508
Chapter 11
Figure 11.15. Comparison of predicted oxygen transfer rates for different models as a function of U*. The
graph shows predictions for an unstructured model (i.e. a stirred tank) based on Eq. 2 (non-coalescing -
top dotted curve) and Eq. 1
(coalescing liquid - bottom dotted curve) as well as the previously described
2 compartment model (non-coalescing case top curve and coalescing case bottom solid curve).
Experimentally determined values of
qd
for
h/d,
in the range 1.75 to 1.35 were between the thick broken
lines.
This example shows that a better accuracy of predicted oxygen transfer rates may be obtained using a
physically motivated compartment model of low complexity in combination with standard correlations for
k:a.
11.4. Metabolic Processes Affected by Scale-up
We have so far only discussed the physical processes that are affected by scale-up. The next
question is: How will these changes affect the microbial kinetics (or physiology) in the large-
scale process? Our prime concern is the concentration gradients that may occur due to poor
mixing in large-scale reactors, but also effects by shear stress may need to be considered. In
large-scale aerated reactors there will almost certainly be gradients present, both with respect to
oxygen, and, in case of fed-batch or continuous operation, most likely also with respect to the
limiting substrate. In the presence of gradients, the overall average volumetric reaction rate
vector, qav, is therefore an integral property according to Eq. (11.29).
*
V
Thus, the entire concentration field need to be known, as well as the kinetic expression giving the
direct concentration effects on volumetric rates, as discussed in chapter 7. Major changes of the
overall metabolism may occur as a function of substrate or oxygen concentration, e.g. in
organisms exhibiting overflow metabolism or anaerobic metabolism. This is true for both the
industrially important organisms
S. cerevisiae
and
E. coli
(see Fig 2.6 and Example 7.3). Ethanol
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