MétaCan
Menu
Back to cohort
Record W1994433578 · doi:10.1021/ie061048+

Gas Hydrate Growth Model in a Semibatch Stirred Tank Reactor

2007· article· en· W1994433578 on OpenAlexafffund
Shahrzad Hashemi, Arturo Macchi, Phillip Servio

Bibliographic record

VenueIndustrial & Engineering Chemistry Research · 2007
Typearticle
Languageen
FieldEnvironmental Science
TopicMethane Hydrates and Related Phenomena
Canadian institutionsMcGill UniversityUniversity of Ottawa
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsContinuous stirred-tank reactorHydrateChemical engineeringChemistryMaterials scienceProcess engineeringEngineeringOrganic chemistry

Abstract

fetched live from OpenAlex

The gas hydrate growth model of Englezos et al. ( Chem. Eng. Sci. 1987, 42, 2647) was modified based on a concentration driving force where the equilibrium concentration at the hydrate surface is determined at the surface pressure and temperature, with the latter varying between the bulk and three-phase equilibrium temperature depending on the rate of heat removal. In order to study hydrate growth kinetics, literature mole consumption rates and hydrate surface area obtained in a semibatch stirred tank reactor were used. The extraction of the intrinsic kinetic rate constant is intimately linked to the estimated hydrate surface area, which is difficult to accurately measure. Theoretical estimation of the surface area using a population balance is also problematic since it does not account for the inherent presence of foreign particles, of unknown quantity and size distribution, serving as nucleation sites. Finally, mole consumption rates in such experimental systems may be controlled by gas−liquid interphase mass transfer, suggesting that accurate interphase mass transfer coefficients are required for proper estimation of the intrinsic kinetic rate constant.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.011
Threshold uncertainty score0.022

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0020.001
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.061
GPT teacher head0.299
Teacher spread0.238 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
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

Citations69
Published2007
Admission routes2
Has abstractyes

Explore more

Same venueIndustrial & Engineering Chemistry ResearchSame topicMethane Hydrates and Related PhenomenaFrench-language works237,207