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Record W2107484168 · doi:10.2166/wqrjc.2011.028

A simulation of the fate of nitrogen in an on-site wastewater treatment system

2011· article· en· W2107484168 on OpenAlexafffund
Jean Bernier, Paul Lessard, Martin Girard

Bibliographic record

VenueWater Quality Research Journal · 2011
Typearticle
Languageen
FieldEnvironmental Science
TopicWastewater Treatment and Nitrogen Removal
Canadian institutionsUniversité Laval
FundersNatural Sciences and Engineering Research Council of CanadaFonds Québécois de la Recherche sur la Nature et les Technologies
KeywordsEffluentWastewaterEnvironmental scienceDenitrificationAerationSewage treatmentEnvironmental engineeringChemical oxygen demandSuspended solidsWaste managementNitrogenEngineeringChemistry

Abstract

fetched live from OpenAlex

An experiment to study and to build a mathematical model to reproduce the behavior of nitrogen in a residential wastewater treatment process was performed. A pilot unit receiving grey and black water was used. The pilot consisted in a septic tank followed by a fixed-film, partly aerated bioreactor with effluent recirculation operated under two different scenarios: normal operating conditions and increased influent flow. Modeling was performed with the GPS-X™ software. Following a sensitivity analysis, the model was calibrated by comparing results from the pilot experiment and those of the simulation predictions. Obtained results show that the studied pilot unit is able to eliminate most of the ammonia contained in the influent, except for days with exceptionally high influent concentrations. Chemical oxygen demand (COD) and total suspended solids (TSS) reduction is also near complete and a partial denitrification is present. The calibrated model shows a good agreement with results obtained in the pilot unit, although work remains to be done for nitrates. Modeling of residential wastewater treatment appears to be a useful tool for understanding, optimizing and predicting the expected performances of such a system.

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)0.002
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.684
Threshold uncertainty score0.461

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
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 head0.168
GPT teacher head0.363
Teacher spread0.195 · 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

Citations0
Published2011
Admission routes2
Has abstractyes

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