Understanding the Mixing Pattern in an Anaerobic Expanded Granular Sludge Bed Reactor: Effect of Liquid Recirculation
Bibliographic record
Abstract
An anaerobic expanded granular sludge bed (EGSB) reactor is considered to be an improvement over upflow anaerobic sludge blanket reactors owing to the former’s ability to recycle the effluent and its modified reactor geometry. However, the mixing pattern in EGSB reactors, which greatly influences the design and the performance of this reactor, has not yet been studied in detail. In this research, the mixing pattern in a lab-scale EGSB reactor treating a synthetic dye wastewater was studied using lithium chloride as a tracer. The tracer exit curve indicated a complete-mix behavior. A simulation study was conducted on identical reactors using conductivity probes, inserted through the sample ports along the height of the reactors and connected to a data acquisition system. The reactors were operated at three different hydraulic retention times (3.3, 5.5, and 9 h) and at four different upflow liquid velocities (1.10, 2.66, 5.33, and 8.68 m/h). The data showed the existence of a plug-flow regime in the basin at lower upflow liquid velocities although the tracer response curves resemble complete-mix behavior. With increasing upflow liquid velocity the flow pattern in the basin deviates from a plug-flow pattern and approaches a complete-mix condition. The EGSB reactor can be modeled as a plug-flow reactor with recycle and dead space, and with a large vessel dispersion number (D/uL>0.01) .
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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 | 0.001 |
| 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".