Synergistic enhancement of methane hydrate inhibition using biopolymers: Experimental and computational insights on kinetics and performance
Bibliographic record
Abstract
Abstract The kinetics of methane hydrate formation in the presence of eight biopolymers (Arabic gum, xanthan gum, inulin, dextran, starch, pectin, pullulan, and guar gum) were investigated and analyzed. A rocking cell apparatus and constant cooling methods were used to determine the nucleation time and gas consumption rate. The effects of these biopolymers were tested at concentrations (0.25, 0.50, 0.75, and 1.0 wt.%). The results indicated that when tested individually, all biopolymers successfully prolonged the induction time, but some of them did not reduce the gas consumption rate at specific concentrations. Guar gum and Arabic gum effectively extended the induction time to 186 and 178 min, respectively. In addition, they decreased the gas consumption rate to the lowest value of 1.84 and 0.89 × 10 −4 mol/min, respectively. The synergistic effect of biopolymers with PVP and PVCap was also investigated. The results showed that they are effective synergists. In addition, they had a substantial synergistic impact on PVCap and PVP performance. Starch and Arabic gum demonstrated exceptional inhibition synergy with PVP, extending the induction time to 349 and 332 min and reducing the rate of gas consumption to 1.02 and 1.01 × 10 −4 mol/min, respectively. Inulin and dextran exhibited extraordinary inhibition synergy, prolonging the induction time to 472 and 434 min and reducing the gas consumption rate to 0.63 and 0.42 × 10 −4 mol/min, respectively. The simulation results demonstrate that guar gum substantially enhances the hydrate inhibition performance of both PVCap and PVP, showing excellent correlation with experimental findings.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".