Review on Hydrate-Based CH<sub>4</sub> Separation from Low-Concentration Coalbed Methane in China
Bibliographic record
Abstract
The consumption of natural gas is of great importance to optimize China’s energy structure toward reduced CO 2 emissions and is projected to increase in the next 10 years. Coalbed methane (CBM) is a primary unconventional natural gas and has been recognized as a significant energy resource to supplement conventional fossil fuels (oil and coal) as a result of its huge potential. It has been established that the hydrate-based gas separation is a promising method for the purification of low-concentration coalbed methane (LCCBM). In this work, the research on hydrate-based CH 4 separation from LCCBM conducted by Chinese researchers over the past 10 years has been reviewed. It is found that significant progress has been made to understand the hydrate-based CH 4 separation technology. On the other hand, the challenges related to achieving milder pressure operating conditions and enhancing the rate of hydrate formation should be overcome. Hence, further work is required to bridge the gap between the gas separation science and the technology. In this regard, future research directions are proposed in this work to help advance the research of hydrate-based CH 4 separation from LCCBM.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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.
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".