Open AccessParameter and state estimation of backers yeast cultivation with a gas sensor array and unscented Kalman filter
Author(s) -
YousefiDarani Abdolrahimahim,
PaquetDurand Olivier,
Hinrichs Jörg,
Hitzmann Bernd
Publication year - 2021
Publication title -
engineering in life sciences
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.547
H-Index - 57
eISSN - 1618-2863
pISSN - 1618-0240
DOI - 10.1002/elsc.202000058
Subject(s) - kalman filter , extended kalman filter , bioprocess , state vector , biomass (ecology) , control theory (sociology) , estimation theory , computer science , engineering , mathematics , statistics , algorithm , ecology , control (management) , artificial intelligence , biology , chemical engineering , physics , classical mechanics
Abstract 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.