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.
Cite
@article{arxiv.2302.09932,
title = {Dynamic Optimization for Monoclonal Antibody Production},
author = {Morten Wahlgreen Kaysfeld and Deepak Kumar and Marcus Krogh Nielsen and John Bagterp Jørgensen},
journal= {arXiv preprint arXiv:2302.09932},
year = {2023}
}
Comments
6 pages, 5 figures, 4 tables. Accepted for IFAC World Congress 2023