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Record W2170637329 · doi:10.1002/aic.14952

Impact of the fluid flow conditions on the formation rate of carbon dioxide hydrates in a semi‐batch stirred tank reactor

2015· article· en· W2170637329 on OpenAlexaff
Sélim Douïeb, Louis Fradette, François Bertrand, Benoı̂t Haut

Bibliographic record

VenueAIChE Journal · 2015
Typearticle
Languageen
FieldEnvironmental Science
TopicMethane Hydrates and Related Phenomena
Canadian institutionsPolytechnique Montréal
Fundersnot available
KeywordsImpellerContinuous stirred-tank reactorVolumetric flow rateLimitingRotational speedMechanicsHydrateTurbineFlow (mathematics)ThermodynamicsHeat transferChemistryFluid dynamicsCarbon dioxideMaterials scienceChemical engineeringMechanical engineeringEngineeringPhysicsOrganic chemistry

Abstract

fetched live from OpenAlex

CO 2 hydrate formation experiments are performed in a 20 L semi‐batch stirred tank reactor using three different impellers (a down‐pumping pitched blade turbine, a Maxblend™, and a Dispersimax™) at various rotational speeds to examine the impact of the flow conditions on the CO 2 hydrate formation rate. An original mathematical model of the CO 2 hydrate formation process that assigns a resistance to each of its constitutive steps is established. For each experimental condition, the formation rate is measured and the rate‐limiting step is determined on the basis of the respective values of the resistances. The efficiencies of the three considered impellers are compared and, for each impeller, the influence of the rotational speed on the rate‐limiting step is discussed. For instance, it is shown that a formation rate limitation due to heat transfer can occur at the relatively small scale used to perform our experiments. © 2015 American Institute of Chemical Engineers AIChE J , 61: 4387–4401, 2015

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.524
Threshold uncertainty score0.307

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)0.0000.000

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 head0.020
GPT teacher head0.252
Teacher spread0.232 · 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

Citations28
Published2015
Admission routes1
Has abstractyes

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