• Developed a CM framework validated against CFD for industrial bioreactors. • Achieved 500 × faster simulations while maintaining hydrodynamic accuracy. • Integrated transient flow dynamics to capture evolving mixing patterns. • Validated mass transfer predictions using CFD-derived kLa values. • Enables real-time process optimization and kinetic model integration. Industrial-scale fermentation processes often suffer from insufficient mixing, which leads to significant spatial heterogeneity in key variables such as dissolved oxygen and substrate concentrations. These gradients adversely impact oxygen transfer, microbial physiology, and overall process productivity. While computational fluid dynamics (CFD) simulations offer detailed insights into flow behavior, their computational requirement limits their applicability in large-scale systems. This study presents the development of a computationally efficient compartment modeling (CM) framework as an alternative to CFD for industrial fermenters. To this end, first, an axisymmetric CFD model was constructed to represent the industrial fermenter geometry. This approach was chosen over a full three-dimensional model to leverage the symmetry inherent in many reactor designs, reducing computational costs while still capturing essential hydrodynamic features such as axial and radial velocity components and turbulent kinetic energy data across different time instances. These variables were extracted to define flow topology and inter-compartment flow rates within the CM. By averaging velocity fields and smoothing gradients, a stable and scalable compartmental representation of reactor hydrodynamics was achieved. The model was validated against CFD data through visualizations of flow patterns and velocity profiles, demonstrating its ability to capture spatial heterogeneity while significantly reducing computational costs. This CM framework offers a versatile tool for integrating kinetic models, facilitating process optimization, and improving design in complex fermenter systems.
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Shah et al. (Fri,) studied this question.
www.synapsesocial.com/papers/69a767eebadf0bb9e87e2edc — DOI: https://doi.org/10.1016/j.ces.2026.123502
Parth D. Shah
Satchit Nagpal
Dong Hun Kwak
Chemical Engineering Science
The Ohio State University
Texas A&M University
Chungnam National University
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