(59t) Improving Industrial-Scale Bioreactor Performance: Development and Validation of Computationally Efficient Compartment-Based Models Using Real Plant Data

Large-scale bioreactors frequently display spatial heterogeneity in cultivation variables, such as substrate and oxygen concentrations, due to poor mixing within the reactor environments. This variation in gradients can expose microorganisms to fluctuating conditions, impacting vital process metrics and physiology. Consequently, understanding the evolution of the local environment of large-scale processes spatially and temporally is crucial for enhancing product quality and yield. Computational fluid dynamics (CFD) is commonly used to spatially visualize bioreactor processes and study system hydrodynamics. However, due to its high resolution, CFD requires a large mesh size and transient modeling, resulting in substantial computational demands that limit fermenter simulations to just a few seconds [1]. This makes simulating the entire duration of a fermentation process infeasible.

To overcome this limitation, we have developed compartment-based models capable of simulating hydrodynamics throughout the entire process duration. These models combine multiple ideally mixed volumes, with flows and connections determined by CFD-based compartment design [2]. Initially, CFD simulations, validated with an industrial-scale bioreactor process, are conducted for specific time snapshots and compartmentalized based on velocity profiles to define compartment volumes and flows [3]. The resulting compartment is integrated with microbial kinetics to predict local concentrations of substrate, biomass, and glucose in a spatiotemporal domain [4]. Additionally, our compartment model accounts for volume changes, like an increase or decrease in volume during process operation. In a case study, we validated this compartment model against data from a real industrial-scale process, which served as the foundation for generating CFD data for various bioreactor volumes and constructing compartments based on axial and radial velocities [5]. The compartment model was initially tested by comparing mixing time and oxygen transfer rates. Subsequently, we integrated volume change and kinetic equations into the compartment model and compared concentration gradients with CFD results for specific volumes. The results of the compartment model showed strong consistency with both the CFD simulations and the data from the industrial-scale process, enabling a comprehensive analysis of the entire spatiotemporal profile for all state concentrations. The compartment model proved useful for predicting metabolic regimes, identifying regions with high or low oxygen uptake, and examining different reactor designs and operating conditions to estimate the optimal conditions for organisms.

In conclusion, our validated dynamic compartment model effectively accounts for fluid dynamics, kinetic, and volume change, providing a comprehensive description of spatiotemporal gradients in local concentrations over the entire duration of large-scale fermentation processes. Boasting a runtime nearly 10⁵ times faster than a CFD model with kinetics, the compartment model presents a more efficient alternative due to its fewer ideally mixed volumes and less complex ordinary differential equations.

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. Tajsoleiman, T., Spann, R., Bach, C., Gernaey, K. V., Huusom, J. K., & Krühne, U. (2019). A CFD based automatic method for compartment model development. Computers & Chemical Engineering, 123, 236-245.
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.