Bayesian calibration of bubble size dynamics applied to CO2 gas fermenters

TL;DR

This paper develops a Bayesian calibration method for bubble size dynamics in CO2 fermenters, improving gas transfer predictions by integrating experimental data with neural network-accelerated inverse modeling.

Contribution

It introduces a Bayesian calibration approach for bubble breakup and coalescence models in CO2 fermentation reactors, enhancing model accuracy beyond existing methods.

Findings

Increased breakage rate improves gas holdup prediction.

Calibrated models better predict bubble size distribution.

Bayesian inference accounts for experimental noise.

Abstract

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. However, the applicability of existing models beyond air-water mixtures remains to be established. Here, an inverse modeling approach, accelerated with a neural network surrogate, calibrates the breakup and coalescence closure models, that are used in class methods for population balance modeling (PBM). The calibration is performed based on 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. To accurately capture gas holdup and interphase mass transfer, the results show that the breakage rate needs to be increased by one order of magnitude. The inferred model parameters are then used on a separate configuration and shown to also improve bubble size distribution predictions.