Abbreviations
AMBC
Advanced and model-based control
CHO
Chinese hamster ovary (mammalian cell)
DCU
Digital control unit
DLL
Dynamic link library
DO
Dissolved oxygen
DoE
Design of experiment
EtOH
Ethanol
GUI
Graphical user Interface
MPC
Model predictive control
NMPC
Nonlinear model predictive control
OLFO
Open-loop-feedback-optimal strategy
OTS
Operator training simulator
P
Product (Ethanol)
P
Proportional (P-controller)
P&ID
Piping and instrumentation diagram
PCS
Process control system
PI
Proportional integral (PI-controller)
PID
Proportional integral derivate (PID-controller)
RQ
Respiratory quotient
S
Substrate (Glucose)
SSF-BC Simultaneous saccharification, fermentation, and biocatalysis
STR
Stirred tank reactor
X
Dry biomass density (S. cerevisiae)
1 Introduction
The development of control strategies for bioprocesses poses huge challenges for
process engineers. The need for new tools that can help with this task, therefore, is
enormous. Optimisation of controllers during production runs is usually exceedingly
difficult or even impossible. Thus, bioprocess operation must be interrupted for
control optimisation. Interruptions of a production run, as well as inadequate control,
can lead to immense financial losses, which must be avoided. A promising approach
to this issue is the application of Digital Twins. The development or optimisation of
control strategies may be performed using this tool, thus leading to a shortened startup time for the newly developed or optimised bioprocess control scheme.
In the early 2000s, the Digital Twin concept was first applied in mechanical
engineering [1–3]. Digital Twins are often seen as virtual representations of physical
systems and can map the entire life cycle of the physical system [2]. Various authors
already published definitions of the term Digital Twin [1–5]. This chapter and
Chapter: Moser, Appl, Brüning, Hass “Mechanistic Mathematical Models as a
Digital Twins for Bioprocess Control Strategy Development and Realisation
65
AMBC
Advanced and model-based control
CHO
Chinese hamster ovary (mammalian cell)
DCU
Digital control unit
DLL
Dynamic link library
DO
Dissolved oxygen
DoE
Design of experiment
EtOH
Ethanol
GUI
Graphical user Interface
MPC
Model predictive control
NMPC
Nonlinear model predictive control
OLFO
Open-loop-feedback-optimal strategy
OTS
Operator training simulator
P
Product (Ethanol)
P
Proportional (P-controller)
P&ID
Piping and instrumentation diagram
PCS
Process control system
PI
Proportional integral (PI-controller)
PID
Proportional integral derivate (PID-controller)
RQ
Respiratory quotient
S
Substrate (Glucose)
SSF-BC Simultaneous saccharification, fermentation, and biocatalysis
STR
Stirred tank reactor
X
Dry biomass density (S. cerevisiae)
1 Introduction
The development of control strategies for bioprocesses poses huge challenges for
process engineers. The need for new tools that can help with this task, therefore, is
enormous. Optimisation of controllers during production runs is usually exceedingly
difficult or even impossible. Thus, bioprocess operation must be interrupted for
control optimisation. Interruptions of a production run, as well as inadequate control,
can lead to immense financial losses, which must be avoided. A promising approach
to this issue is the application of Digital Twins. The development or optimisation of
control strategies may be performed using this tool, thus leading to a shortened startup time for the newly developed or optimised bioprocess control scheme.
In the early 2000s, the Digital Twin concept was first applied in mechanical
engineering [1–3]. Digital Twins are often seen as virtual representations of physical
systems and can map the entire life cycle of the physical system [2]. Various authors
already published definitions of the term Digital Twin [1–5]. This chapter and
Chapter: Moser, Appl, Brüning, Hass “Mechanistic Mathematical Models as a
Digital Twins for Bioprocess Control Strategy Development and Realisation
65