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Record W2328842200 · doi:10.2166/wst.2011.145

Enhanced nutrient removal MBR system with chemical addition for low effluent TP

2011· article· en· W2328842200 on OpenAlexaff
Wenjun Liu, Zhirong Hu, Robert L. Walker, Peter Dold

Bibliographic record

VenueWater Science & Technology · 2011
Typearticle
Languageen
FieldEnvironmental Science
TopicWastewater Treatment and Nitrogen Removal
Canadian institutionsEnviroSim (Canada)
Fundersnot available
KeywordsEffluentActivated sludgeEnvironmental scienceMembrane bioreactorActivated sludge modelPhosphorusEnhanced biological phosphorus removalBioreactorEnvironmental engineeringPrecipitationProcess (computing)ChemistryProcess engineeringWaste managementSewage treatmentEngineeringComputer science

Abstract

fetched live from OpenAlex

A pilot study was conducted to test an membrane bioreactor (MBR) process for combined biological and chemical P removal to achieve a very low effluent total phosphorus (TP) concentration of 0.025 mg P/L. With the data from the pilot test, a simulation study was performed to demonstrate that: (1) the pilot system behaviour (effluent quality, MLSS, etc.) can be modelled accurately with an activated sludge model combined with a chemical precipitation model; and (2) with the calibrated model, simulation scenarios can be performed to further understand the pilot MBR process, and provide information for optimizing design and operation when applied at full-scale. Results from the pilot test indicated that the system could achieve very low effluent TP concentration through biological P removal with a limited chemical addition, and chemical addition to remove P to very low level did not affect other biological processes, i.e., organic and nitrogen removal. Simulation studies indicate that the process behaviour can be modelled accurately with an activated sludge model combined with a chemical precipitation model, and the calibrated model can be used to provide information to optimize system design and operation, e.g., chemical addition control under dynamic loading conditions is important for maintaining biological P removal.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.017
Threshold uncertainty score0.585

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
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 head0.008
GPT teacher head0.188
Teacher spread0.180 · 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.

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
Published2011
Admission routes1
Has abstractyes

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