Oxygen supply in aerobic bioprocesses is of crucial importance. For this reason, this paper presents the oxygen demand of different cells and summarizes experimental and numerical possibilities for the determination of oxygen transfer in bioreactors. The focus lies on the volumetric oxygen mass transfer coefficient (k_La) calculation using computational fluid dynamics and state‐of‐the‐art models for surface‐aerated and forced‐aerated bioreactors. In addition, experimental methods for the determination of thek_Lavalue and the gas bubble size distribution are presented.

Optimizing bioprocesses requires an in-depth understanding, from a bioengineering perspective, of the cultivation systems used. A bioengineering characterization is typically performedviaexperimental or numerical methods, which are particularly well-established for stirred bioreactors. For unstirred, non-rigid systems such as wave-mixed bioreactors, numerical methods prove to be problematic, as often only simplified geometries and motions can be assumed. In this work, a general approach for the numerical characterization of non-stirred cultivation systems is demonstrated using the CELL-tainer bioreactor with two degree of freedom motion as an example. In a first step, the motion is recordedviamotion capturing, and a 3D model of the culture bag geometry is generatedvia3D-scanning. Subsequently, the bioreactor is characterized with respect to mixing time, and oxygen transfer rate, as well as specific power input and temporal Kolmogorov length scale distribution. The results demonstrate that the CELL-tainer with two degrees of freedom outperforms classic wave-mixed bioreactors in terms of oxygen transport. In addition, it was shown that in the cell culture version of the CELL-tainer, the critical Kolmogorov length is not surpassed in any simulation.

Optimal oxygen supply is vitally important for the cultivation of aerobically growing cells, as it has a direct influence on cell growth and product formation. A process engineering parameter directly related to oxygen supply is the volumetric oxygen mass transfer coefficient $k_{L}a$. It is the influences on $k_{L}a$ and computing time of different interfacial force and population balance models in stirred bioreactors that have been evaluated in this study. For this investigation, the OpenFOAM 7 open-source toolbox was utilized. Firstly, the Euler–Euler model with a constant bubble diameter was applied to a 2$\mathrm{L}$ scale bioreactor to statistically examine the influence of different interfacial models on the $k_{L}a$ value. It was shown that the $k_L$ model and the constant bubble diameter have the greatest influence on the calculated $k_{L}a$ value. To eliminate the problem of a constant bubble diameter and to take effects such as bubble breakup and coalescence into account, the Euler–Euler model was coupled with population balance models (PBM). For this purpose, four coalescence and five bubble breakup models were examined. Ultimately, it was established that, for all of the models tested, coupling computational fluid dynamics (CFD) with PBM resulted in better agreement with the experimental data than using the Euler–Euler model. However, it should be noted that the higher accuracy of the PBM coupled models requires twice the computation time.

HEK293 is a widely used cell line in the fields of research and industry. It is assumed that these cells are sensitive to hydrodynamic stress. The aim of this research was to use particle image velocimetry validated computational fluid dynamics (CFD) to determine the hydrodynamic stress in both shake flasks, with and without baffles, and in stirred Minifors 2 bioreactors to evaluate its effect on the growth and aggregate size distribution of HEK293 suspension cells. The HEK FreeStyle^TM 293-F cell line was cultivated in batch mode at different specific power inputs (from 63 $\mathrm{W} {\mathrm{m}}^{-3}$ to 451 $\mathrm{W} {\mathrm{m}}^{-3}$), whereby $\approx 60 \mathrm{W} {\mathrm{m}}^{-3}$ corresponds to the upper limit, which is what has been typically described in published experiments. In addition to the specific growth rate and maximum viable cell density VCD${}_{\max }$, the cell size distribution over time and cluster size distribution were investigated. The VCD${}_{\max }$ of $(5.77\pm 0.02)·{10}^6$$\mathrm{cells}$$\mathrm{m}$${\mathrm{L}}^{-1}$ was reached at a specific power input of 233 $\mathrm{W} {\mathrm{m}}^{-3}$ and was $23.8\%$ higher than the value obtained at 63 $\mathrm{W} {\mathrm{m}}^{-3}$ and $7.2\%$ higher than the value obtained at 451 $\mathrm{W} {\mathrm{m}}^{-3}$. No significant change in the cell size distribution could be measured in the investigated range. It was shown that the cell cluster size distribution follows a strict geometric distribution whose free parameter p is linearly dependent on the mean Kolmogorov length scale. Based on the performed experiments, it has been shown that by using CFD-characterised bioreactors, the VCD${}_{\max }$ can be increased and the cell aggregate rate can be precisely controlled.

Scaling bioprocesses remains a major challenge. Since it is physically impossible to increase all process parameters equally, a suitable scale-up strategy must be selected for a successful bioprocess. One of the most widely used criteria when scaling up bioprocesses is the specific power input. However, this represents only an average value. This study aims to determine the Kolmogorov length scale distribution by means of computational fluid dynamics (CFD) and to use it as an alternative scale-up criterion for geometrically non-similar bioreactors for the first time. In order to obtain a comparable Kolmogorov length scale distribution, an automated geometry and process parameter optimization was carried out using the open-source tools OpenFOAM and DAKOTA. The Kolmogorov–Smirnov test statistic was used for optimization. A HEK293-F cell expansion (batch mode) from benchtop (Infors Minifors 2 with 4 L working volume) to pilot scale (D-DCU from Sartorius with 30 L working volume) was carried out. As a reference cultivation, the classical scale-up approach with constant specific power input (233 W m⁻³) was used, where a maximum viable cell density (VCD${}_{\max }$) of $5.02·{10}^6$ cells mL⁻¹ was achieved (VCD${}_{\max }$ at laboratory scale $5.77·{10}^6$ cells mL⁻¹). Through the automated optimization of the stirrer geometry (three parameters), position and speed, comparable cultivation results were achieved as in the small scale with a maximum VCD of $5.60·{10}^6$ cells mL⁻¹. In addition, even on the pilot scale, cell aggregate size distribution was seen to strictly follow a geometric distribution and can be predicted with the help of CFD with the previously published correlation.