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Record W2573335065 · doi:10.1021/acs.iecr.6b04165

Effect of Beaded Activated Carbon Fluidization on Adsorption of Volatile Organic Compounds

2017· article· en· W2573335065 on OpenAlexafffund
Samineh Kamravaei, Pooya Shariaty, Masoud Jahandar Lashaki, John D. Atkinson, Zaher Hashisho, John H. Phillips, James E. Anderson, Mark E. Nichols

Bibliographic record

VenueIndustrial & Engineering Chemistry Research · 2017
Typearticle
Languageen
FieldEnvironmental Science
TopicMinerals Flotation and Separation Techniques
Canadian institutionsUniversity of Alberta
FundersNatural Sciences and Engineering Research Council of CanadaFord Motor Company
KeywordsActivated carbonAdsorptionFluidizationChemistryChemical engineeringCarbon fibersChromatographyOrganic chemistryMaterials scienceFluidized bedComposite material

Abstract

fetched live from OpenAlex

This research investigates the effect of adsorbent bed configuration on volatile organic compounds (VOCs) adsorption with beaded activated carbon (BAC). Five-cycle adsorption/desorption tests using a single VOC (1,2,4-trimethylbenzene) and a mixture of nine VOCs were completed using fixed and fluidized bed adsorber configurations. Adsorption tests were completed with full loading of the adsorbent. All regeneration tests were completed in the fixed bed arrangement. The adsorption capacity of the BAC was not affected by the adsorption bed’s configuration in case of reaching full breakthrough. For the VOC mixture, however, 30% less heel buildup was observed for the fluidized bed configuration. Higher accumulation of heavy adsorbates was found in fixed bed as opposed to fluidized bed, explaining the higher heel buildup in the former. On the other hand, improved mass transfer was found across the entire fluidized bed as a result of better gas-particle contact. These results show that, besides the expected engineering advantages of a fluidized bed adsorption system (e.g., lower pressure drop), decreased heel buildup is an additional advantage when using fluidized bed adsorption in industrial settings.

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)0.001
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 score0.007
Threshold uncertainty score0.506

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
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)0.0000.000

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 head0.053
GPT teacher head0.341
Teacher spread0.288 · 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

Citations46
Published2017
Admission routes2
Has abstractyes

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