  This paper presents a dynamic optimization numerical case study for
Monoclonal Antibody (mAb) production. The fermentation is conducted in a
continuous perfusion reactor. We represent the existing model in terms of a
general modeling methodology well-suited for simulation and optimization. The
model consists of six ordinary differential equations (ODEs) for the
non-constant volume and the five components in the reactor. We extend the model
with a glucose inhibition term to make the model feasible for optimization case
studies. We formulate an optimization problem in terms of an optimal control
problem (OCP) and consider four different setups for optimization. Compared to
the base case, the optimal operation of the perfusion reactor increases the mAb
yield with 44% when samples are taken from the reactor and with 52% without
sampling. Additionally, our results show that multiple optimal feeding
trajectories exist and that full glucose utilization can be forced without loss
of mAb formation.
