Flow Rates Through Alum Coagulation and Activated Sludge Flocs
Bibliographic record
Abstract
Abstract The liquid velocity inside alum and activated sludge flocs was calculated using the size, settling velocity and permeability of activated sludge flocs. The permeability of activated sludge flocs has been determined experimentally. The permeability of alum coagulation flocs was assumed to be half of the permeability of activated sludge flocs based on the size of the pores in these flocs. The average flow velocity inside an activated sludge floc was calculated to be 1575 µm/s, which is in the range of the flow experimentally measured inside biofilms at a distance of about 100 µm from the substratum by Beer et al. (1995). The flow inside an alum coagulation floc was calculated to be 318 µm/s. The flow velocity inside the same flocs estimated with Davies permeability model were 0.7 µm/s for activated sludge flocs and 20 µm/s for alum coagulation flocs. Therefore, the flow velocities estimated on the basis of experimentally determined permeability were much higher than the velocities calculated with Davies permeability model. Davies permeability model assumes homogeneous distribution of porosity inside an aggregate. Direct observations made during the analysis of floc sections have proven this assumption to be wrong. Flocs have fractal structure and the models predicting their permeability should be based on this feature. Flow rates through alum and activated sludge flocs predicted on the basis of the fractal model of a floc compared well with experimental 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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".