(263f) A Novel Modelling Approach Coupling Multiphase Hydrodynamics and Biokinetics for Simulating Large-Scale Bioreactors

Large-scale bioreactors are challenging systems to model due to the multiple physical and biological phenomena taking place during fermentation. Bioreactor models should account for mass transport limitations (due to gas–liquid mixing and interfacial mass transfer) taking place at larger scales as well as chemical and biochemical conversions. Although biological growth typically occurs over a timescale of hours/days, biochemical reactions taking place inside the cell such as enzymatic reactions and allosteric regulation typically occur over a timescale of milliseconds/seconds [1,2,3]. Moreover, the consumption of substrates can occur on a timescale lower than the fluid circulation time in large-scale bioreactors, leading to the formation of spatial concentration gradients. To capture the interactions between material transport and chemical/biochemical conversions, models should include both phenomena. Computational fluid dynamics (CFD) offers a fine resolution of liquid/gas–liquid hydrodynamics in various fermentation equipment but it is an approach that requires long computational times to simulate short periods of equipment operating time [4,5]. This is due to the fine spatial discretization required to solve the mass and momentum equations. The high computational demand of CFD simulations is a clear limitation for applications where real-time model evaluations are required, e.g., in the case of model-based monitoring and control and implementation in digital twins. The computational demand and the difficult coupling with chemical reactions make it infeasible to simulate entire fermentations using CFD.

Compartment models, networks of ideally mixed compartments with given volumes and exchange flowrates, offer an alternative to CFD to simulate fluid mixing. This approach is less computationally demanding, but also less accurate than CFD due to the flow being specified as an input by the user. Recent work has focused on informed formulation of the compartments from experimental data or CFD results.

A CFD-based compartmentalization method for single phase systems [6] was previously proposed and is now extended to account for gas-liquid hydrodynamics, including bubble-induced turbulence in the liquid phase, and mass transfer of gaseous species into the liquid phase.

The developed method was used to simulate an aerobic fermentation of Saccharomyces cerevisiae in a 100m3 stirred tank. The results reveal the importance of quantifying spatial gradients in large scale reactors, and to include their impact on the process dynamics. Moreover, the efficiency of the employed numerical solvers allows for fast model evaluations, enabling to test various operating conditions, and discover optimal operation strategies in terms of design and control of the reactors.

1. Nadal-Rey, G., McClure, D. D., Kavanagh, J. M., Cornelissen, S., Fletcher, D. F., & Gernaey, K. v. (2021). Understanding gradients in industrial bioreactors. In Biotechnology Advances (Vol. 46). https://doi.org/10.1016/j.biotechadv.2020.107660
2. Dai, X., Zhu, M., Warren, M., Balakrishnan, R., Patsalo, V., Okano, H., Williamson, J. R., Fredrick, K., Wang, Y. P., & Hwa, T. (2016). Reduction of translating ribosomes enables Escherichia coli to maintain elongation rates during slow growth. Nature Microbiology, 2. https://doi.org/10.1038/nmicrobiol.2016.231
3. Balakrishnan, R., Mori, M., Segota, I., Zhang, Z., Aebersold, R., Ludwig, C., & Hwa, T. (2022). Principles of gene regulation quantitatively connect DNA to RNA and proteins in bacteria. Science, 378(6624). https://doi.org/10.1126/science.abk2066
4. Spann, R., Glibstrup, J., Pellicer-Alborch, K., Junne, S., Neubauer, P., Roca, C., Kold, D., Lantz, A. E., Sin, G., Gernaey, K. v., & Krühne, U. (2019). CFD predicted pH gradients in lactic acid bacteria cultivations. Biotechnology and Bioengineering, 116(4). https://doi.org/10.1002/bit.26868
5. Haringa, C., Tang, W., Wang, G., Deshmukh, A. T., van Winden, W. A., Chu, J., van Gulik, W. M., Heijnen, J. J., Mudde, R. F., & Noorman, H. J. (2018). Computational fluid dynamics simulation of an industrial P. chrysogenum fermentation with a coupled 9-pool metabolic model: Towards rational scale-down and design optimization. Chemical Engineering Science, 175. https://doi.org/10.1016/j.ces.2017.09.020
6. le Nepvou De Carfort, J., Pinto, T., & Krühne, U. (2024). An Automatic Method for Generation of CFD-Based 3D Compartment Models: Towards Real-Time Mixing Simulations. Bioengineering, 11(2). https://doi.org/10.3390/bioengineering11020169