Automated Measurement and Monitoring of Bioprocesses: Key Elements of the M3C Strategy - Measurement, Monitoring, Modelling, and Control of Bioprocesses - page 13

of 10 3 s -1 . The online counts were found to be ''extremely accurate'' for dilute
suspensions (i.e., B10 7 cells ml -1 ) with respect to the offline gold standard;
however, accuracy was limited due to errors caused by the dilution, which has to
be increased with increasing cell density during the batch cultivation. Kinetics and
growth dynamics could be derived from these datasets with high resolution (ca.
every 15 min [ 39 ]). The FCM-determined cell number concentration has also been
used to control continuous cultivation, in a so-called cytostat, in which the cell
number concentration is the time-invariant variable in steady state [ 40 ].
3.2 Bio-Activity
As stated above, the biomass concentration is a variable of great importance. To be
exact, it is the mass of living and metabolically active cells that has to be targeted,
because this represents the amount of available biocatalyst in kinetic and balance
equations.
3.2.1 Calorimetry
Calorimetry is a relatively simple method to derive information about the activity
of a population, yet it is very seldom exploited. It has long been known that the
specific heat production rate is closely—and almost constantly—correlated with
the specific oxygen uptake rate; this is also known as the oxocaloric yield (Y Q/O2 ),
having a value of around 450 kJ mol -1 [ 41 , 42 ]. The signals necessary to calculate
the heat flux are usually available but not evaluated: in most bioreactors, the
temperature of the biosuspension is well known because a temperature controller is
employed; such controllers are often cascaded controllers which also require the
temperature of the coolant as an input. If only the flow rate of the coolant is known
(or at least kept constant) too, one can simply derive the heat flux from these data.
The larger the scale of the reactor, the less important are errors added due to heat
transfer to the ambient (i.e., the ''more adiabatic'' the reactor), but the technique is
also reported for bench-scale bioreactors, with sensitivity as low as 50 mW l -1
[ 43 ]. This provides a simple, heat-based biomass and specific growth rate esti-
mator for microbial cultures [ 44 ]. Of course, there are some systematic distur-
bances included in this result such as mechanical power dissipation to the liquid,
friction converted to heat in bearings and sealing, or the evaporation loss of heat
when dry air is used to aerate a fermenter. However, all these effects can be
quantified in abiotic control experiments and, later, accounted for in the real
fermentation processes (e.g., [ 45 , 46 ]). Since this technique balances the entire
reactor, one gets a direct estimate for the performance of a production lot.