estimation in downstream processing remain rather limited. The advancement in
state and parameter estimation methods in downstream processes leads to better
knowledge of the location and concentration of the product and key contaminants,
which are essential for process optimization and control.
So far most of the Kalman filter algorithms are implemented for monitoring
fed-batch cultivations; however more attention is required for real-time implementation of the Kalman filter algorithms for controlling the feed rate and substrate
production in these cultivations. Further efforts are also required towards implementation of state estimation methods in batch and continuous cultivations.
From the presented literature, it could be concluded that the non-linear extensions
of the Kalman filter are powerful tools for state estimation in bioprocesses; therefore
they could be used for digitalization of bioprocesses. Accordingly, in a case study, a
digital twin of the baker’s yeast batch fermentation process was developed by using
a dynamic non-linear model of the process as well as an EKF algorithm. The
proposed method gives the possibility to predict glucose, ethanol and biomass
concentrations simultaneously from the only available infrequent online measurements of ethanol concentration. The accuracy of the estimated biomass and substrate
production are in line with other studies which have also implemented an EKF
algorithm for monitoring the baker’s yeast cultivation [32, 49]. However, in our
application the maximal specific growth rates on glucose and ethanol are also
estimated. As a consequence, the rapid and precise estimation of these variables
could increase the overall knowledge integration in the digital twin of the process.
Overall, the unique advantage of online monitoring and in general digital twins of
bioprocesses is that they could play critical roles in bioprocess development such as
supporting problem solving in manufacturing, reducing effort in setting up a control
strategy and accelerating process performance by taking corrective actions automatically and in real time.
Appendix
Extended Kalman filter Matlab code: Online state prediction of batch yeast cultivations based on ethanol gas sensors.
%Initialization
clear; close all; clc;
sympref('AbbreviateOutput', false);
%Variable and parameter definition
%Symbols for symbolic math calculations
syms G E X P t
real
syms Y_gx Y_ge Y_ex mu1 mu2 K_M_G K_M_E
real
%Variables / Parameters
initX = [2.5; 6; 0.2; 0.15; 0.08];
% initial state (Biomass,
% Glucose, Ethanol)
The Kalman Filter for the Supervision of Cultivation Processes
119
state and parameter estimation methods in downstream processes leads to better
knowledge of the location and concentration of the product and key contaminants,
which are essential for process optimization and control.
So far most of the Kalman filter algorithms are implemented for monitoring
fed-batch cultivations; however more attention is required for real-time implementation of the Kalman filter algorithms for controlling the feed rate and substrate
production in these cultivations. Further efforts are also required towards implementation of state estimation methods in batch and continuous cultivations.
From the presented literature, it could be concluded that the non-linear extensions
of the Kalman filter are powerful tools for state estimation in bioprocesses; therefore
they could be used for digitalization of bioprocesses. Accordingly, in a case study, a
digital twin of the baker’s yeast batch fermentation process was developed by using
a dynamic non-linear model of the process as well as an EKF algorithm. The
proposed method gives the possibility to predict glucose, ethanol and biomass
concentrations simultaneously from the only available infrequent online measurements of ethanol concentration. The accuracy of the estimated biomass and substrate
production are in line with other studies which have also implemented an EKF
algorithm for monitoring the baker’s yeast cultivation [32, 49]. However, in our
application the maximal specific growth rates on glucose and ethanol are also
estimated. As a consequence, the rapid and precise estimation of these variables
could increase the overall knowledge integration in the digital twin of the process.
Overall, the unique advantage of online monitoring and in general digital twins of
bioprocesses is that they could play critical roles in bioprocess development such as
supporting problem solving in manufacturing, reducing effort in setting up a control
strategy and accelerating process performance by taking corrective actions automatically and in real time.
Appendix
Extended Kalman filter Matlab code: Online state prediction of batch yeast cultivations based on ethanol gas sensors.
%Initialization
clear; close all; clc;
sympref('AbbreviateOutput', false);
%Variable and parameter definition
%Symbols for symbolic math calculations
syms G E X P t
real
syms Y_gx Y_ge Y_ex mu1 mu2 K_M_G K_M_E
real
%Variables / Parameters
initX = [2.5; 6; 0.2; 0.15; 0.08];
% initial state (Biomass,
% Glucose, Ethanol)
The Kalman Filter for the Supervision of Cultivation Processes
119