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Record W2803607456 · doi:10.2166/wst.2001.0145

Modelling biological phosphorus removal from a cheese factory effluent by an SBR

2001· article· en· W2803607456 on OpenAlexaff
R. C. Ky, Yves Comeau, Maxime Perrier, I. Takács

Bibliographic record

VenueWater Science & Technology · 2001
Typearticle
Languageen
FieldEnvironmental Science
TopicWastewater Treatment and Nitrogen Removal
Canadian institutionsHydromantis Environmental Software Solutions (Canada)Polytechnique Montréal
Fundersnot available
KeywordsEffluentSequencing batch reactorPhosphorusFactory (object-oriented programming)Activated sludge modelEnhanced biological phosphorus removalCalibrationEnvironmental scienceProcess engineeringPulp and paper industryEnvironmental engineeringWaste managementBiochemical engineeringChemistryEngineeringMathematicsActivated sludgeComputer scienceSewage treatmentStatistics

Abstract

fetched live from OpenAlex

A mathematical model, named A3DX, based on ASM3(A3) for C and N removal, on the bio-P metabolic model of the Technological University of Delft (D), and on extra processes (X) for chemical and biological phosphorus removal, was developed and used to simulate the treatment of a fermented cheese factory effluent by a sequencing batch reactor (SBR). Experimental data obtained from a pilot-scale SBR were used to calibrate the model. The model calibration was performed by changing a minimal number (four) of default values for parameters, and by introducing a Monod function to account for magnesium limitation. This study suggests that the value of stoichiometric and kinetic model parameters determined with municipal effluents or enriched bio-P cultures can be reasonably used with some agro-industrial effluents with minimal parameter adjustment for calibration.

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 score0.033
Threshold uncertainty score0.065

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)0.0010.000

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 head0.019
GPT teacher head0.224
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

Citations12
Published2001
Admission routes1
Has abstractyes

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