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

Euler-Lagrange CFD simulations, where the biotic phase is represented by computational particles (parcels), provide information on environmental gradients inside bioreactors from the microbial perspective. Such information is highly relevant for reactor scale-down and process optimization. One of the major challenges is the computational intensity of CFD simulations, especially when resolution of dynamics in the flowfield is required. Lattice-Boltzmann large-eddy simulations (LB-LES) form a very promising approach for simulating accurate, dynamic flowfields in stirred reactors, at strongly reduced computation times compared to finite volume approaches. In this work, the performance of LB-LES in resolving substrate gradients in large-scale bioreactors is explored, combined with the inclusion of a Lagrangian biotic phase to provide the microbial perspective. In addition, the hydrodynamic performance of the simulations is confirmed by verification of hydrodynamic characteristics (radial velocity, turbulent kinetic energy, energy dissipation) in the impeller discharge stream of a 29 cm diameter stirred tank. The results are compared with prior finite volume simulation results, both in terms of hydrodynamic and biokinetic observations, and time requirements.BT/Bioprocess Engineerin

Through the Organism’s eyes: The interaction between hydrodynamics and metabolic dynamics in industrial-scale fermentation processes

The broth in industrial scale fermentors may contain significant gradients in, for example, substrate concentration, dissolved oxygen and shear rates. From the perspective of microbes in this fermentor, these gradients translate to temporal variations in their environment that may affect their metabolic response. As a result, there may be differences in process yield between laboratory scale fermentations and their industrial counterpart. Rather than scaling-up bioprocesses based on equivalence, it is recommended to scale-down: mimic the large-scale environment in lab scale setups, to account for hydrodynamic-metabolic interaction from the start. In this thesis, the use of Euler-Lagrange computational fluid dynamics to capture the large-scale fermentation environment is explored. Lagrangian simulations offer to study processes from the microbial perspective (so-called “lifelines”), and enable coupling of metabolic models describing the response to external variations. With this, it is possible to take the history of the trajectory of the microbe into account, as organisms may not adapt to their surroundings instantaneously. Guidelines for the setup of fermentor simulations are presented, and several means for processing the lifelines are discussed. The obtained information is used to design lab-scale fermentations that mimic large-scale conditions. It is furthermore shown how coupled hydrodynamic-metabolic simulations can be used to predict yield-loss, assess process improvements, and study the onset of population heterogeneity in large-scale fermentors. Additionally, a more fundamental towards the role of the turbulent Schmidt number in multi-impeller mixing is included.ChemE/Transport Phenomen

Microbial lifelines in bioprocesses: From concept to application

Bioprocesses are scaled up for the production of large product quantities. With larger fermenter volumes, mixing becomes increasingly inefficient and environmental gradients get more prominent than in smaller scales. Environmental gradients have an impact on the microorganism's metabolism, which makes the prediction of large-scale performance difficult and can lead to scale-up failure. A promising approach for improved understanding and estimation of dynamics of microbial populations in large-scale bioprocesses is the analysis of microbial lifelines. The lifeline of a microbe in a bioprocess is the experience of environmental gradients from a cell's perspective, which can be described as a time series of position, environment and intracellular condition. Currently, lifelines are predominantly determined using models with computational fluid dynamics, but new technical developments in flow-following sensor particles and microfluidic single-cell cultivation open the door to a more interdisciplinary concept. We critically review the current concepts and challenges in lifeline determination and application of lifeline analysis, as well as strategies for the integration of these techniques into bioprocess development. Lifelines can contribute to a successful scale-up by guiding scale-down experiments and identifying strain engineering targets or bioreactor optimisations.Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.BT/Bioprocess Engineerin

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

Euler–Lagrange computational fluid dynamics simulations offer great potential for the integration of transport dynamics and metabolic dynamics in fermentation systems. Since the seminal work of Lapin et al. [1,2], progress has been made, mainly in the analysis of CFD data and translation to laboratory setup designs. Different large-scale processes require different analysis methods; in this paper we discuss which analysis methods are best suited for given reactor types, by reviewing prior simulation cases as well as introducing new test cases. Furthermore, we address challenges in the translation from Euler–Lagrange simulations to laboratory scale systems, and propose methods to work around these shortcomings. Based on the current state of the art, we propose guidelines for the selection of data analysis methods, and we discuss the design of rational scale-down simulators. We conclude with a brief discussion regarding the requirements and possibilities of next-generation scale-down simulators, such as microfluidic single-cell analysis, and possible ways to approximate cellular lifelines from invasive intra-cellular measurements.Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.ChemE/Transport PhenomenaImPhys/Imaging PhysicsExecutive boardBT/Bioprocess Engineerin

Inter-compartment interaction in multi-impeller mixing: Part I. Experiments and multiple reference frame CFD

CFD simulations of mixing in single-phase multi-Rushton stirred tanks based on the RANS methodology frequently show an over-prediction of the mixing time. This hints at an under-prediction of the mass exchange between the compartments formed around the individual impellers. Some studies recommend tuning the turbulent Schmidt number to address this issue, but this appears to be an ad-hoc correction rather than physical adjustment, thereby compromising the predictive value of the method. In this work, we study the flow profile in between two Rushton impellers in stirred tank. The data hints at the presence of macro-instabilities, and a peak in turbulent kinetic energy in the region of convergent flow, which both may promote inter-compartment mass exchange. CFD studies using the steady-state multiple reference frame model (unsteady simulations are treated in part II) inherently fail to include the macro-instability, and underestimate the turbulent kinetic energy, thereby strongly over-estimating mixing time. Furthermore, the results are highly mesh-sensitive, with increasing mesh density leading to a poorer prediction of the mixing time. Despite proper results for 1-impeller studies, we do not deem MRF-RANS models suitable for mixing studies in multi-impeller geometries.Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.ChemE/Transport Phenomen

Lagrangian modeling of hydrodynamic–kinetic interactions in (bio)chemical reactors: Practical implementation and setup guidelines

Large substrate concentration gradients can exist in chemical or biochemical reactions, resulting from a large circulation time compared to the turnover time of substrates. The influence of such gradients on the microbial metabolism can significantly compromise optimal bioreactor performance. Lapin et al. (2004) proposed an Euler–Lagrange CFD method to study the impact of such gradients from the microbial point of view. The discrete representation of the biomass phase yields an advantageous perspective for studying the impact of extra-cellular variations on the metabolism, but at significant computational cost. In particular, the tracked number of particles, as well as the applied time resolution, have a large impact on both the accuracy of the simulation and the runtime of the simulation. In this work we study the influence of these parameters on both the simulation results and computation time, and provide guidelines for accurate Euler–Lagrange bioreactor simulations at minimal computational cost.ChemE/Transport PhenomenaBT/Bioprocess Engineerin

Stochastic parcel tracking in an Euler–Lagrange compartment model for fast simulation of fermentation processes

The compartment model (CM) is a well-known approach for computationally affordable, spatially resolved hydrodynamic modeling of unit operations. Recent implementations use flow profiles based on Computational Fluid Dynamics (CFD) simulations, and several authors included microbial kinetics to simulate gradients in bioreactors. However, these studies relied on black-box kinetics that do not account for intracellular changes and cell population dynamics in response to heterogeneous environments. In this paper, we report the implementation of a Lagrangian reaction model, where the microbial phase is tracked as a set of biomass-parcels, each linked with an intracellular composition vector and a structured reaction model describing their intracellular response to extracellular variations. A stochastic parcel tracking approach is adopted, in contrast to the resolved trajectories used in CFD implementations. A penicillin production process is used as a case study. We show good performance of the model compared with full CFD simulations, both regarding the extracellular gradients and intracellular pool response, using the mixing time as a matching criterion and taking into account that the mixing time is sensitive to the number of compartments. The sensitivity of the model output towards some of the inputs is explored. The coarsest representative CM requires a few minutes to solve 80 h of flow time, compared with approximately 2 weeks for a full Euler–Lagrange CFD simulation of the same case. This alleviates one of the major bottlenecks for the application of such CFD simulations towards the analysis and optimization of industrial fermentation processes.BT/Bioprocess Engineerin

Inter-compartment interaction in multi-impeller mixing: Part II. Experiments, sliding mesh and large Eddy simulations

Steady state multiple reference frame-RANS (MRF-RANS) simulations frequently show strong over-predictions of the mixing time in single-phase, multi-impeller mixing tanks, which is sometimes patched by ad hoc tuning of the turbulent Schmidt-number. In Part I of this work, we experimentally revealed the presence of macro-instabilities in the region between the impellers, as well as a peak in the turbulent kinetic energy in the region where the flow from the individual impellers converges. The MRF-RANS method was found unable to capture both. In this second paper, we show that the sliding-mesh RANS (SM-RANS) approach does capture the effect of macro-instabilities, while still underestimating the turbulent kinetic energy. Consequently, the SM-RANS method mildly over-estimates the mixing time, while being less sensitive to the exact mesh geometry. Large eddy simulations with the dynamic Smagorinsky model reasonably capture the kinetic energy contained in macro-instabilities, and properly assess the turbulent kinetic energy in the region between the impellers, even for crude meshes. Consequently, the mixing time is reasonably assessed, and even under-predicted at the crudest meshes. However, the turbulent kinetic energy and energy dissipation in the impeller discharge stream are poorly assessed by the dynamic Smagorinsky model.Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.ChemE/Transport PhenomenaExecutive boardImPhys/Imaging Physic

Euler-Lagrange analysis towards representative down-scaling of a 22 m3 aerobic S. cerevisiae fermentation

With reaction timescales equal to or shorter than the circulation time, the ideal mixing assumption typically does not hold for large scale bioreactors. As a consequence large scale gradients in extra-cellular conditions such as the substrate concentration exist, which may significantly impact the metabolism of micro-organisms and thereby the process performance. The influence of extra-cellular variations on the organism can be tested using so-called scale-down simulators, laboratory scale setups where deliberate, controlled fluctuations are imposed in the extra-cellular environment.ChemE/Transport PhenomenaBT/Bioprocess Engineerin

High-resolution computation predicts that low dissolved CO concentrations and CO gradients promote ethanol production at industrial-scale gas fermentation

Gradients in dissolved gas concentrations are expected to affect the performance of large reactors for anaerobic gas (CO, H2, CO2) fermentation. To study how these gradients, and the dissolved gas concentration level itself, influence the productivity of the desired product ethanol and the product spectrum of C. autoethanogenum, we coupled a CFD model of an industrial-scale gas fermentor to a metabolic kinetic model for a wide range of metabolic regimes. Our model results, together with literature experimental data and a model with constantdissolved gas concentrations, indicate high ethanol specificity at low dissolved CO concentrations, with acetate reduction to ethanol at very low dissolved CO concentrations and combined ethanol and acetate production at higher CO concentrations. The gradient was predicted to increase both the biomass-specific ethanol production rate and the electron-to-ethanol yield by ~25%. This might be due to intensified ferredoxin and NAD+ redox cycles, with the rate of the Rnf complex – a critical enzyme for energy conservation – as key driver towardsethanol production, all at the expense of a reduced flux to acetate. We present improved mechanistic understanding of the gas fermentation process, and novel leads for optimization and fundamental research, by coupling observations from various down-scaled lab experiments to expected microbial lifelines in an industrial-scale reactor.BT/Bioprocess Engineerin