Recently, a mechanism has been proposed to describe how salts do (not) contribute to bubble coalescence inhibition. This is done by considering how ions (re)distribute themselves along the G/L-interface, expressing this as a Gibbs-Marangoni pressure. The thesis’ goal is to investigate the potential and applicability of this mechanism to predict bubble coalescence inhibition for salts in biotechnological processes, specifically bubble columns. This goal is achieved by recreating the mechanism and evaluating whether the Gibbs- Marangoni pressure is a better predictor than the ionic strength. To make this comparison, experiments are performed to study which ionic strengths/concentrations induce bubble coalescence inhibition. These numbers are then modelled in terms of Gibbs-Marangoni pressures, from where it is assessed which of the two better predicts this inhibition. More- over, additional experimental results from the literature have been tested on the model to support this assessment. The outcome of this thesis is a combination of multiple interesting insights. First, the above mechanism is successfully reproduced into a model. This statement is supported by com- paring the model’s results with previous literature applying this mechanism, showing that results deviate less than 10% from each other. Secondly, from the performed experiments, bubble coalescence inhibition by salts is observable at an average ionic strength of 47.82 mM. Using these results in the model shows that this inhibition happens at an average Gibbs-Marangoni pressure of 1312.91 Pa. How- ever, the inhibiting salt concentrations from the experiments deviate by more than 168% compared to the literature. This difference can be caused by many factors, such as dissimilarities in the experimental setups used. It shows that the critical Gibbs-Marangoni pressure is dependent on these factors and therefore not entirely generalizable for all scenarios. Finally, the model’s results are validated by comparing whether the additionally calculated surface tensions deviate less than 10% from the literature. This showed resulting Gibbs- Marangoni pressures are valid for pure salts at concentrations from 0.02-1.50 M. However, the validity of the model for salt mixtures is questionable. To conclude, the proposed mechanism can describe bubble coalescence inhibition for salts in bubble columns. However, its accuracy is debatable as the model’s results cannot be validated for mixtures. This may require the inclusion of other ion contributions into the model, such as ion polarizability, solvation energy and ion-charge density. Hopefully, this brings us closer to better modelling the effect of salts, and other compounds, on bubble coalescence inhibition.

The oxygen mass transfer coefficient, kLa, is a critical parameter in bioprocess performance, influencing the design of stirred tank reactors. During a fermentation process, the behavior of bubbles and the kLa can be effected by various components present in the broth. This study investigates the effect of organic compounds (ethanol, glycerol and acetic acid), which are produced during a yeast fermentation process (S. cerevisiae). It is concluded that acetic acid appears to decrease the geometric mean bubble diameter, in aqueous solutions. As a surfactant, the acetic acid molecules accumulate in the gas-liquid interface, thereby inhibiting bubble coalescence. This leads to an increase in interfacial area and a 34% rise in kLa has been measured. Glycerol, however, showed no significant impact in water. Ethanol should exhibit a similar trend to acetic acid, as reported in previous literature (Puiman, Elisiário, et al., 2022). Remarkably, when these organic compounds were added to growth medium for a yeast fermentation - which consist of synthetic medium, glucose and vitamins - the effects were negligible. This likely due to the coalescence-enhancing properties of antifoam, which is present in synthetic media. On the other hand, the concentrations of the organic compounds tested were relatively low. In addition, during the fermentation process, the production of organic compounds did not significantly affect the bubble size. A slight increase in kLa was observed, arguably due to a reduction in working volume. Whilst performing the tests, the kLa was measuring with three different experimental determination methods: the dynamic pressure method, the dynamic gassing-out method, and the gaseous oxygen balance method. The dynamic gassing-out method appeared a bit more consistent than the dynamic pressure method, possibly due to pressure stabilization issues. The gaseous oxygen balance method did not provide consistent results throughout the entire fermentation, as the method is sensitive and relies on very accurate gas measurements, especially when the oxygen consumption is low. The dynamic gassing-out method proved to be stable and provided the most results used for comparisons of experiment. However, considering its limitations, it requires validation with a ground truth, such as a validated reliable chemical method. The experimental kLa data was compared with predicted values, using empirical correlations. All predicted values remained within 50% margin to the experimental values (determined with dynamic gassing-out method). To be able to use these prediction models in bioprocess design, further investigation is essential.

An optical fibre probe was tested in liquids with biotechnologically relevant compounds, to determine if any limits arise in bubble property determination due to these compounds. The data from this fibre probe was compared to the data from a camera for which an image processing algorithm was developed. This algorithm was able to successfully filter objects using configurable parameters. Objects had to be in focus, within a certain size range and elliptical in shape. The filtering was able to work with partially out of focus bubbles, and bubbles at the edges of the image within tolerances specified by the parameters. To provide insight into the relation between the fibre probe data and camera data a control measurement was performed in pure water, after which measurements were performed in a mixture containing 100g/L ethanol and a mixture containing 0.4M NaCl. The data sets from both measurement methods were compared for bubble velocity and bubble size. The velocity would be directly comparable,
the size was however the local height at the point where the fibre probe pierced the bubbles. As such the height directly below the fibre probe of the bubbles was determined from the images to serve as comparable data. The comparison between the probe data and camera data was inconclusive due to a dissimilarity between the distributions. Mainly the overestimation of the comparable size from the camera data was a problem. Which was likely due to the camera showing only a 2D projection of the bubble and as such the entire height would be used for this comparison. For the velocity the camera data had a narrower distribution as compared to the fibre probe data. This could be caused by the camera averaging the speed of bubbles between two or even three frames if a bubble was not detected in the middle frame, the probe might be more sensitive to these variations and as such yield a wider distribution. The image processing algorithm showed a correct processing of the images to yield usable data, the exact comparable data however still needs to be improved for this specific application.

Understanding the dynamics of bubbles in broth is crucial for various biotechnological processes conducted in bubble columns, such as the manufacturing of cultured meat. In this study, the drag coefficient of individual bubbles ascending in sodium chloride, sodium sulphate, albumin, and egg-white protein solutions was determined. This was accomplished by extracting bubble characteristics, such as the equivalent diameter and terminal velocity, from images captured by a camera system, utilizing bubble detection by a software suite. The obtained terminal velocities and computed drag coefficients were then compared to existing predictive models developed by Clift, Fan and Tsuchiya, Ishii and Zuber, and Peebles and Garber. This comparative analysis was conducted to assess the accuracy of these models in predicting bubble behavior in contaminated water. The results indicated that the drag coefficient of single bubbles rising in salt and protein solutions is higher compared to when ascending in demineralized water. The drag coefficient of single bubbles rising in protein solutions predicted by the drag model of Ishii and Zuber aligned well with the experimentally derived drag coefficients. However, both drag models tended to underpredict the drag coefficient of bubbles ascending in protein solutions. In the case of salt solutions, the drag coefficient predictions by Peebles and Garber approached the experimentally derived drag coefficients more closely although both models tended to overpredict the drag coefficient of bubbles ascending in salt solutions. Additional data acquisition of bubbles within the same size distribution rising in the examined salt and protein solutions is needed to validate and further refine these observations.