MétaCan
Menu
Back to cohort
Record W2088164601 · doi:10.1021/ef400676g

Coal Mine Methane Gas Recovery by Hydrate Formation in a Fixed Bed of Silica Sand Particles

2013· article· en· W2088164601 on OpenAlexaff
Dong‐Liang Zhong, Nagu Daraboina, Peter Englezos

Bibliographic record

VenueEnergy & Fuels · 2013
Typearticle
Languageen
FieldEnvironmental Science
TopicMethane Hydrates and Related Phenomena
Canadian institutionsUniversity of British Columbia
FundersNational Science Foundation
KeywordsMethaneClathrate hydrateHydrateChemistryTetrahydrofuranChemical engineeringNucleationCoalMineralogyOrganic chemistry

Abstract

fetched live from OpenAlex

In the present work, the separation of CH 4 from low-concentration coal mine methane gas (30 mol % CH 4 /N 2 ) through hydrate crystallization was investigated in a fixed bed of silica sand particles. The influence of the additive tetrahydrofuran (THF) on hydrate equilibrium conditions and kinetics of CH 4 separation was studied as well. The incipient hydrate equilibrium conditions at 1 mol % THF were determined using the isothermal pressure search method. It was found that the presence of THF significantly reduced the hydrate equilibrium conditions as compared to those obtained in liquid water with the same gas mixture. CH 4 recovery in the water-saturated silica sand bed was considerably low (∼12.0%) because N 2 molecules might compete with CH 4 molecules to enter the hydrate crystals under high pressure conditions. The addition of THF to the bed of silica sand particles reduced the nucleation time of gas hydrate formed from the 30 mol % CH 4 /N 2 gas and increased the CH 4 recovery (∼21.4%) significantly. The comparison of CH 4 separation between the silica sand bed and the stirred reactor in the presence of THF indicated that CH 4 recovery was approximately the same, but the conversion of water to hydrate in the THF solution-saturated silica sand bed was largely increased.

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 categoriesInsufficient payload (model declined to judge)
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 score0.009
Threshold uncertainty score0.998

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)0.0030.000

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 head0.008
GPT teacher head0.198
Teacher spread0.190 · 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 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

Citations57
Published2013
Admission routes1
Has abstractyes

Explore more

Same venueEnergy & FuelsSame topicMethane Hydrates and Related PhenomenaFrench-language works237,207