Multivariable Nonlinear Control of Biomass and Metabolite Concentrations in a High-Cell-Density Perfusion Bioreactor
Bibliographic record
Abstract
This paper presents the development of a multivariable nonlinear adaptive controller for perfusion bioreactors, and its simulated behavior on a model that has been identified from experimental data. A contribution to the bioprocess model is also proposed, which is supported by experimental observations. The proposed control strategy is a multivariable approach to regulate the biomass and substrate concentrations using the fresh medium addition and direct bleeding streams as the manipulated variables. Level control would be ensured by a proportional integral (PI) control loop, using either the perfusion flow (draining flow that retains the cells in the reactor) or a nutrient-free phosphate-buffered saline (PBS) solution flow added to the reactor. The flow that is used for level control determines the operation mode of the reactor, being perfusion or chemostat. This paper presents the controller design, switching considerations between the operation modes, and parameter tuning guidelines. The controller tunings are essentially obtained by pole placement, based on linearization of the closed-loop dynamics. Simulation results prove the technique to be rather efficient, while the transitions between the operation modes are smooth and without risks.
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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 | 0.000 | 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 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".