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Record W2004229671 · doi:10.2166/wst.2007.640

Model-based optimisation of the biological performance of a sidestream MBR

2007· article· en· W2004229671 on OpenAlexaff
Ingmar Nopens, Gürkan Sin, Tao Jiang, Luca d’Antonio, S. Stama, Jie Zhao, Peter A. Vanrolleghem

Bibliographic record

VenueWater Science & Technology · 2007
Typearticle
Languageen
FieldEnvironmental Science
TopicWastewater Treatment and Nitrogen Removal
Canadian institutionsUniversité Laval
FundersUniversiteit Gent
KeywordsEnvironmental sciencePhosphorusEffluentResidence time (fluid dynamics)NitrateChemistryEnvironmental engineeringPhosphateAmmoniumPulp and paper industryEngineering

Abstract

fetched live from OpenAlex

A model-based optimisation of the operation in view of the biological performance in terms of nitrogen (N) and phosphorus (P) removal of a pilot-scale side-stream MBR has been performed by means of a two-tier scenario analysis. The methodology uses two different scenario analyses to simulate the effect of three degrees of freedom in the MBR system: (1) DO set-point in the aerobic reactor, (2) sludge residence time and (3) internal recirculation rate. The scenarios are simulated using a calibrated ASM2d MBR model. Effluent quality, in terms of nitrate, ammonia and phosphate, is used to select the best scenario. It proved to be a compromise between nitrogen and phosphorus removal as these are linked. A 42% reduction in ammonium and a 32% reduction in nitrate concentration were achieved. Phosphate removal is partly sacrificed (39% increase) compared to the standard operation.

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)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.007
Threshold uncertainty score0.014

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.015
GPT teacher head0.218
Teacher spread0.204 · 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 designSimulation or modeling
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

Citations20
Published2007
Admission routes1
Has abstractyes

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