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Record W2084367585 · doi:10.1080/09593330802214630

RESPIROMETRIC STUDIES ON THE IMPACT OF HUMIC SUBSTANCES ON THE ACTIVATED SLUDGE TREATMENT: MITIGATION OF AN INHIBITORY EFFECT CAUSED BY DIESEL OIL

2008· article· en· W2084367585 on OpenAlexaff
Ewa Lipczyńska‐Kochany, Jan Kochany

Bibliographic record

VenueEnvironmental Technology · 2008
Typearticle
Languageen
FieldEnvironmental Science
TopicAdvanced oxidation water treatment
Canadian institutionsConestoga CollegeEnvironment and Climate Change Canada
Fundersnot available
KeywordsActivated sludgeDiesel fuelWaste managementActivated carbonChemistryOil sludgeEnvironmental scienceEnvironmental chemistryEnvironmental engineeringSewage treatmentEngineeringAdsorptionOrganic chemistry

Abstract

fetched live from OpenAlex

This paper describes the results of aerobic respirometric studies on the application of humic substances (humate) to mitigate an inhibitory effect of petroleum hydrocarbons (diesel oil) on the returned activated sludge (RAS) in sewage from a municipal treatment plant. Initial results of the respirometric tests and non-linear regression analysis showed that diesel oil had an inhibitory effect on the activity of biomass and that kinetic data complied with the Haldane model for inhibitory wastes. Humate addition significantly enhanced the oxygen uptake by RAS. Application of humate at the dose of 2000 mg 1(-1) to the sewage contaminated with 10 mg l(-1) of diesel oil resulted in almost complete recovery of the biomass oxygen uptake. Non-linear regression analysis of the respirometric data indicated that this system complied with the Monod model for non-inhibitory wastes. Thus, the application of humic substances to mitigate the inhibitory effects of oil spills in wastewater treatment plants seems to be an attractive alternative to the treatments using activated carbon or specialized sorbents.

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 score0.005
Threshold uncertainty score0.601

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 head0.017
GPT teacher head0.265
Teacher spread0.248 · 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

Citations8
Published2008
Admission routes1
Has abstractyes

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