A mechanistic approach for predicting mass transfer in bioreactors, John A. Thomas, Xiaoming Liu, Brian DeVincentis, Helen Hua, Grace Yao, Michael C. Borys, Kathryn Aron, Girish Pendse. Chemical Engineering Science, Volume 237, 2021. https://doi.org/10.1016/j.ces.2021.116538
The biomanufacturing processes that produce biologic drugs has become an extremely important area of study within the pharmaceutical industry. Within such processes, the drug substance is typically produced by living organisms within stirred tank bioreactors that require a continuous supply of sparged oxygen. The overall oxygen transfer rate to the fluid is a nonlinear convolution of the gas bubble size distribution, fluid properties, local fluid energy dissipation rates, and local dissolved oxygen concentrations. The complexity of this process presents challenges to process scale-up and intensification. In this work, we propose, implement, and validate a mechanistic transport model for predicting oxygen transfer rates within stirred tank bioreactors. To begin, we describe the relevant conservation laws and key principles from turbulence theory that govern mass transfer. Next, we present a physics-based modeling approach for solving these equations in tandem and in real-time. We then systematically validate the model against experimental data at operating scales ranging from 5 L to 2000 L. By running the algorithm on graphics processing units (GPUs), the approach is shown to solve at timescales practical for industrial application.
A CFD Digital Twin to Understand Miscible Fluid Blending, John Thomas, Kushal Sinha, Gayathri Shivkumar, Lei Cao, Marina Funck, Sherwin Shang & Nandkishor K. Nere. AAPS PharmSciTech volume 22, Article number: 91, 2021. https://doi.org/10.1208/s12249-021-01972-5
The mixing of stratified miscible fluids with widely different material properties is a common step in biopharmaceutical manufacturing processes. Differences between the fluid densities and viscosities, however, can lead to order-of-magnitude increase in blend times relative to the blending of single-fluid systems. Moreover, the mixing performance in two-fluid systems can be strongly dependent on the Richardson number defined as the ratio of fluid buoyancy to fluid inertia. In this work, we combine lattice Boltzmann transport algorithms with graphics card-based computing hardware to build accelerated digital twins of a physical mixing tanks. The digital twins are designed to predict real-time fluid mechanics with a fidelity that rivals experimental characterization at orders-of-magnitude less cost than physical testing. After validating the twins against measured single- and multi-fluid mixing data, we use them to explore the physics governing fluid blending in stratified two-fluid systems. We use output from the twins to provide general guidance on stratified two-fluid mixing processes, as well as guidance for building such models for other types of physical systems.
Novel evaluation method to determine the local mixing time distribution in stirred tank reactors, J. Fitschen, S. Hofmann, J. Wutz, A.v. Kameke, M. Hoffmann, T. Wucherpfennig, M. Schlüter. Chemical Engineering Science: X, Volume 10, 2021. https://doi.org/10.1016/j.cesx.2021.100098.
Stirred tank reactors are frequently used for mixing as well as heat- and mass transfer processes in chemical and biochemical engineering due to their robust operation and extensive experiences in the past. However, for cell culture processes like mammalian cell expression systems, special requirements have to be met to ensure optimal cell growth and product quality. One of the most important requirements to ensure ideal transport processes is a proper mixing performance, characterized typically by the global mixing time tmix,global or the dimensionless global mixing time tmix,global·n. As an evaluation method for mixing time determination, the time is usually determined until a tracer signal (e.g. conductivity) has reached a constant value after a peak has been introduced (e.g. by adding a salt). A disadvantage of this method is, that the position of tracer feeding as well as the position of the probe significantly influences the detected mixing time. Further on, the global mixing time does not provide any information about the spatial and temporal ”history” of the mixing process to identify areas that are mixed poorly or areas that form stable compartments. To overcome this disadvantage, a novel image analysis will be presented in this study for the detailed characterization of mixing processes by taking into account the history of mixing. The method is based on the experimental determination of the local mixing time distribution by using a multi-color change caused by a pH-change in a bromothymol blue solution. A 3L transparent stirred tank reactor is used for the benchmark experiment. To demonstrate the suitability of the new characterization method for the validation of numerical simulations, a calculation with a commercial Lattice-Boltzmann approach (M-Star CFD) has been performed additionally and evaluated regarding mixing time distributions. The exemplary application of image analysis to a numerical mixing time simulation shows good agreement with the corresponding experiment. On the one hand, this shows that the method can also be interesting for numerical work, especially for experimental validation, and on the other hand, this allows much deeper insights into the mixing behavior compared to conventional mixing criteria. For example the new method enables the characterization of mixing on different scales as well as the identification of micro- and macroscopic flow structures. The strong influence of the acid to base ratio on mixing time experiments becomes clearly visible with the new method.