Recovery of Methane from a Variable-Volume Bed of Silica Sand/Hydrate by Depressurization
Bibliographic record
Abstract
Methane hydrate was formed in water occupying the interstitial spaces of a cylindrical bed of silica sand particles. The sand particles have an average diameter equal to 329 μm. The amount of methane consumed during the experiment (methane gas uptake) was determined through pressure and temperature measurements and mass balance calculations. Three different sized beds of silica sand particles were used. Water conversion to hydrates in the range of 73−84% was achieved for all of the formation experiments. Hydrate formation was followed by decomposition at 4.0 °C driven by depressurization at 3.1 MPa (nine experiments) and 2.3 MPa (one experiment). Methane recovery measurement curves were determined for each experiment. The initial rate of recovery was found to be strongly dependent on the silica sand bed size. The rate of recovery was found to depend weakly on the size during the second stage, and after 1.25 h, it was constant. During decomposition at 2.3 MPa, the temperature in some locations inside the bed dropped below the freezing point of water and the gas recovery rate was enhanced. This is in agreement with recently reported conclusions by Tsypkin [ Fluid Dynamics 2005, 40 (1), 117−125] and Zhou et al. [ Ind. Eng. Chem. Res. 2009, 48 (6), 3142−3149]. Finally, the gas recovery data was correlated with an empirical model with one parameter that was found to depend linearly on the bed size.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".