Synergistic Effect of SDS and Rha on Hydrate-Based Purification of Low-Concentration Coalbed Methane in the Presence of THF
Bibliographic record
Abstract
Low-concentration coalbed methane (LCCBM) is a type of unconventional natural gas and it poses significant challenges for energy recovery and environmental management. In this study, a multifaceted experimental methodology combining kinetic measurements, in situ Raman spectroscopy, and high-pressure morphological observations was employed to investigate the synergistic effects of sodium dodecyl sulfate (SDS) and Rhamnolipid (Rha) biosurfactant on the hydrate-based purification of the low-concentration coalbed methane (LCCBM) gas mixture in the presence of tetrahydrofuran (THF). It was found that the CH 4 recovery efficiency and selectivity are greatly improved in the SDS + Rha + THF composite system. At the optimal concentrations (300 ppm of SDS +500 ppm Rha +5.56 mol % THF), the CH 4 recovery rate of 68.52% and the separation factor of 3.04 are achieved, outperforming the systems using individual promoters like THF + SDS or THF + Rha. Morphological observations show that SDS can promote hydrate nucleation and growth, while Rha increases hydrate nucleation sites, improves hydrate compactness, and raises the proportion of strong hydrogen bonds in the aqueous solution. In the SDS + Rha + THF composite system, the advantages of SDS and Rha are combined, so the strong hydrogen bond content is increased. This synergy not only accelerates the rate of LCCBM hydrate formation but also enhances the LCCBM purification efficiency. Therefore, this study provides a green and efficient strategy for the hydrate-based LCCBM purification, bridging the gap between laboratory-scale studies and industrial application.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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) | 0.000 | 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".