Euler–Lagrange approach to model heterogeneities in stirred tank bioreactors – Comparison to experimental flow characterization and particle tracking

Delafosse, Angélique; Calvo, Sébastien; Collignon, Marie-Laure; Delvigne, Frank; Crine, Michel; Toye, Dominique

doi:10.1016/j.ces.2015.05.045

Cited by 37 publications

(32 citation statements)

References 23 publications

(25 reference statements)

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“…Our analysis of microorganism lifelines draws heavily from the techniques used in circulation time determination , which are based on consecutive crossings of a region in space. Since microorganisms have no notion of their physical location, we use

q_{normals} - space

as a more relevant space for the determination of residence time/circulation time distributions regarding variations in q s .…”

Section: Resultsmentioning

confidence: 99%

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

Haringa

Tang

Deshmukh

et al. 2016

Engineering in Life Sciences

104

140

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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.

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q_{normals} - space

as a more relevant space for the determination of residence time/circulation time distributions regarding variations in q s .…”

Section: Resultsmentioning

confidence: 99%

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

Haringa

Tang

Deshmukh

et al. 2016

Engineering in Life Sciences

104

140

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“…Several approaches to radically speed up these computations are under development. CFD‐based compartment models (Delafosse et al, ; Delafosse et al, ) offer a balance between speed and accuracy but require steady flow fields. Recently developed recurrence‐CFD (Lichtenegger & Pirker, ; Pirker & Lichtenegger, ) reconstructs the dynamic evolution of the flow based on recurring patterns.…”

Section: Future Prospectmentioning

confidence: 99%

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

Wang

Haringa

Tang

et al. 2019

Biotech & Bioengineering

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Metabolomics aims to address what and how regulatory mechanisms are coordinated to achieve flux optimality, different metabolic objectives as well as appropriate adaptations to dynamic nutrient availability. Recent decades have witnessed that the integration of metabolomics and fluxomics within the goal of synthetic biology has arrived at generating the desired bioproducts with improved bioconversion efficiency. Absolute metabolite quantification by isotope dilution mass spectrometry represents a functional readout of cellular biochemistry and contributes to the establishment of metabolic (structured) models required in systems metabolic engineering. In industrial practices, population heterogeneity arising from fluctuating nutrient availability frequently leads to performance losses, that is reduced commercial metrics (titer, rate, and yield). Hence, the development of more stable producers and more predictable bioprocesses can benefit from a quantitative understanding of spatial and temporal cell-to-cell heterogeneity within industrial bioprocesses. Quantitative metabolomics analysis and metabolic modeling applied in computational fluid dynamics (CFD)-assisted scale-down simulators that mimic industrial heterogeneity such as fluctuations in nutrients, dissolved gases, and other stresses can procure informative clues for coping with issues during bioprocessing scale-up. In previous studies, only limited insights into the hydrodynamic conditions inside the industrial-scale bioreactor have been obtained, which makes case-by-case scale-up far from straightforward. Tracking the flow paths of cells circulating in large-scale bioreactors is a highly valuable tool for evaluating cellular performance in production tanks. The "lifelines" or "trajectories" of cells in industrial-scale bioreactors can be captured using Euler-Lagrange CFD simulation. This novel methodology can be further coupled with metabolic (structured) models to provide not only a statistical analysis of cell lifelines triggered by the environmental fluctuations but also a global assessment of the metabolic response to heterogeneity inside an industrial bioreactor. For the future, the industrial design should be dependent on the computational framework, and this integration work will allow bioprocess scale-up to the industrial scale with an end in mind. K E Y W O R D SCFD, Euler-Langrange, metabolic model, metabolomics, population heterogeneity, scale down

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“…In compartment models, as a combined approach between CFD models and axial dispersion models, model complexity is reduced on macro scale by describing the reactor in uniform compartments, which enables the implementation of complex kinetics on micro scale. This compartment modeling approach has been proved suitable in different applications 32–47. Among others, successful applications to bioreactors,32–34 bubble columns,35,36 and loop reactors37 have been reported by now.…”

Section: Introductionmentioning

confidence: 99%

“…This compartment modeling approach has been proved suitable in different applications 32–47. Among others, successful applications to bioreactors,32–34 bubble columns,35,36 and loop reactors37 have been reported by now. Additionally, compartment models have been used to simulate, for example, gas–liquid stirred tank reactors 38–41.…”

Section: Introductionmentioning

confidence: 99%

“…The determination of compartments in the reported applications is mostly performed based on either simulated velocity fields as for bioreactors, loop reactors, and bubble columns32,33,35–37 or energy dissipation rate, for example in stirred tank models 38–41,46,47. Others used (tracer) concentration and temperature gradients to determine compartments 34,42,44,45.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Simulation of Polymer Reactors Using the Compartment Modeling Approach

Cremer-Bujara

Biessey

Grünewald

2019

Macro Reaction Engineering

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A compartment modeling approach based on computational fluid dynamics (CFD) simulations is applied to a simplified static mixer geometry. Compartments are derived from velocity fields obtained from cold CFD simulations. This methodology is based on the definition of periodic flow zones (PFZ) derived from the recurrent flow profile within the static mixer. In general, PFZ can be characterized by two different compartments: flow zones with hydrodynamic behavior of a tubular reactor and dead zones exhibiting a more continuous stirred tank reactor‐like characteristic. In CFD studies the influence of changing fluid properties, for example viscosity, on flow profile due to polymerization progress is considered. In the deterministic compartment model, the continuous flow profile within the static mixer is transformed to basic reactor models interconnected via an exchange stream. To reduce model complexity and the number of model parameters, constant volumes of compartments are assumed. Changes in hydrodynamics are considered by a variable exchange flow rate as a function of Re manipulating residence time in compartments. Simulation studies show the influence of decreasing exchange flow rates with polymerization progress, as Re decreases, resulting in a greater increase of viscosity in dead zones. The reactor performance is qualitatively represented by the simulation results.

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Euler–Lagrange approach to model heterogeneities in stirred tank bioreactors – Comparison to experimental flow characterization and particle tracking

Cited by 37 publications

References 23 publications

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

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

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

Simulation of Polymer Reactors Using the Compartment Modeling Approach

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