Fluidization in an Anaerobic EGSB Reactor: Analysis of Primary Wakes and Modeling of Sludge Blanket
Bibliographic record
Abstract
Fluidization of biogranules in an anaerobic expanded granular sludge bed (EGSB) reactor is stochastic in nature and it is a function of the size distribution and the frequency of generation of flow-through gas bubbles in the reactor. Other factors that contribute to the distribution of granules along the height of the reactor are the settling characteristics of granules and the fluid velocity. A simulation was conducted in a test column to obtain a relationship between the flow-through gas and granules at different heights along the column. This relationship was combined with the pattern of gas flow through an identical EGSB reactor to create a model to predict the concentration of granules at different heights along the reactor. The model can predict well the stochastic nature of the axial distribution of granules but underestimates the number of granules at different heights. The reasons for such deviations are explained. The pattern of granule shedding from the primary wake associated with spherical cap bubbles and terminal velocities of bubbles have also been studied and modeled to estimate the maximum height of ascent of granules under isolated spherical cap bubbles. The results of this model agreed well with the experimental observations.
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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 | 0.001 |
| 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 | 0.001 | 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".