From industrial fermentor to CFD-guided downscaling: what have we learned?

Abstract

• A comprehensive overview of the Euler-Lagrange bioreactor simulation approach. • Application of Euler-Lagrange CFD to three different case studies. • Different strategies to design scale-down experiments using CFD data are discussed. • Approach selection chart based on hydrodynamic characteristics of modeled reactor. • The potential of combining Euler-Lagrange CFD with microfluidics is discussed.

Coupled metabolic‐hydrodynamic modeling enabling rational scale‐up of industrial bioprocesses

“…Among all the scopes of kinetic modeling, simulation and performance prediction on the complete fermentation process are garnering more and more traction (H. Noorman, ). A study hotspot in this area is the prediction of cell performance in a large‐scale industrial fermentation which is featured with environmental heterogeneities (Haringa et al, ; H. Noorman, ; H. J. Noorman & Heijnen, ; Straathof et al, ). On one side, these industrial fermentation features, however, are extremely difficult to be captured due to the fact of process complexity and difficulty in data acquisition.…”

“…On the other side, it will be highly valuable for such an industrial process if a proper model can describe such a scenario and provide explicit and quantitative suggestions on the strain and process improvement. The integration of microbial kinetics and fluid dynamics offered great potential in describing details in a hundred‐ton fermentor (Delvigne et al, ; Haringa et al, ). For instance, Lapin, Muller, and Reuss () and Lapin, Schmid, and Reuss ()) first successfully combined the CFD framework with an E. coli kinetic model.…”

“…These lifelines collected from such simulations can be analyzed towards the design of laboratory scale‐down simulators which are representative of the large‐scale environment (Figure b); these scale‐down simulators can be used for experimental assessment of the cellular response. Three different methods for lifeline analysis were developed by Haringa et al (). In regime analysis, the lifeline gets discretized into a limited number of regimes, each with a certain characteristic, such as substrate starvation and excess, overflow/byproduct formation, and so forth (Haringa et al, ; Kuschel et al, ; Siebler et al, ).…”

“…The frequency at which cells switches between regimes and the duration of exposure to each regime are quantified. The other methods, arc analysis, and Fourier analysis aimed at quantifying the duration and magnitude‐distribution of fluctuations in the lifeline (Haringa et al, , ). The preferred method depends on both the flow pattern and the nature of the organism's response, but also on the way of downscaling.…”

“…However, it should be noted that a prerequisite for this kind of representative downscaling experiment is the knowledge of how conditions vary in large‐scale reactors. Towards this goal, computational fluid dynamics (CFD) is a powerful tool to allow a complete view of the concentration fields and flow patterns in industrial‐scale reactors and has been utilized to guide rational downscaling designs (Haringa, Mudde, & Noorman, ). In these representative scale‐down simulators, the environments undergone by the cells at large‐scale can be mimicked and biological insights can thereafter be extracted from omics datasets and process modeling (Joyce & Palsson, ).…”