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Record W1968191491 · doi:10.2166/wst.2012.018

Radial distribution modeling of liquid-phase phenol concentration in a liquid–solid fluidized bed photoreactor

2012· article· en· W1968191491 on OpenAlexaff
Shuangshi Dong, Dandan Zhou, Xiaotao Bi

Bibliographic record

VenueWater Science & Technology · 2012
Typearticle
Languageen
FieldEngineering
TopicInnovative Microfluidic and Catalytic Techniques Innovation
Canadian institutionsUniversity of British Columbia
FundersNational Natural Science Foundation of China
KeywordsMass transferFluidized bedPhenolVolumetric flow rateChemistryChromatographyPhotocatalysisPenetration (warfare)RADIUSTitanium dioxideMass transfer coefficientCarbon dioxideAnalytical Chemistry (journal)Materials scienceThermodynamicsCatalysisComposite materialOrganic chemistry

Abstract

fetched live from OpenAlex

A fluidized bed photoreactor with titanium dioxide-immobilized spherical activated carbon particles was examined. The light intensity profile was modeled using the Lambert-Beer rule for the modeling of the radial distribution of liquid-phase phenol concentration in the fluidized bed photoreactor, when considering the reactor composed of numerous differential annular drums and no mass transfer between drums. The model could be well matched with the experimental data which indicated the liquid flow rate of 13.8 L/min was the optimum in the balance of flow rate-related light penetration and photocatalyst concentration. By integration of liquid-phase phenol concentration along the radius, photocatalytic oxidation performance of the photoreactor was evaluated in comparison with the experimental data and model prediction. The results showed that the errors were less than 30% for most of the predictions. It is suggested that mass transfer and flow rate difference along the radial direction should be considered to obtain more precise prediction.

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 score0.263
Threshold uncertainty score0.567

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
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 head0.014
GPT teacher head0.269
Teacher spread0.255 · 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

Citations3
Published2012
Admission routes1
Has abstractyes

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