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Record W2071462291 · doi:10.1139/s06-024

Treatment kinetics of aircraft deicing fluid in an anaerobic baffled reactor

2007· article· en· W2071462291 on OpenAlexvenueno aff
Kevin J. Kennedy, Micha Barriault

Bibliographic record

VenueJournal of Environmental Engineering and Science · 2007
Typearticle
Languageen
FieldEnvironmental Science
TopicWastewater Treatment and Nitrogen Removal
Canadian institutionsnot available
Fundersnot available
KeywordsAcidogenesisHydraulic retention timeChemical oxygen demandAnaerobic exerciseChemistryWastewaterPulp and paper industryBaffleBioreactorEnvironmental scienceWaste managementAnaerobic digestionEnvironmental engineeringChemical engineeringMethaneBiology

Abstract

fetched live from OpenAlex

A four compartment anaerobic baffled reactor (ABR) operated with and without recycle treated aircraft deicing wastewater. Without recycle a minimum hydraulic retention time (HRT) of 27 h (organic loading rate, OLR, of 6.2 g·L–1·d–1) with a chemical oxygen demand (COD) removal efficiency of 89% and a Uoverall of 0.25 g·g–1·d–1 was achieved. With recycle the minimum HRT was 17 h (OLR of 9.9 g·L–1·d–1) with 93% COD removal efficiency, achieved at a Uoverall of 0.32 g·g–1·d–1. Anaerobic baffled reactor compartments served to naturally separate acidogenic and methanogenic activities longitudinally through the reactor, with acidogenic activity highest in compartment 1. A first order compartment model for substrate removal could not describe ABR performance. The rate coefficients (k1–k4) or kavg were inconsistent and no predictive correlation of k values with substrate concentration, HRT or OLR could be achieved in describing the naturally attenuated two-phase phenomenon.Key words: anaerobic, baffled reactor, deicing fluid, ethylene glycol, treatment.

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 score0.418
Threshold uncertainty score0.422

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
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 head0.007
GPT teacher head0.203
Teacher spread0.196 · 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

Citations16
Published2007
Admission routes1
Has abstractyes

Explore more

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