  The use of microbial gas fermentation for transforming captured CO2 into
sustainable fuels and chemicals has been identified as a promising
decarbonization pathway. To accelerate the scale-up of gaseous CO2 fermentation
reactors, computational models need to predict gas-to-liquid mass transfer
which requires capturing the bubble size dynamics, i.e. bubble breakup and
coalescence. In this work, an inverse modeling approach is used to calibrate
the breakup and coalescence closure models, that are used in the
Multiple-Size-Group (MUSIG) population balance modeling (PBM). The calibration
aims at replicating experimental results obtained in a CO2-air-water-coflowing
bubble column reactor. Bayesian inference is used to account for noise in the
experimental dataset and bias in the simulation results. The estimated
simulation bias also allows identifying the best-performing closure models
irrespective of the model parameters used. The calibration results suggest that
the breakage rate is underestimated by one order of magnitude in two different
breakup modeling approaches.
