Euler‐Lagrange computational fluid dynamics for (bio)reactor scale down: An analysis of organism lifelines

Abstract

The trajectories, referred to as lifelines, of individual microorganisms in an industrial scale fermentor under substrate limiting conditions were studied using an Euler‐Lagrange computational fluid dynamics approach. The metabolic response to substrate concentration variations along these lifelines provides deep insight in the dynamic environment inside a large‐scale fermentor, from the point of view of the microorganisms themselves. We present a novel methodology to evaluate this metabolic response, based on transitions between metabolic “regimes” that can provide a comprehensive statistical insight in the environmental fluctuations experienced by microorganisms inside an industrial bioreactor. These statistics provide the groundwork for the design of representative scale‐down simulators, mimicking substrate variations experimentally. To focus on the methodology we use an industrial fermentation of Penicillium chrysogenum in a simplified representation, dealing with only glucose gradients, single‐phase hydrodynamics, and assuming no limitation in oxygen supply, but reasonably capturing the relevant timescales. Nevertheless, the methodology provides useful insight in the relation between flow and component fluctuation timescales that are expected to hold in physically more thorough simulations. Microorganisms experience substrate fluctuations at timescales of seconds, in the order of magnitude of the global circulation time. Such rapid fluctuations should be replicated in truly industrially representative scale‐down simulators.

Comparative performance of different scale‐down simulators of substrate gradients in Penicillium chrysogenum cultures: the need of a biological systems response analysis

“…S4). The total average value in the 54 m 3 large‐scale fermentor is about 34.4 μM (Haringa et al ., ), which value is well in between the two compartments of the current TCR system (Fig. S4).…”

“…() observed a 50% decrease in penicillin productivity as well as a higher turnover rate of storage pools relative to continuously fed chemostat cultivation (de Jonge et al ., , ). However, as recently argued (Haringa et al ., ), in these previous scale‐down studies, typically fluctuation timescales of 100–500 s have been applied (Vardar and Lilly, ; Neubauer and Junne, ; de Jonge et al ., ; Heins et al ., ; Lemoine et al ., ), which were based on the 95% mixing time () at industrial scales (Limberg et al ., ). However, a more realistic approach is to base the frequency of the substrate oscillations on the 4–5 times lower circulation time (Haringa et al ., ), which implies that the cells in reality experience faster changes in substrate concentration at timescales of tens of seconds, which has been recently confirmed from computational fluid dynamics (CFD) simulations of the 54 m 3 penicillin fermentation case (Haringa et al ., ).…”

A 9‐pool metabolic structured kinetic model describing days to seconds dynamics of growth and product formation by Penicillium chrysogenum

“…The model will be integrated into a CFD framework for industrial bioreactor simulations, where the organisms are expected to experience substrate concentration variations of significant amplitude, with cycle time ranging from tens of seconds to several minutes depending on the reactor scale (Haringa et al, 2016(Haringa et al, , 2017. The model will be integrated into a CFD framework for industrial bioreactor simulations, where the organisms are expected to experience substrate concentration variations of significant amplitude, with cycle time ranging from tens of seconds to several minutes depending on the reactor scale (Haringa et al, 2016(Haringa et al, , 2017.…”

“…For genome/large-scale metabolic flux models, the step toward dynamic simulation poses a major challenge due to the limited mechanistic in vivo kinetic knowledge of each reaction. Still, the black box model has limitations: the cell's individual "experiences, " or "life-lines" are not taken into consideration (Haringa et al, 2016;Lapin et al, 2006Lapin et al, , 2010. Using a black box model, Larsson et al (1996) simulated the glucose concentration gradient in a 30 m 3 cultivation of Saccharomyces cerevisiae by integrating CFD and a hyperbolic glucose uptake model.…”

CFD predicted pH gradients in lactic acid bacteria cultivations

“…Moreover, biokinetic models are coupled with fluid dynamics to analyze environmental gradients during fermentations (Schmalzriedt, Jenne, Mauch, & Reuss, 2003;Wang et al, 2015). If biokinetic models are directly integrated into CFD, both the Euler-Euler approach (Bannari, Bannari, Vermette, & Proulx, 2012;Elqotbi, Vlaev, Montastruc, & Nikov, 2013) and the Euler-Lagrange approach combined with a population balance model (Haringa et al, 2016;Lapin, Müller, & Reuss, 2004;Lapin, Schmid, & Reuss, 2006;Morchain, Gabelle, & Cockx, 2013) are commonly applied. Compartment models, which are based on the knowledge about the fluid dynamics in the bioreactor obtained from CFD models, reduce the number of spatial elements and decrease the computational demand (Vrábel et al, 2001).…”