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Record W2116709253 · doi:10.2166/wqrj.2002.025

Flow Rates Through Alum Coagulation and Activated Sludge Flocs

2002· article· en· W2116709253 on OpenAlexaff
Beata Gorczyca, Jerzy J. Ganczarczyk

Bibliographic record

VenueWater Quality Research Journal · 2002
Typearticle
Languageen
FieldEnvironmental Science
TopicUrban Stormwater Management Solutions
Canadian institutionsUniversity of TorontoUniversity of Manitoba
Fundersnot available
KeywordsAlumSettlingPermeability (electromagnetism)CoagulationChemistryActivated sludgeFlocculationVolumetric flow ratePorosityEnvironmental engineeringChemical engineeringSewage treatmentMechanicsEnvironmental scienceMembrane

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.477
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0090.002

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.

Opus teacher head0.213
GPT teacher head0.388
Teacher spread0.175 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; both teacher heads agree on what is shown here.

Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations19
Published2002
Admission routes1
Has abstractyes

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