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Record W2332664793 · doi:10.2166/wst.2011.053

Ozonation of endogenous residue and active biomass from a synthetic activated sludge

2011· article· en· W2332664793 on OpenAlexafffund
Marc‐André Labelle, Anas Ramdani, S. Déléris, Alain Gadbois, Peter Dold, Yves Comeau

Bibliographic record

VenueWater Science & Technology · 2011
Typearticle
Languageen
FieldEnvironmental Science
TopicWastewater Treatment and Nitrogen Removal
Canadian institutionsEnviroSim (Canada)Polytechnique Montréal
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsBiodegradationChemistryActivated sludgeOzoneLysisResidue (chemistry)SolubilizationBiomass (ecology)Pulp and paper industryEnvironmental chemistryWaste managementSewage treatmentOrganic chemistryBiochemistryAgronomyBiology

Abstract

fetched live from OpenAlex

Coupling the activated sludge and the ozonation processes is an efficient, although expensive, solution for sludge reduction. A better knowledge of the mechanisms involved in the degradation of various sludge fractions by ozone is needed to optimize the coupled process. The objectives of this study were to determine the biodegradability of ozone-solubilized endogenous residue, the action of ozone on the active biomass and the solubilization yield of these two main sludge fractions. Batch tests were conducted with slug input of ozone stock solution into fresh or aerobically digested synthetic sludge. Biodegradability of the solubilized endogenous residue was increased by ozonation by up to 0.27 g BOD5/g CODi. Ozone caused biomass lysis, as opposed to an increase in maintenance needs, as shown by a correlation between the decrease in microbial activity and viability. Lysis caused by ozonation was associated with a solubilization of 20% of the lyzed cell COD mass. Solubilization yields were of 9.6 and of 1.9 to 3.6 g COD/g O3 for fresh and aerobically digested sludge, respectively. Design of sludge ozonation processes should account for the variability between the solubilization yield and biodegradability of the various sludge fractions.

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)0.000
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 score0.006
Threshold uncertainty score0.670

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.002
Scholarly communication0.0000.000
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 head0.021
GPT teacher head0.201
Teacher spread0.181 · 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

Citations10
Published2011
Admission routes2
Has abstractyes

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