Zonal rate model for axial and radial flow membrane chromatography, part II: Model‐based scale‐up
Bibliographic record
Abstract
Membrane chromatography (MC) systems are finding increasing use in downstream processing trains for therapeutic proteins due to the unique mass-transfer characteristics they provide. As a result, there is increased need for model-based methods to scale-up MC units using data collected on a scaled-down unit. Here, a strategy is presented for MC unit scale-up using the zonal rate model (ZRM). The ZRM partitions an MC unit into virtual flow zones to account for deviations from ideal plug-flow behavior. To permit scale-up, it is first configured for the specific device geometry and flow profiles within the scaled-down unit so as to achieve decoupling of flow and binding related non-idealities. The ZRM is then configured for the preparative-scale unit, which typically utilizes markedly different flow manifolds and membrane architecture. Breakthrough is first analyzed in both units under non-binding conditions using an inexpensive tracer to independently determine unit geometry related parameters of the ZRM. Binding related parameters are then determined from breakthrough data on the scaled-down MC capsule to minimize sample requirements. Model-based scale-up may then be performed to predict band broadening and breakthrough curves on the preparative-scale unit. Here, the approach is shown to be valid when the Pall XT140 and XT5 capsules serve as the preparative and scaled-down units, respectively. In this case, scale-up is facilitated by our finding that the distribution of linear velocities through the membrane in the XT140 capsule is independent of the feed flow rate and the type of protein transmitted. Introduction of this finding into the ZRM permits quantitative predictions of breakthrough over a range of industrially relevant operating conditions.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".