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Record W2064913962 · doi:10.1080/09593332608618478

Design Strategy for a Simultaneous Nitrification/Denitrification of a Slaughterhouse Wastewater in a Sequencing Batch Reactor: ASM2D Modeling and Verification

2005· article· en· W2064913962 on OpenAlex
Youssef Filali-Meknassi, M Auriol, R. D. Tyagi, Yves Comeau, Rao Y. Surampalli

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueEnvironmental Technology · 2005
Typearticle
Languageen
FieldEnvironmental Science
TopicWastewater Treatment and Nitrogen Removal
Canadian institutionsInstitut National de la Recherche Scientifique
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsSequencing batch reactorDenitrificationWastewaterNitrificationActivated sludge modelActivated sludgeChemical oxygen demandWaste managementSimultaneous nitrification-denitrificationChemistryPulp and paper industryOrganic matterEnvironmental scienceNitrogenEnvironmental engineeringEngineering

Abstract

fetched live from OpenAlex

Sequencing Batch Reactor (SBR) was used to treat slaughterhouse wastewater which contains average Chemical Oxygen Demand (COD) concentration of 5000 mg l(-1) and ammonium of 360 mgN l(-1). Nitrification/denitrification process was conducted for nitrogen removal. The influent wastewater as internal carbon source and sodium acetate as an external one was used for completing denitrification to achieve the simultaneous organic matter removal (95-96%) and nitrogen removal (95-97%). In addition, the dynamic SBR simulation model for biological nitrogen removal based on the Activated Sludge Model No. 2d (ASM2d) and GPS-X software is presented. The experimental study for the calibration and validation of the model was carried out using laboratory SBR. The study showed that the model provides a powerful tool to reduce the experimental expenditure and time to find the optimum strategy.

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.

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 score0.218
Threshold uncertainty score0.762

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 head0.025
GPT teacher head0.230
Teacher spread0.205 · 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