Investigations into the effects of xylomannan-based sugar compounds on methane hydrate kinetics
Bibliographic record
Abstract
Over the past two decades, the focus of industrial research into hydrate inhibition has shifted from hydrate prevention to hydrate management, with emphasis on the development of environmentally-friendly, low-dosage kinetic inhibitors.The purpose of this work was to measure the effects of four naturally occurring polysaccharides on structure I methane gas hydrate nucleation and growth kinetics and to assess their application to industrial-type systems.These compounds were selected based on the xylomannan sugar found in the Alaskan beetle Upis Ceramboides that afforded 3.7°C of thermal hysteresis (Walters et al., 2009).Experiments conducted at 275.15K and pressures of 4645 and 5645 kPa with concentrations of 0.07 and 0.7 wt% of chemical revealed that these polysaccharides exhibit very weak inhibiting effects on hydrate nucleation and growth, with solubility appearing to be a major factor in performance.Furthermore, their precise mode of action, initially assumed to be kinetic, was brought into question by the similar performances of a thermodynamic inhibitor on hydrate kinetics, as well as the lack of certain key features in their mole consumption profiles when compared to an effective kinetic inhibitor.These results reaffirm that the application of natural polysaccharides to hydrate inhibition in their unmodified chemical states is limited, and the focus of future developments should be on solubilization and functionalization.
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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) | 0.001 | 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".