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Record W2414720588 · doi:10.2175/106143006x105274

Performance of a Submerged Membrane Bioreactor System for Biological Nutrient Removal

2006· article· en· W2414720588 on OpenAlexaff
Javier Mouthon-Bello, Hondge Zhou

Bibliographic record

VenueWater Environment Research · 2006
Typearticle
Languageen
FieldEnvironmental Science
TopicWastewater Treatment and Nitrogen Removal
Canadian institutionsUniversity of Guelph
Fundersnot available
KeywordsAnoxic watersHydraulic retention timeEffluentChemical oxygen demandWastewaterMembrane bioreactorChemistryNitrogenPhosphorusBioreactorPulp and paper industryNutrientSuspended solidsSewage treatmentBiochemical oxygen demandEnvironmental engineeringEnvironmental chemistryEnvironmental science

Abstract

fetched live from OpenAlex

A pilot submerged membrane bioreactor coupled with biological nutrient removal was used to treat the primary effluent at a municipal wastewater treatment plant. Long-term experiments were conducted by varying hydraulic retention time from 6 to 8 hours and solids retention time from 20 to 50 days, respectively. The performance was assessed by monitoring key wastewater parameters, including chemical oxygen demand (COD), nitrogen, and phosphorus concentration in individual anoxic, anaerobic, aerobic, and membrane separation zones. Results showed that the tested system can consistently achieve COD, nitrogen, and phosphorus removal efficiencies at 80 to 98%, 70 to 93%, and 89 to 98%, respectively. Effluent COD remained low as a result of efficient solid retention, even though there was great variation in influent quality. However, total nitrogen increased proportionally with influent concentration. At a 50-day solids retention time, higher COD and nitrogen oxides specific utilization rates in the anoxic zone resulted in a high production of nitrogen oxides in the subsequent aerobic zone.

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)0.000
metaresearch head score (Gemma)0.000
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 score0.003
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0010.001

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 head0.037
GPT teacher head0.252
Teacher spread0.215 · 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

Citations14
Published2006
Admission routes1
Has abstractyes

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