(326e) Accelerating Bioprocess Simulations: A Compartment Modeling Framework Developed Using Axisymmetric CFD for Industrial Fermenters

Large-scale fermentation processes often exhibit significant spatial heterogeneities in substrate concentrations, dissolved oxygen (DO) levels, and product distributions due to insufficient mixing. These gradients can adversely affect microbial performance, process yield, and product quality, especially in aerobic fed-batch operations, where fermenter volumes dynamically change over the course of fermentation [1,2]. While computational fluid dynamics (CFD) offers detailed insights into the mixing behavior and oxygen transfer characteristics within fermenters, the substantial computational resources required for CFD limit its utility for continuous, real-time evaluation and control of large-scale industrial processes.

To overcome these computational limitations, we have developed a compartment modeling (CM) framework that integrates transient hydrodynamic data obtained from axisymmetric CFD simulations. The axisymmetric CFD model leverages the inherent cylindrical symmetry commonly present in industrial fermenters, significantly reducing computational demand without compromising the accuracy of the hydrodynamic predictions [3]. This approach captures essential axial and radial flow features, including turbulence intensity, velocity fields, and flow topology, essential for accurate compartmentalization. The developed CM framework explicitly addresses the transient nature of hydrodynamics in industrial fed-batch fermentations. It dynamically accounts for volume changes and incorporates variations in dissolved oxygen throughout the process duration, providing accurate spatiotemporal predictions of biomass growth, substrate consumption, product formation, and oxygen distribution [4]. Using velocity profiles and turbulent kinetic energy data from CFD, we systematically divided the fermenter into ideally mixed compartments, establishing flow connections based on mass transport principles and ensuring robust model convergence [5].

This methodology enables rapid simulation of concentration gradients and localized mixing inefficiencies, significantly outperforming traditional CFD in terms of computational efficiency, approximately 500 times faster while retaining critical hydrodynamic fidelity. Validation against experimental data from a 525 kL industrial fermenter demonstrated the model's capability to accurately predict spatial heterogeneities in biomass, substrates, and product concentrations, alongside dissolved oxygen profiles. By providing real-time insights into areas experiencing oxygen limitation or suboptimal mixing conditions, the CM offers valuable guidance for process optimization strategies, including impeller positioning, aeration control, and substrate feeding schedules. Consequently, this compartment modeling framework stands as a powerful, scalable tool for real-time decision-making, facilitating enhanced productivity, improved product quality, and more consistent outcomes in large-scale bioprocessing operations.

References:

1. Delafosse, A., Collignon, M. L., Calvo, S., Delvigne, F., Crine, M., Thonart, P., & Toye, D. (2014). CFD-based compartment model for description of mixing in bioreactors. Chemical Engineering Science, 106, 76-85.
2. Nadal-Rey, G., McClure, D. D., Kavanagh, J. M., Cassells, B., Cornelissen, S., Fletcher, D. F., & Gernaey, K. V. (2021). Development of dynamic compartment models for industrial aerobic fed-batch fermentation processes. Chemical Engineering Journal, 420, 130402.
3. de León, H. M., Straathof, A., & Haringa, C. (2025). Dynamic compartment models: Towards a rapid modeling approach for fed-batch fermentations. Chemical Engineering Science, 308, 121396.
4. Shah, P., Sheriff, M. Z., Bangi, M. S. F., Kravaris, C., Kwon, J. S. I., Botre, C., & Hirota, J. (2022). Deep neural network-based hybrid modeling and experimental validation for an industry-scale fermentation process: Identification of time-varying dependencies among parameters. Chemical Engineering Journal, 135643.
5. Bisgaard, J., Zahn, J. A., Tajsoleiman, T., Rasmussen, T., Huusom, J. K., & Gernaey, K. V. (2022). Data-based dynamic compartment model: Modeling of E. coli fed-batch fermentation in a 600 m3 bubble column. Journal of Industrial Microbiology and Biotechnology, 49(5), kuac021.