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Record W2009821643 · doi:10.2166/wst.2007.226

Strategies for minimizing deflocculation of biosolids due to oxygen disturbances

2007· article· en· W2009821643 on OpenAlexafffund
Y. Zhang, D. Grant Allen

Bibliographic record

VenueWater Science & Technology · 2007
Typearticle
Languageen
FieldEnvironmental Science
TopicWastewater Treatment and Nitrogen Removal
Canadian institutionsUniversity of Toronto
FundersNatural Sciences and Engineering Research Council of CanadaUniversity of Toronto
KeywordsBiosolidsEffluentPulp and paper industryChemistryTurbidityKraft processKraft paperTetraethylammonium chlorideChlorideEnvironmental chemistryEnvironmental engineeringEnvironmental sciencePotassiumEcologyBiologyOrganic chemistry

Abstract

fetched live from OpenAlex

The objective of this paper is to identify deflocculation under short-term disturbances of dissolved oxygen (DO) in a continuous system, and to explore the strategies for minimizing the carry-over of biosolids. Sludge deflocculation was examined in four parallel sequencing batch reactors (SBRs) fed with primary treated effluents from a bleached kraft pulp and paper mill. Results show that the DO transients caused a reduction of soluble COD (SCOD) removal efficiency by 50-70%, increases in suspended solid (SS) concentration and turbidity by more than 100%. Under the DO disturbances, the ratio of bulk Ca2+/K+ decreased by 10% in the treated effluents. All these changes were reversible within 24 hrs after the DO concentrations were restored up to 4 mg/L, indicating a physicochemical response of microorganisms to the DO transients. The sludge deflocculation is correlated with the decreasing ratio of Ca2+ to K+ in the extracellular environment. Addition of calcium chloride, tetraethylammonium chloride or glibenclamide promotes the formation of bioflocs bigger than 12.5 microm, but the deflocculation of biosolids under the DO transients wasn't completely prevented.

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 score0.028
Threshold uncertainty score0.311

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 studies0.0000.001
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 head0.012
GPT teacher head0.250
Teacher spread0.238 · 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

Citations2
Published2007
Admission routes2
Has abstractyes

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