Computational fluid dynamics simulation of an industrial P. chrysogenum fermentation with a coupled 9-pool metabolic model: Towards rational scale-down and design optimization

Haringa, C.; Tang, W.; Wang, G.; Deshmukh, A.T.; van Winden, Wouter A.; Chu, Ju; van Gulik, W.M.; Heijnen, J.J.; Mudde, R.F.; Noorman, H.J.

doi:10.1016/j.ces.2017.09.020

Computational fluid dynamics simulation of an industrial P. chrysogenum fermentation with a coupled 9-pool metabolic model

Title

Computational fluid dynamics simulation of an industrial P. chrysogenum fermentation with a coupled 9-pool metabolic model: Towards rational scale-down and design optimization

Author

Haringa, C. (TU Delft ChemE/Transport Phenomena)
Tang, W. (TU Delft OLD BT/Cell Systems Engineering)
Wang, G. (TU Delft OLD BT/Cell Systems Engineering)
Deshmukh, A.T. (TU Delft OLD BT/Cell Systems Engineering)
van Winden, Wouter A. (DSM)
Chu, Ju (East China University of Technology)
van Gulik, W.M. (TU Delft OLD BT/Cell Systems Engineering)
Heijnen, J.J. (TU Delft OLD BT/Cell Systems Engineering)
Mudde, R.F. (TU Delft ChemE/Transport Phenomena)
Noorman, H.J. (TU Delft BT/Bioprocess Engineering)

Date

2018

Abstract

We assess the effect of substrate heterogeneity on the metabolic response of P. chrysogenum in industrial bioreactors via the coupling of a 9-pool metabolic model with Euler-Lagrange CFD simulations. In this work, we outline how this coupled hydrodynamic-metabolic modeling can be utilized in 5 steps. (1) A model response study with a fixed spatial extra-cellular glucose concentration gradient, which reveals a drop in penicillin production rate q_p of 18–50% for the simulated reactor, depending on model setup. (2) CFD-based scale-down design, where we design a 1-vessel scale down simulator based on the organism lifelines. (3) Scale-down verification, numerically comparing the model response in the proposed scale-down simulator with large-scale CFD response. (4) Reactor design optimization, reducing the drop in penicillin production by a change of feed location. (5) Long-term fed-batch simulation, where we verify model predictions against experimental data, and discuss population heterogeneity. Overall, these steps present a coupled hydrodynamic-metabolic approach towards bioreactor evaluation, scale-down and optimization.

Subject

CFD
Euler-Langrange
Industrial
Metabolic model
Scale-down

To reference this document use:

http://resolver.tudelft.nl/uuid:5155aa7a-2b76-4599-b4f5-8d6a3c859e9f

DOI

https://doi.org/10.1016/j.ces.2017.09.020

ISSN

0009-2509

Source

Chemical Engineering Science, 175, 12-24

Part of collection

Institutional Repository