Modelling Approaches for Bio-Manufacturing Operations - Measurement, Monitoring, Modelling, and Control of Bioprocesses - page 87

thus be made earlier and with due attention paid to aspects such as facility or
corporate constraints that may affect the choice of process strategy.
1.2 Advantages in Developing Bioprocess Models
In respect of some of the challenges outlined above, modelling can be a very useful
way to combat the resulting cost and time pressures for a wide variety of scenarios
that require process- or strategic-related decision making (Table 1 ). Mathematical
simulations are a potentially useful method for designing and developing bio-
processes [ 48 ], reducing the costs and times required to conduct process devel-
opment and manufacturing activities while maximising productivity [ 21 , 22 , 29 ,
39 ]. Simulations can be used to identify input parameters or parts of a process
which have an especially pronounced effect upon technical or cost performance,
hence giving an estimation of process robustness [ 29 ]. Models can help to compare
different flowsheet choices in a virtual setting [ 25 ], enabling selection of robust
manufacturing protocols and optimising plant capacity utilisation in order to
maximise throughput at minimised production costs. Manufacturing routes can
also be evaluated in terms of likely capital expenditure when deciding whether to
commit resources to a project or not [ 8 , 25 ]. If implemented across process
development groups and other functions, simulations can provide a common
language to facilitate communication between different groups such as fermenta-
tion, primary recovery and purification [ 26 , 31 ]. Models can be technical in nature
for determining material balances for individual unit operations or whole pro-
cesses, or as is increasingly the case, they can also address business concerns. This
allows an engineer to answer both technical and financial questions simultaneously
[ 9 ], enabling a more holistic optimal process synthesis to be completed more
rapidly than if done by experimentation alone. Models help to provide focus to
experimental studies, thus reducing the total amount of time spent in the laboratory
or pilot plant [ 32 ]. Models can also allow the investigation of an experimental
design in order to shed light on the validity of model assumptions [ 19 ]. Another
area in which models are of use is that of determining resource utilisation. Sim-
ulations provide a useful way to synthesise many manufacturing activities into a
single portal from which it becomes possible to see where resources are in high
demand and where bottlenecks may exist [ 32 , 46 ]. The availability of resources
such as labour, equipment and ancillary supplies is critical in allowing a plant to
run smoothly. At times of high demand, simulations can indicate where a process
is at greatest risk if resources become unavailable e.g. due to the maintenance or
emergency shutdown of equipment or because operators are unavailable.
Evaluating process changes is another task to which models are well suited, and
they can be applied for quantifying the impacts to determine whether the addi-
tional expenditure is worth the effort and to determine whether a process or facility
can accommodate changes; For example, if titre improvements are made in a
fermentation stage, then models can be used to check whether the downstream