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Record W1968217126 · doi:10.1021/ie900906e

CFD Modeling of Metronidazole Degradation in Water by the UV/H<sub>2</sub>O<sub>2</sub> Process in Single and Multilamp Photoreactors

2010· article· en· W1968217126 on OpenAlexafffund
Masroor Mohajerani, Mehrab Mehrvar, Farhad Ein‐Mozaffari

Bibliographic record

VenueIndustrial & Engineering Chemistry Research · 2010
Typearticle
Languageen
FieldEnvironmental Science
TopicAdvanced oxidation water treatment
Canadian institutionsToronto Metropolitan University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsAlkalinityHydrogen peroxideChemistryAqueous solutionDistilled waterDegradation (telecommunications)UltravioletTurbulenceAnalytical Chemistry (journal)ChromatographyMaterials scienceThermodynamicsOrganic chemistry

Abstract

fetched live from OpenAlex

A dynamic model for the degradation of aqueous metronidazole by hydrogen peroxide and ultraviolet irradiation processes (UV/H 2 O 2 ) as an advanced oxidation technology (AOT) in a single lamp tubular photoreactor as well as in a multilamp tubular photoreactor is developed. The model contains the main chemical and photochemical reactions in a medium flowing in the turbulent regime. The optimal hydrogen peroxide concentrations of 75, 150, and 200 mg L −1 were predicted for different alkalinity concentrations of 0, 1.5, and 3 μM, respectively. The model is validated with distilled water as well as alkaline water at different alkalinity concentrations. The model is validated by using the experimental data reported in the open literature. The velocity field and concentration profiles for the turbulent flow using the k −ε model are determined by computational fluid dynamics (CFD).

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)0.000
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 score0.036
Threshold uncertainty score0.072

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0020.000
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 head0.034
GPT teacher head0.273
Teacher spread0.239 · 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

Citations34
Published2010
Admission routes2
Has abstractyes

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