Alternative fermentation feedstocks such as ethanol can be produced from CO₂ via electrocatalytic processes that coproduce O₂. In this study, industrial-scale fermentation of ethanol with pure O₂ for single cell protein (SCP) production was studied using a modeling approach. This approach considered (i) microbial kinetics, (ii) gas–liquid transfer, and (iii) an exploration of potential operational constraints. The technical feasibility for producing up to 58 kt/y of SCP in a 600 m³ bubble column operating in continuous mode was assessed and attributed mainly to a high O₂ transfer rate of 1.1 mol/(kg h) through the use of pure O₂. However, most of the pure O₂ fed to the fermenter remains unconsumed due to the large gas flows needed to maximize mass transfer. In addition, biomass production may be hampered by high dissolved CO₂ concentrations and by large heat production. The model estimates a microbial biomass concentration of 114 g/kg, with a yield on ethanol of 0.61 g_x/g_ethanol (> 95% (Formula presented.)). Although the large predicted O₂ transfer capacity seems technically feasible, it needs further experimental validation. The model structure allows the analysis of alternative substrates in the same way as identifying the best carbon feedstock.

Gas fermentation processes (using CO₂, CO, H₂, CH₄) have gained significant research and commercial interest in the last years due to their potential for carbon capture and sequestration. The small economic margins of these processes necessitate the use of large-volume bioreactors. For cost-effective gas delivery, we advise using pneumatically agitated bioreactors, like bubble column reactors, compared to traditional stirred-tank reactors. Although scale-up is conventionally done on an empirical and rule-of-thumb basis, rational methods are currently available. The most important one is the knowledge-driven scaling-up approach, wherein (CFD-based) hydrodynamic and kinetic models of large-scale bioreactors guide the design of representative lab-scale experiments. We suggest several future research directions to enhance the predictive capacity of these models and thereby accelerate scaling-up gas fermentation processes.

Efficient operation of bioreactors is crucial for the success of biomanufacturing processes. Traditional Computational Fluid Dynamics (CFD) simulations provide detailed insights but often involve lengthy computation times and complexity, hindering their practicality for real-time applications. This study introduces a novel multivariate unsupervised learning algorithm that clusters bioreactors into physically meaningful regions based on CFD-generated and real-world data. These clusters not only facilitate the determination of internal reactor regimes but also serve as a foundational step for developing compartment models. Our approach utilizes a custom k-means clustering algorithm, which ensures spatial continuity of clusters by incorporating geometric data, and optimizes the number of compartments to maximize physical significance and data retention. This optimization is guided by a Pareto front analysis, balancing the need for clear compartment definition with the preservation of maximum information from the dataset. The effectiveness and versatility of this methodology were verified through case studies involving a 202 m³ Rushton impeller bioreactor (steady state simulation) and an 840 m³ airlift reactor (dynamic simulation). In the airlift reactor, the clustering algorithm accounted for dynamic fluctuations by averaging the simulation results, providing a robust method for incorporating temporal variations into the compartment analysis. The findings highlight the advantages of 3-D compartmentalization in capturing the intricate dynamics of fluid motion and cellular activities, thereby advancing the design of bioreactors and scaling down experiments for more efficient industrial applications.

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.

Abstract: Syngas fermentation to ethanol has reached industrial production. Further improvement of this process would be aided by quantitative understanding of the influence of imposed reaction conditions on the fermentation performance. That requires a reliable model of the microbial kinetics. Data were collected from 37 steady states in chemostats and from many batch experiments that use Clostridium authoethanogenum. Biomass-specific rates from CO conversion experiments were related to each other according to simple reaction stoichiometries and the Pirt equation, with only the ratio of ethanol to acetate production remaining as degree of freedom. No clear dependency of this ratio on dissolved concentrations, such as CO or acetic acid concentration, was found. This is largely caused by the lack of knowledge about the dependency of the CO uptake rate (and hence all other rates) on the CO concentration. This knowledge gap is caused by a lack of dissolved CO measurements. For dissolved H₂, a similar gap applies. Modelling H₂ consumption adds more degrees of freedom to the system, so that more structured experiments with H₂ is needed. The inhibition of gas consumption by acetate and ethanol is partly known but needs further study. Key points: • Set of Clostridium autoethanogenum syngas fermentation data from chemostats. • Unstructured kinetic models can relate most biomass-specific rates to dilution rates. • Lack of dissolved gas measurements limits deeper understanding.

One of the major challenges mankind faces nowadays is combating climate change. A substantial fraction of greenhouse gases are released by industrial processes, as steelmaking, (oil)refinery and waste processing. Emissions from these processes can partly be prevented with a recently developed technology called gas fermentation. Within this process, synthesis gas – amixture containing CO, CO2 and H2 – is converted into ethanol and acetic acid by bacteria such as Clostridium autoethanogenum. These products could be used in a wide range of applications, like fuels, plastics and cosmetics. Whilst gas fermentation is already applied at commercial-scale, challenges in scale-up persists due to complex multi-scale interactions among the bioreactor, gas bubbles, and bacteria. The poor solubility of CO and H2 alongside gas bubble coalescence, leads to low gas-to-liquid mass transfer rates (typically denoted via kLa). Slow mixing in industrial bioreactors (500m3), and high gas conversion rates, result in large spatial variations in dissolved gas concentrations. Bacteria experiencing concentration fluctuations have often been related to decreased process performance...

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 constant
dissolved 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 towards
ethanol 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.

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.

In gas fermentations (using O2, CO, H2, CH4 or CO2), gas-to-liquid mass transfer is often regarded as one of the limiting processes. However, it is widely known that components in fermentation broths (e.g., salts, biomass, proteins, antifoam, and organic products such as alcohols and acids) have tremendous impact on the volumetric mass transfer coefficient kLa. We studied the influence of ethanol on mass transfer in three fermentation broths derived from syngas fermentation. In demineralized water, we observed that the addition of ethanol, the expected product, increased kLa two-fold in the 0–5 g L−1 range, after which near-constant kLa values were obtained. In the fermentation broths, kLa was increased significantly (2–4 fold compared to water) by ethanol supplementation, and to be highly influenced by broth salinity. Our results indicate that kLa is a dynamic parameter in gas fermentation experiments and can be significantly increased due to broth components.

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.