass as well as the growth rate parameters during the batch cultivation.