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Record W111153507 · doi:10.2175/106143005x82226

Simultaneous Nitrification and Denitrification with Anoxic Phosphorus Uptake in a Membrane Bioreactor System

2006· article· en· W111153507 on OpenAlexafffund
Jignesh Patel, George Nakhla

Bibliographic record

VenueWater Environment Research · 2006
Typearticle
Languageen
FieldEnvironmental Science
TopicWastewater Treatment and Nitrogen Removal
Canadian institutionsWestern University
FundersCanada Foundation for Innovation
KeywordsKjeldahl methodWastewaterEffluentAnoxic watersNitrificationMembrane bioreactorBiochemical oxygen demandHydraulic retention timeChemical oxygen demandChemistryDenitrificationPhosphorusPulp and paper industrySewage treatmentEnvironmental engineeringNitrogenEnvironmental chemistryEnvironmental science

Abstract

fetched live from OpenAlex

The performance of an innovative membrane bioreactor (MBR) process using anoxic phosphorus uptake with nitrification and denitrification for the treatment of municipal wastewater with respect to operational performance and effluent quality is addressed in this paper. The system was operated at steady-state conditions with a synthetic acetate-based wastewater at a hydraulic retention time (HRT) of 12 hours and on degritted municipal wastewater at a total system HRT of 6 hours. The MBR system was able to achieve 99% biochemical oxygen demand (BOD), chemical oxygen demand (COD), and ammonia-nitrogen (NH4(+)-N); 98% total Kjeldahl nitrogen (TKN); and 97% phosphorus removal, producing effluent BOD, COD, NH4+-N, TKN, nitrate-nitrogen, nitrite-nitrogen, and phosphate-phosphorus of <3, 14, 0.2, 0.26, 5.8, 0.21, and <0.01 mg/L, respectively, at the 6-hour HRT. The comparison of the synthetic and municipal wastewater run is presented in this paper. Steady-state mass balance on municipal wastewater was performed to reveal some key features of the modified MBR 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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
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 score0.037
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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)0.0000.001

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 head0.015
GPT teacher head0.229
Teacher spread0.214 · 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.

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

Citations7
Published2006
Admission routes2
Has abstractyes

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