Use of cell bleed in a high cell density perfusion culture and multivariable control of biomass and metabolite concentrations
Bibliographic record
Abstract
Abstract A main problem in controlling bioprocesses is the lack of manipulated variables. Batches and fed‐batches cannot be drained of the waste substances produced by the biomass. A chemostat (CSTR) may have the dilution rate as the manipulated variable, allowing a certain control over the biomass concentration with a risk, however, of washout if the dilution rate gets higher than the maximum growth rate. Perfusion processes with full biomass retention are somewhat similar to batches, as no steady state is really obtained until biomass growth is stopped by nutrient limitations. Cell bleed is often used in perfusions to improve the overall cell culture viability, and prevent accumulation of dead cells. However, use of the cell bleed stream as a manipulated variable for control has not yet received much attention. This paper's main contribution is the use of cell bleed as an additional degree of freedom in a multivariable control strategy for a perfusion culture. To add to the originality of the contribution, the control strategy used is multivariable nonlinear adaptive backstepping, which has never been used for a perfusion bioreactor. Results show a good performance of the controller, while the chosen set points actually correspond to perfusion operation. Copyright © 2006 Curtin University of Technology and John Wiley & Sons, Ltd.
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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 | 0.000 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".