Characterization and optimization of acoustic filter performance by experimental design methodology
Bibliographic record
Abstract
Acoustic cell filters operate at high separation efficiencies with minimal fouling and have provided a practical alternative for up to 200 L/d perfusion cultures. However, the operation of cell retention systems depends on several settings that should be adjusted depending on the cell concentration and perfusion rate. The impact of operating variables on the separation efficiency performance of a 10-L acoustic separator was characterized using a factorial design of experiments. For the recirculation mode of separator operation, bioreactor cell concentration, perfusion rate, power input, stop time and recirculation ratio were studied using a fractional factorial 2(5-1) design, augmented with axial and center point runs. One complete replicate of the experiment was carried out, consisting of 32 more runs, at 8 runs per day. Separation efficiency was the primary response and it was fitted by a second-order model using restricted maximum likelihood estimation. By backward elimination, the model equation for both experiments was reduced to 14 significant terms. The response surface model for the separation efficiency was tested using additional independent data to check the accuracy of its predictions, to explore robust operation ranges and to optimize separator performance. A recirculation ratio of 1.5 and a stop time of 2 s improved the separator performance over a wide range of separator operation. At power input of 5 W the broad range of robust high SE performance (95% or higher) was raised to over 8 L/d. The reproducible model testing results over a total period of 3 months illustrate both the stable separator performance and the applicability of the model developed to long-term perfusion cultures.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".