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Record W1995159338 · doi:10.2166/aqua.2008.038

Application of a multiphase CFD modelling approach to improve ozone residual monitoring and tracer testing strategies for full-scale drinking water ozone disinfection processes

2008· article· en· W1995159338 on OpenAlexaffabout
Jianping Zhang, Peter M. Huck, G. D. Stubley, William B. Anderson

Bibliographic record

VenueJournal of Water Supply Research and Technology—AQUA · 2008
Typearticle
Languageen
FieldEnvironmental Science
TopicWater Treatment and Disinfection
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsOzoneTRACEREnvironmental scienceResidualFull scaleComputational fluid dynamicsScale (ratio)Waste managementProcess engineeringEngineeringComputer scienceMeteorologyAerospace engineering

Abstract

fetched live from OpenAlex

A multiphase computational fluid dynamics (CFD) model has been developed to address the major components of ozone disinfection processes: contactor hydraulics, ozone decay and mass transfer. The model was applied to simulate ozone profiles and tracer residence time distributions of ozone contactors at the DesBaillets Water Treatment Plant (WTP) in Montreal, Canada. The modelling results showed that ozone residuals at the cross-section of the outlet of each chamber in the ozone contactors were very sensitive to monitoring point selection. The optimum locations were significantly affected by multiple operational parameters including water/gas flow rates, ozone dosage and baffling conditions. The modelling results suggested that multiple monitoring points should be used to obtain more representative ozone residuals. The CFD model was also used to study the factors affecting tracer residence time distribution (RTD). It was observed that the method of tracer injection could slightly affect tracer RTD results while sampling location had a significant influence on tracer RTD prediction or measurement. Therefore, it is suggested that multiple sampling points should be employed during tracer tests if possible.

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 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 score0.208
Threshold uncertainty score0.386

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 communication0.0000.001
Open science0.0000.000
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 head0.044
GPT teacher head0.288
Teacher spread0.244 · 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

Citations11
Published2008
Admission routes2
Has abstractyes

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