3.3 Cultivation Mode
From an operational point of view, cultivation of microorganisms can be performed
in batch, fed-batch and continuous modes. In fed-batch cultivation modes, set point
control of the substrate concentration by manipulating the input flow rate is a matter
of particular economic and scientific interest. In order to have an efficient control
system, sufficient knowledge about the process state variables is required, which can
be achieved by the state estimation methods such as the Kalman filter or its
extensions. Therefore, previous studies have almost exclusively focused on the
application of state estimation methods for fed-batch cultivations (34 publications).
However, online monitoring and estimation of state variables in batch cultivations is
also crucial in order to monitor the state and if necessary may improve it to achieve
high productivity over the process. For instance, controlling the level of dissolved
oxygen (DO) in the fermentation broth, effects the rate of microbial metabolism.
Accordingly, Lee et al. [19] have implemented an EKF for noise filtering of
dissolved oxygen measurements which were used for controlling the DO levels in
batch cultivation of E. coli. This approach and, more generally, online monitoring
and state estimation of variables in batch cultivations remain briefly addressed in the
literature.
3.4 Bioprocess Phase
Mixing of medium and pre-cultures are performed during upstream processing phase
and separation and purification of the product from biomass is performed during the
downstream processing phase. In order to optimize cell growth and maximize the
product yield, online monitoring and a tight control is required during both phases.
The presented articles show there have been numerous studies to investigate the
application of state estimation methods during the cultivation phase (39 papers).
However, the articles indicate that only two authors had examined the application of
Kalman filtering methods for state and variable estimation in downstream
processing. For efficient and robust process development in the downstream
processing phase, knowledge of the location and concentration of the product and
key contaminants is also crucial. Holwill et al. [28] have used a low technology
detection system involving the measurement of rate of change of absorbance at a
single wavelength after addition of reagent to a representative sample stream. This
provided online data detailing the performance of a continuous precipitation process.
This information as well as a mathematical model which describes the fractional
protein perception were fed into a control algorithm which was programmed to
maintain predefined set points by feedback control through adjustments to the
overall feed saturation. The Kalman filter was used for estimating the parameters
of the model. Feidl et al. [59] developed a state estimation procedure for estimation
108
A. Yousefi-Darani et al.
From an operational point of view, cultivation of microorganisms can be performed
in batch, fed-batch and continuous modes. In fed-batch cultivation modes, set point
control of the substrate concentration by manipulating the input flow rate is a matter
of particular economic and scientific interest. In order to have an efficient control
system, sufficient knowledge about the process state variables is required, which can
be achieved by the state estimation methods such as the Kalman filter or its
extensions. Therefore, previous studies have almost exclusively focused on the
application of state estimation methods for fed-batch cultivations (34 publications).
However, online monitoring and estimation of state variables in batch cultivations is
also crucial in order to monitor the state and if necessary may improve it to achieve
high productivity over the process. For instance, controlling the level of dissolved
oxygen (DO) in the fermentation broth, effects the rate of microbial metabolism.
Accordingly, Lee et al. [19] have implemented an EKF for noise filtering of
dissolved oxygen measurements which were used for controlling the DO levels in
batch cultivation of E. coli. This approach and, more generally, online monitoring
and state estimation of variables in batch cultivations remain briefly addressed in the
literature.
3.4 Bioprocess Phase
Mixing of medium and pre-cultures are performed during upstream processing phase
and separation and purification of the product from biomass is performed during the
downstream processing phase. In order to optimize cell growth and maximize the
product yield, online monitoring and a tight control is required during both phases.
The presented articles show there have been numerous studies to investigate the
application of state estimation methods during the cultivation phase (39 papers).
However, the articles indicate that only two authors had examined the application of
Kalman filtering methods for state and variable estimation in downstream
processing. For efficient and robust process development in the downstream
processing phase, knowledge of the location and concentration of the product and
key contaminants is also crucial. Holwill et al. [28] have used a low technology
detection system involving the measurement of rate of change of absorbance at a
single wavelength after addition of reagent to a representative sample stream. This
provided online data detailing the performance of a continuous precipitation process.
This information as well as a mathematical model which describes the fractional
protein perception were fed into a control algorithm which was programmed to
maintain predefined set points by feedback control through adjustments to the
overall feed saturation. The Kalman filter was used for estimating the parameters
of the model. Feidl et al. [59] developed a state estimation procedure for estimation
108
A. Yousefi-Darani et al.