Applying multiple approaches to deepen understanding of mixing and mass transfer in large-scale aerobic fermentations

Abstract

Different methods are used at Corteva® Agriscience to improve our understanding of mixing in large-scale mechanically agitated fermentors. These include (a) use of classical empirical correlations, (b) use of small-scale models, and (c) computational fluid dynamics (CFD). Each of these approaches has its own inherent strengths and limitations. Classic empirical or semi-empirical correlations can provide insights into mass transfer, blending, shear, and other important factors but are dependent on the geometry and condition used to develop the correlations. Laboratory-scale modelling can be very useful to study mixing and model the effect of heterogeneity on the culture, but success is highly dependent on the methodology applied. CFD provides an effective means to accelerate the exploration of alternative design strategies through physics-based computer simulations that may not be adequately described by existing knowledge or correlations. However, considerable time and effort is needed to build and validate these models. In this paper, we review the various approaches used at Corteva Agriscience to deepen our understanding of mixing in large-scale fermentation processes.

CFD‐based bioreactor model with proportional–integral–derivative controller functionality for dissolved oxygen and pH

“…GPU‐native algorithms, such as the lattice–Boltzmann (LB) method for modeling fluid flow and bounding volume hierarchy (BVH) methods for tracking bubble collisions, present a multiple order‐of‐magnitude runtime improvement over CPU‐based approaches (Hanspal et al, 2022; Haringa, 2022). As such, rather than calculating a snapshot of the flow field, LB‐based models on GPUs can be used to practically simulate hours of multiphase process mechanics (Hanspal et al, 2020). Beyond directional insights, quantitative and time‐accurate predictions related to the cell culture properties and their coupling to the controller response can be simulated directly.…”

Verification of Dead Zones Generated in Bioreactors as a Proactive Stage in Bioreactor Design

“…It is receiving wide attention for their many advantages over conventional reactors. With existing of the inner draft tube in the airlift bioreactor, the gas bubbling system causes a medium to up-flow with suspended nutrients and living microorganisms [Hanspal et al, 2020; Salazar-Magallon and Huerta, 2020; Al-Mashhadani, 2017].…”

An analysis of organism lifelines in an industrial bioreactor using Lattice‐Boltzmann CFD

“…Furthermore, some relevant physical phenomena were lacking until recently, and the need for computing clusters may have been prohibitive. In recent years, this has been changing; a more extensive range of physical phenomena has been included in LB, including reaction, particle/bubble flow [ 28 , 29 , 30 ], rheology [ 31 ], and mass transfer [ 32 ]. In addition, GPU‐based LB [ 33 ] has brought hardware requirements within reach for a wider range of users, while open‐source and commercial codes have simplified application [ 24 , 32 ].…”