(518b) Optimizing Bioreactor Efficiency at Industry-Scale: Leveraging CFD and Real-World Data for Advanced Spatiotemporal Analysis through a Compartment Modeling Approach

Large-scale bioreactors, central to numerous biotechnological applications, are characterized by spatial heterogeneity in essential cultivation variables like substrate and oxygen concentrations. This heterogeneity arises due to suboptimal mixing, subjecting microorganisms to fluctuating environmental conditions, thereby affecting crucial process metrics such as productivity and product quality. Computational Fluid Dynamics (CFD) serves as a pivotal tool in understanding bioreactor hydrodynamics; however, its applicability is marred by computational demands, limiting simulations to transient snapshots rather than the entirety of bioprocess durations [1].

Overcoming this bottleneck, this work introduces a novel compartment modeling approach, enhancing our capacity to simulate and optimize large-scale fermentation processes over their full duration [2]. To this end, we leverage compartment-based models that incorporate the entire process time, integrating CFD-derived compartment designs with microbial kinetics represented by ordinary differential equations (ODEs) [3]. Our approach delineates the bioreactor space into a series of ideally mixed compartments, derived from detailed CFD simulations capturing the complex flow patterns within industrial-scale reactors. This compartmentalization, performed by axial and radial velocity distributions, enables a nuanced replication of reactor hydrodynamics [4,5]. Through meticulous validation against industrial-scale data, our models demonstrate an unparalleled ability to capture the nuanced spatiotemporal profiles of state concentrations, thus offering a deeper understanding of metabolic regimes across the reactor volume. Significantly, this framework enables the identification of zones experiencing sub-optimal conditions, such as oxygen limitations and substrate consumption, thereby informing targeted interventions to enhance process performance.

This compartment model's validation against industrial process data underscores its fidelity in replicating actual process conditions, exhibiting remarkable consistency with both CFD simulations and empirical observations. By capturing the entirety of the fermentation process in a computationally efficient manner, this model stands out as a superior alternative to traditional CFD models, reducing simulation runtimes significantly while maintaining a high level of accuracy. In conclusion, our validated dynamic compartment modeling approach marks a significant step forward in simulating large-scale fermentation processes. By integrating detailed hydrodynamic behaviors with process kinetics, this framework not only surpasses the temporal limitations of conventional CFD analyses but also delivers a granular view of bioreactor heterogeneity. With its ability to run simulations over times faster than CFD models, our compartment model stands as a potent tool for bioprocess optimization, heralding a new era of precision in biomanufacturing.

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. 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.
4. 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.
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.