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Record W2470693733 · doi:10.1002/cjce.22583

Effect of additives on formation and decomposition kinetics of methane clathrate hydrates: Application in energy storage and transportation

2016· article· en· W2470693733 on OpenAlexvenueno aff
Asheesh Kumar, Omkar Singh Kushwaha, Pramoch Rangsunvigit, Praveen Linga, Rajnish Kumar

Bibliographic record

VenueThe Canadian Journal of Chemical Engineering · 2016
Typearticle
Languageen
FieldEnvironmental Science
TopicMethane Hydrates and Related Phenomena
Canadian institutionsnot available
FundersCouncil of Scientific and Industrial Research, IndiaNational University of Singapore
KeywordsClathrate hydrateMethaneChemistryKineticsHydrateDissociation (chemistry)DecompositionTetrahydrofuranInorganic chemistryChemical engineeringOrganic chemistry

Abstract

fetched live from OpenAlex

Abstract Methane gas storage and transportation via clathrate hydrates is proposed to be a potential solution for large‐scale energy storage. In this work, we study the formation and decomposition kinetics of methane hydrates (MH) in a laboratory‐scale unstirred crystallizer. The present investigation demonstrates comparative studies of hydrate formation and dissociation kinetics in the presence of tetrahydrofuran (55.6 and 27.8 mmol/mol, 5.56 and 2.78 mol % THF) and sodium dodecyl sulphate (1 mg/g, 0.1 wt% SDS). Moreover, the storage capacity and hydrate formation kinetics in both the systems are discussed. In a recent work, enhanced methane hydrate growth in the presence of THF at close to atmospheric conditions was demonstrated. The emphasis of the current work is to study the stability of hydrates to understand dissociation kinetics by measuring the rate of hydrate decomposition at different temperatures. Hydrate stability measurements were performed at −8, −3, 2, 10, and 20 °C to study the decomposition rates of MH and self‐preservation in presence of the two additives THF and SDS.

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)0.000
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 score0.015
Threshold uncertainty score0.122

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 head0.003
GPT teacher head0.180
Teacher spread0.177 · 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

Citations45
Published2016
Admission routes1
Has abstractyes

Explore more

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