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Record W1996397679 · doi:10.1139/s08-014

Development, laboratory and pilot testing of an innovative single sludge step-feed anoxic–aerobic nitrogen removal process

2008· article· en· W1996397679 on OpenAlexaffvenue
Aotian Xu, Stephanie Young, Yanqiu Zhang

Bibliographic record

VenueJournal of Environmental Engineering and Science · 2008
Typearticle
Languageen
FieldEnvironmental Science
TopicWastewater Treatment and Nitrogen Removal
Canadian institutionsUniversity of Regina
Fundersnot available
KeywordsNitrificationAnoxic watersNitrifying bacteriaHydraulic retention timeWastewaterPulp and paper industryActivated sludgeNitrogenDenitrificationSewage treatmentPilot plantEnvironmental engineeringEnvironmental scienceEnvironmental chemistryChemistryWaste managementEngineering

Abstract

fetched live from OpenAlex

A single-sludge step-feed anoxic–aerobic process (SSFAOP), characterized by enhanced nitrogen reduction performance, long solids retention time (SRT), and short aerobic hydraulic retention time (HRT), was designed to optimize nitrification and denitrification processes for ammonia and nitrogen removal. The favorable conditions for nitrifying bacteria were provided by using influent flow splitting and step feeding. The relative predominance of nitrifying bacteria resulted in an increase of nitrification rate and decrease of nitrification time. In addition, the SSFAOP process design and operation were optimized by computer simulation and laboratory testing. The optimum operating parameters were determined through laboratory and pilot testing by evaluating the effects of temperature, pH, and dissolved oxygen (DO) on ammonia reduction efficiencies and treatment performance. The laboratory and pilot testing results demonstrated that the SSFAOP was a cost-effective process for nitrogen reduction. It is, therefore, feasible to implement industrial full-scale applications in wastewater treatment plants.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.002
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
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 head0.016
GPT teacher head0.200
Teacher spread0.183 · 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 source (direct Gemma or distilled Codex), 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

Citations1
Published2008
Admission routes2
Has abstractyes

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