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Record W2117634502 · doi:10.1002/aic.14433

Rheological evaluation of kinetic hydrate inhibitors in NaCl/<i>n</i>‐heptane solutions

2014· article· en· W2117634502 on OpenAlexafffund
Hassan Sharifi, Savvas G. Hatzikiriakos, Peter Englezos

Bibliographic record

VenueAIChE Journal · 2014
Typearticle
Languageen
FieldEnvironmental Science
TopicMethane Hydrates and Related Phenomena
Canadian institutionsUniversity of British Columbia
FundersNatural Sciences and Engineering Research Council of CanadaUniversity of British Columbia
KeywordsHydrateNucleationHeptaneEconomies of agglomerationRheologyPolyvinylpyrrolidoneRheometerClathrate hydrateChemical engineeringKinetic energyChemistryMaterials scienceOrganic chemistryComposite materialEngineeringPhysics

Abstract

fetched live from OpenAlex

The performance of polyvinylpyrrolidone (PVP) and polyvinylcaprolactam (PVCap) as kinetic hydrate inhibitors (KHIs) in the presence of NaCl and n‐heptane was evaluated by using a high‐pressure cell in conjunction with a rotational rheometer. The addition of KHIs was found to prolong the induction time and decrease the hydrate growth. On the other hand, hydrates agglomerated more readily. PVP performed more efficiently than PVCap in delaying nucleation time but PVCap controlled the growth and delayed agglomeration more effectively. Addition of n‐heptane to the system increased induction time and reduced growth. Unexpectedly, addition of KHIs in the presence of n‐heptane decreased nucleation time but controlled growth effectively. Meanwhile, hydrate particles remained dispersed more efficiently and no agglomeration was detected. These observations confirm that high‐pressure rheology is an additional laboratory assessment tool to evaluate KHIs under ocean field conditions. © 2014 American Institute of Chemical Engineers AIChE J , 60: 2654–2659, 2014

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)0.003
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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.309
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
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)0.0030.000

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 head0.025
GPT teacher head0.250
Teacher spread0.225 · 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 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

Citations33
Published2014
Admission routes2
Has abstractyes

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