Modeling of scale‐down effects on the hydrodynamics of expanded bed adsorption columns
Bibliographic record
Abstract
Expanded-bed adsorption (EBA) is a technique for primary recovery of proteins starting from unclarified broths. This process combines centrifugation, concentration, filtration, and initial capturing of the proteins in a single step. An expanded bed (EB) is comparable to a packed bed in terms of separation performance but its hydrodynamics are that of a fluidized bed. Downstream process development involving EBA is normally carried out in small columns to minimize time and costs. Our purpose here is to characterize the hydrodynamics of expanded beds of different diameters, to develop scaling parameters that can be reliably used to predict separation efficiency of larger EBA columns. A hydrodynamic model has been developed which takes into account the radial liquid velocity profile in the column. The scale-down effect can be characterized in terms of apparent axial dispersion, D(axl,app), and plate number, N(EB), adapted for expanded bed. The model is in good agreement with experimental results obtained from 1- and 5-cm column diameters with buffer solutions of different viscosities. The model and the experiments show an increase of apparent axial dispersion with an increase in column diameter. Furthermore, the apparent axial dispersion is affected by an increase in liquid velocity and viscosity. Supported by visual observations and predictions from the model, it was concluded that operating conditions (liquid viscosity and superficial velocity) resulting in a bed-void fraction between 0.7 and 0.75 would provide the optimal separation efficiency in terms of N(EB).
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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) | 0.000 | 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 | 0.000 |
| 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".