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Euler-Lagrangian Simulations: A Proper Tool for Predicting Cellular Performance in Industrial Scale Bioreactors

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Digital Twins

Part of the book series: Advances in Biochemical Engineering/Biotechnology ((ABE,volume 177))

Abstract

Eulerian-Lagrangian approach to investigate cellular responses in a bioreactor has become the center of attention in recent years. It was introduced to biotechnological processes about two decades ago, but within the last few years, it proved itself as a powerful tool to address scale-up and -down topics of bioprocesses. It can capture the history of a cell and reveal invaluable information for, not only, bioprocess control and design but also strain engineering. This way it will be possible to shed light on the actual environment that cell experiences throughout its lifespan. Lifelines of a microorganism in a bioreactor can serve as the missing link that encompasses the biological timescales and the physical timescales. For this purpose digitalization of bioreactors provides us with new insights that are not achievable in industrial reactors easily if at all, namely, substrate and product gradients; high-shear regions are among the most interesting factors that can be reproduced adequately with help of a digital twin. In this chapter basic principles of this method will be introduced, and later on some practical aspects of particle tracking technique will be illustrated. In the final section, some of the advantages and challenges associated with this method will be discussed.

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Acknowledgments

This work was partially supported by the German Federal Ministry of Education and Research (BMBF), grant number: FKZ 031B0629.

Abbreviations and Nomenclatures

CFD:

Computational fluid dynamics

DNS:

Direct numerical simulation

DO:

Dissolved oxygen

EL:

Euler-Lagrange

LES:

Large eddy simulation

NSE:

Navier-Stokes equations

PBM:

Population balance model

PFR:

Plug flow reactor

RANS:

Reynolds-averaged Navier-Stokes

STR:

Stirred tank reactor

UDF:

User-defined function

a :

Bubble surface

C glucose :

Glucose concentration

\( {C}_{O_2} \) :

Dissolved oxygen concentration

\( {C}_{O_2}^{\ast } \) :

Equilibrium oxygen concentration

C x :

Biomass concentration

d p :

Bubble diameter

D :

Diffusion coefficient

k :

Turbulent kinetic energy

k l :

Mass transfer coefficient

K glucose :

Saturation constant for glucose

K oxygen :

Saturation constant for oxygen

N :

Agitation rate

P :

Bioreactor power input

q s :

Specific substrate uptake rate

q s, max :

Maximum specific substrate uptake rate

\( {q}_{O_2} \) :

Specific oxygen uptake rate

\( {q}_{O_2,\max } \) :

Maximum specific oxygen uptake rate

St :

Stokes number

V :

Bioreactor volume

\( {Y}_{\frac{x}{s}} \) :

Biomass yield

\( {Y}_{\frac{o}{s}} \) :

Oxygen yield

ε :

Turbulent kinetic energy dissipation rate

μ :

Growth rate

μ max :

Maximum growth rate

μ liquid :

Molecular viscosity

ν :

Kinematic viscosity

ρ p :

Bubble density

τ mix :

Bioreactor mixing time

τ fluid :

Fluid timescale

τ p :

Bubble timescale

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Authors and Affiliations

  1. Institute of Biochemical Engineering, University of Stuttgart, Stuttgart, Germany

    Christopher Sarkizi Shams Hajian, Julia Zieringer & Ralf Takors

Authors
  1. Christopher Sarkizi Shams Hajian
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  2. Julia Zieringer
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  3. Ralf Takors
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Corresponding author

Correspondence to Ralf Takors .

Editor information

Editors and Affiliations

  1. Institute of Chemical, Environmental and Bioscience Engineering, Vienna University of Technology, Wien, Austria

    Christoph Herwig

  2. Institute of Bioprocess and Biosystems Engineering, Hamburg University of Technology, Hamburg, Germany

    Ralf Pörtner

  3. Institute of Bioprocess and Biosystems Engineering, Hamburg University of Technology, Hamburg, Germany

    Johannes Möller

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Hajian, C.S.S., Zieringer, J., Takors, R. (2020). Euler-Lagrangian Simulations: A Proper Tool for Predicting Cellular Performance in Industrial Scale Bioreactors. In: Herwig, C., Pörtner, R., Möller, J. (eds) Digital Twins. Advances in Biochemical Engineering/Biotechnology, vol 177. Springer, Cham. https://doi.org/10.1007/10_2020_133

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