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Record W2031119151 · doi:10.2166/wst.2011.026

Evaluation of a new model for the reduction of excess sludge production by ozonation of return activated sludge: what solids COD fraction is affected?

2010· article· en· W2031119151 on OpenAlexaff
Dominic Frigon, Siavash Isazadeh

Bibliographic record

VenueWater Science & Technology · 2010
Typearticle
Languageen
FieldEnvironmental Science
TopicWastewater Treatment and Nitrogen Removal
Canadian institutionsMcGill University
Fundersnot available
KeywordsActivated sludge modelActivated sludgeNitrificationBiomass (ecology)ChemistryOzoneEffluentHeterotrophPulp and paper industryVolatile suspended solidsMixed liquor suspended solidsEnvironmental engineeringEnvironmental chemistryEnvironmental scienceSewage treatmentNitrogenEcologyBacteriaBiology

Abstract

fetched live from OpenAlex

This paper aims at clarifying the effect of ozone on the RAS solids to model activated sludge systems equipped with RAS-ozonation processes for the reduction of sludge production. A common hypothesis is that ozone only affects active biomass by promoting cryptic growth. Data from a pilot-scale study were used to test this and two other model extensions to IWA-ASM3. All model extensions were able to simulate the observed linear reduction in sludge production with increasing ozone dose when the MLVSS are kept constant. However, model simulations showed the inconsistency of the cryptic growth hypothesis with the extent of sludge reduction. The second tested model extensions assumes that ozone affects all the solids fractions (active biomass, endogenous residue, and influent inert particulate COD) equally. This extension could properly simulate the observed sludge reduction, but it failed to predict the trends in effluent BOD₅, ATP/VSS, and nitrification rates. A third tested model extension, which performed better, assumes that biomass is inactivated at a specific rate higher than the specific rate of transformation by ozone of the other solids fractions. Finally, the predictions from this model extension were most accurate if either (i) the nitrifiers were inactivated at a lower rate then heterotrophs, (ii) the nitrifiers model parameters (e.g., maximum growth rate) were changed under ozone (i.e., metabolic adaptation, (iii) or both.

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.049
Threshold uncertainty score0.368

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.023
GPT teacher head0.275
Teacher spread0.252 · 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; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations12
Published2010
Admission routes1
Has abstractyes

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