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Numerical Simulation of a UV-PCO Plate Reactor

2019· article· en· W2982056957 on OpenAlexaff
Hao Luo, Guangxin Zhang, Lexuan Zhong, Zaher Hashisho

Bibliographic record

VenueIOP Conference Series Materials Science and Engineering · 2019
Typearticle
Languageen
FieldEnergy
TopicTiO2 Photocatalysis and Solar Cells
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsAirflowChemical kineticsIrradianceComputational fluid dynamicsReaction rateChemistryMass transferKineticsMechanicsKinetic energyTurbulence kinetic energyEnvironmental scienceCatalysisThermodynamicsOpticsPhysicsChromatographyTurbulenceOrganic chemistry

Abstract

fetched live from OpenAlex

Abstract Ultraviolet photocatalytic oxidation (UV-PCO) system has gained increasing attention in indoor air treatments, of which the PCO reaction kinetics mainly depend on the radiation and airflow (contaminants) fields. It is recognized that the airflow rate has a significant influence on the PCO kinetics with the balance of the VOC mass transfer process. However, there are few discussion for the airflow rate dependency in the PCO kinetic reaction model. In addition, most of the studies in the literature assumed the incident surface irradiance was the energy participating in PCO reactions, which is not accurate. Hence, a 3D Computational Fluid Dynamics (CFD) model, including catalyst photon absorption coefficient, the conservation of mass, momentum, energy, and species, as well as PCO reaction kinetics, was introduced in this study. The determined model parameters were verified by the experimental data for a plate reactor challenged with 10 ppm acetone. The airflow rate dependency was examined in conjunction with CFD providing local flow field information, and the catalyst absorbed light intensity was quantified by a validated radiation model. It was found that the PCO removal efficiency predictions from the developed mathematical model agree with experimental data.

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 score0.005
Threshold uncertainty score0.451

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.013
GPT teacher head0.216
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 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

Citations1
Published2019
Admission routes1
Has abstractyes

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