CFD simulations are widely used to quantify the mixing performance of stirred tanks for various applications in chemical engineering and biotechnology. Due to advances in GPU computing, these simulations increasingly employ Large Eddy Simulation (LES), which explicitly resolves the dynamics of large-scale turbulence. Although such simulations are fully deterministic and therefore theoretically reproducible, small numerical variations induced by round-off errors, floating-point arithmetic, and differences in the distribution and ordering of operations in parallel computing lead to separation of trajectories i.e., different flow-field evolutions and consequently to significant run-to-run variability in predicted mixing times, even on the same hardware architecture. This work investigates the impact of repeated simulations, in the form of a case study, on the mixing-time distribution observed in a (Formula presented) stirred tank reactor using two commercial CFD packages operating with representative, production-level solver configurations. The analysis does not aim to assess the general performance of numerical method classes, but rather to quantify run-to-run variability under fixed solver settings and to compare the resulting numerical distributions to experimental variability. The results demonstrate that numerical variability is of comparable magnitude to the experimental spread, highlighting the necessity to treat LES-derived metrics as statistical ensembles rather than deterministic values. It is concluded that the reporting of confidence intervals is essential for methodological rigour in LES-based mixing studies.

Controlling cell population dynamics and phenotypic diversification is a key objective in systems and synthetic biology, particularly for ensuring uniform responses from engineered gene circuits. While cell-machine interfaces have been employed to modulate host-gene circuit interactions, environmental perturbations typical of industrial bioreactor conditions remain underexplored. In this study, we investigate the impact of such perturbations on the general stress response in Escherichia coli and Saccharomyces cerevisiae. Using scale-down bioreactor experiments, we evaluate the performance of the Segregostat, a real-time control system that leverages automated flow cytometry to induce dynamic nutrient shifts. The Segregostat achieves robust stress response control, even under severe perturbations such as extended residence times in a two-compartment reactor. We hypothesise that this robustness arises from the system's ability to amplify host-compatible fluctuations beyond bioreactor-induced perturbations. Our findings highlight the importance of integrating environmental factors into control strategies for reliable gene circuit behaviour in industrial bioprocessing environments.

Flow-following sensor technology offers a method to collect information on flow patterns and local velocities in pilot- and industrial scale reactors, which are practically inaccessible to many measurement techniques. Such data is highly valuable for scale-up of bioprocesses, as well as validation of bioreactor CFD simulations. Flow-following sensors were applied in a pilot-scale (2 m³ filled volume) bubble column fermentor, showing that axially resolved data can be acquired under heterogeneous bubbly flow conditions with high gas holdup. Next the use of the collected data for validation of CFD simulations of the pilot-scale reactor is explored, discriminating between models utilizing different interphase interaction models. The CFD simulation was found capable of capturing the velocity profile and circulation behavior, but full validation was found to be challenging. When simulating virtual sensors via Lagrangian particle tracking, differences are observed in terms of particle distribution and sensitivity to particle density between experimental and simulated data, indicating further development of representative CFD simulations is required.

The growing global population and heightened concern for climate change leads to increased interest in utilizing microbial fermentations to replace polluting production processes for e.g., plastics, fuels, and animal proteins. Computational fluid dynamics (CFD) is a valuable tool for accelerating the scale-up and optimization of large-scale bioprocesses. However, the design correlations underlying most of these CFD models are validated with air-water systems, not accounting for the distinct hydrodynamic properties of microbial fermentation broth. In this review, we provide an extensive overview of the current understanding of how various biotechnologically relevant solutes impact the hydrodynamics of bubble columns. We examine the effects of components found in fermentation broths, including salts, surfactants, viscoelastic solutes, alcohols, acids, ketones, sugars, biomass, and proteins, on mass transfer, bubble formation, bubble interactions, and flow regime transitions. These components all exhibit unique effects, yet their combined influences remain poorly understood. Future research should prioritize identifying the concentration at which coalescence inhibition occurs for different compounds, especially in mixtures, and exploring the role of proteins in bubble column hydrodynamics from micro- to macroscale.

In large-scale syngas fermentation, strong gradients in dissolved gas (CO, H₂) concentrations are very likely to occur due to locally varying mass transfer and convection rates. Using Euler-Lagrangian CFD simulations, we analyzed these gradients in an industrial-scale external-loop gas-lift reactor (EL-GLR) for a wide range of biomass concentrations, considering CO inhibition for both CO and H₂ uptake. Lifeline analyses showed that micro-organisms are likely to experience frequent (5 to 30 s) oscillations in dissolved gas concentrations with one order of magnitude. From the lifeline analyses, we developed a conceptual scale-down simulator (stirred-tank reactor with varying stirrer speed) to replicate industrial-scale environmental fluctuations at bench scale. The configuration of the scale-down simulator can be adjusted to match a broad range of environmental fluctuations. Our results suggest a preference for industrial operation at high biomass concentrations, as this would strongly reduce inhibitory effects, provide operational flexibility and enhance the product yield. The peaks in dissolved gas concentration were hypothesized to increase the syngas-to-ethanol yield due to the fast uptake mechanisms in C. autoethanogenum. The proposed scale-down simulator can be used to validate such results and to obtain data for parametrizing lumped kinetic metabolic models that describe such short-term responses.

This study focuses on the metabolic impacts of simultaneous glucose and oxygen concentration gradients on penicillin production in an industrial-scale fermentor, using the computational fluid dynamics-cellular reaction dynamics approach. Inclusion of oxygen-coupling considerably impacts the glucose uptake and resulting penicillin productivity. This is characterised by six metabolic regimes; lifeline data reconstructed from experimental results, recorded from the cellular perspective, indicates rapid dynamics in glucose and dissolved oxygen uptake by the microorganisms. The results are highly sensitive to variations in the oxygen-related model parameters, requiring accurate insight into the multiphase hydrodynamics and metabolic processes. Hypothetical scenarios with stronger glucose-oxygen limitations than tested experimentally were further explored. A precision scale-down (SD) simulator was designed based on the lifeline data, requiring considerable operational dynamics, with increasing system complexity and implementation difficulty. These insights may inspire further research into alternative SD configurations better suited to mimic the rapid dynamics of large-scale fermentation processes.

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.

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.

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.

Mass transfer limitations in syngas fermentation processes are mostly attributed to poor solubility of CO and H₂ in water. Despite these assumed limitations, a syngas fermentation process has recently been commercialized. Using large-sale external-loop gas-lift reactors (EL-GLR), CO-rich off-gases are converted into ethanol, with high mass transfer performance (7–8.5 g.L^-1.h⁻¹). However, when applying established mass transfer correlations, a much poorer performance is predicted (0.3–2.7 g.L^-1.h⁻¹). We developed a CFD model, validated on pilot-scale data, to provide detailed insights on hydrodynamics and mass transfer in a large-scale EL-GLR. As produced ethanol could increase gas hold-up (+30%) and decrease the bubble diameter (≤2 mm) compared to air–water mixtures, we found with our model that a high volumetric mass transfer coefficient (650–750 h⁻¹) and mass transfer capacity (7.5–8 g.L^-1.h⁻¹) for CO are feasible. Thus, the typical mass transfer limitations encountered in air–water systems can be alleviated in the syngas-to-ethanol fermentation process.

Scaling up bioprocesses is one of the most crucial steps in the commercialization of bioproducts. While it is known that concentration and shear rate gradients occur at larger scales, it is often too risky, if feasible at all, to conduct validation experiments at such scales. Using computational fluid dynamics equipped with mechanistic biochemical engineering knowledge of the process, it is possible to simulate such gradients. In this work, concentration profiles for the by-products of baker’s yeast production are investigated. By applying a mechanistic black-box model, concentration heterogeneities for oxygen, glucose, ethanol, and carbon dioxide are evaluated. The results suggest that, although at low concentrations, ethanol is consumed in more than 90% of the tank volume, which prevents cell starvation, even when glucose is virtually depleted. Moreover, long exposure to high dissolved carbon dioxide levels is predicted. Two biomass concentrations, i.e., 10 and 25 g/L, are considered where, in the former, ethanol production is solely because of overflow metabolism while, in the latter, 10% of the ethanol formation is due to dissolved oxygen limitation. This method facilitates the prediction of the living conditions of the microorganism and its utilization to address the limitations via change of strain or bioreactor design or operation conditions. The outcome can also be of value to design a representative scale-down reactor to facilitate strain studies.