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Record W2116178465 · doi:10.1002/aic.12251

Performance evaluation of UV reactor using optical diagnostic techniques

2010· article· en· W2116178465 on OpenAlexaff
Siamak Elyasi, Fariborz Taghipour

Bibliographic record

VenueAIChE Journal · 2010
Typearticle
Languageen
FieldEnvironmental Science
TopicAdvanced oxidation water treatment
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsFluenceInletMaterials scienceFluorescenceProcess engineeringLaserNuclear engineeringChemistryAnalytical Chemistry (journal)Biological systemOpticsChromatographyMechanical engineeringEngineeringPhysics

Abstract

fetched live from OpenAlex

Abstract Conventionally, the performance of a UV reactor is evaluated using the concentration of photoreactive chemicals at the outlet vs. the inlet. This research presents a novel method for measuring the concentration distribution of a photoreactive chemical inside a photoreactor using a modified planar laser‐induced fluorescence method. The fluence distribution was measured for a pilot scale photoreactor under different operating conditions. The visualized result of the fluence distribution revealed significant information about the local/overall performance of the photoreactor. This method is a powerful diagnostic tool for the determination of the local performance inside a UV reactor, as well as for the evaluation of models simulating UV reactor behavior. © 2010 American Institute of Chemical Engineers AIChE J, 2011

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 categoriesInsufficient payload (model declined to judge)
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 score0.147
Threshold uncertainty score1.000

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 communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.026
GPT teacher head0.304
Teacher spread0.278 · 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.

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

Citations10
Published2010
Admission routes1
Has abstractyes

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