Scale‐up of controlled‐shear affinity filtration using computational fluid dynamics
Bibliographic record
Abstract
Controlled shear affinity filtration (CSAF) is an integrated bioprocess that positions a contoured rotor above a membrane affinity chromatography column to permit the capture and purification of a secreted protein product directly from cell culture. Here, computational fluid dynamics (CFD) simulations previously used on a laboratory-scale unit (Francis et al., Biotechnol. Bioeng. 2005, 95, 1207-1217) are extended to study the fluid hydrodynamics and expected filter performance of the CSAF device for rotor sizes up to 140 cm in radius. We show that the fluid hydrodynamics within the rotor chamber of larger-scale CSAF units are complex and include turbulent boundary layers; thus, CFD likely provides the only reliable route to CSAF scale-up. We then model design improvements that will be required for CSAF scale-up to permit processing of industrial feedstock. The result is the in silico design of a preparative CSAF device with an optimized rotor 140 cm in radius. The scaled up device has an effective filtration area of 5.93 m(2), which should allow for complete processing in ca. 2 h of 1000 L of culture harvested from either a perfusion, fed-batch or batch bioreactor. Finally, a novel method for the parallelization of CSAF units is presented for use in bioprocessing operations larger than 1000 L.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".