Liquid residence time distribution in a two‐compartment wastewater treatment bioreactor
Bibliographic record
Abstract
Residence time distribution (RTD) of liquid phase in a two‐compartment (packed‐bed/packed‐bed or packed‐bed/packing‐free) bioreactor for wastewater treatment was evaluated via a complex liquid flow structure. RTD in packed‐bed compartments was modelled using a modified axial dispersion‐exchange model with a liquid stream which bypasses the dynamic liquid region. The liquid in the dynamic region is dispersed in axial and transverse directions, whereas the stagnant region exchanges mass with the dynamic region, the two regions being in direct contact. RTD in packing‐free compartment was modelled as a continuous stirred‐tank reactor with ideal or non‐ideal mixing zone. Time‐domain analysis coupled with the phenomenological RTD model was used to identify the model parameters. The proposed RTD model offers the opportunity to quantify parameters as dynamic liquid fraction, dynamic liquid flow rate fraction, number of transfer units between dynamic and stagnant liquid regions, stagnant liquid holdup, which otherwise would have been difficult to estimate using other methods and which can affect the bioreactor performance. The proposed RTD model describes adequately the hydrodynamics of the two‐compartment packed‐bed bioreactor while the simulations unveil likely tendency of the RTD curves subject to different sets of model parameters.
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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 | 0.000 |
| Research integrity | 0.001 | 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".