Dynamic modeling and optimal fed-batch feeding strategies for a two-phase partitioning bioreactor
Bibliographic record
Abstract
A dynamic model for the degradation of phenol in a two-phase partitioning bioreactor has been developed based on mechanistic balances around the bioreactor. The key process characteristics including substrate transfer between the organic and aqueous phases, substrate inhibition, oxygen limitation, and cell entrainment were incorporated into the model. The model predictions were validated against existing experimental data obtained for a 2-L bioreactor, and good correlation was observed for the time frames of the simulations, as well as for trends in cell and substrate concentrations. Optimal fed-batch, phenol feeding strategies were then developed based on two approaches: (1) maximization of phenol consumption in a fixed time interval and (2) consumption of a fixed amount of phenol in minimal time. The optimal feeding policies, determined using the Iterative Dynamic Programming algorithm, provided substantial improvements in the amount of phenol consumed when compared to a typical experimental heuristic approach. For example, 45.73 g of phenol was predicted to be consumed in 50 h (not including lag phase) using the optimal feeding profile compared to 10.26 g of phenol consumed in the simulated experimental approach. Oxygen limitation was predicted to be a recurring operational challenge in the partitioning bioreactor, and had a strong impact on the optimization results.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".