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...@en

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.@en