Bibliographic record
Abstract
A variety of applications have emerged for gas hydrates in industrial processes. However, to implement hydrate technologies, a fundamental understanding of their formation is required. This thesis investigates gas hydrate growth using low-dosage kinetic promoters as well as thermodynamic promoters. Two model surfactants are investigated to study the effect of kinetic promoters. Sodium dodecyl sulphate is used as a model anionic conventional surfactant and DOWFAX 8390 is used as a model anionic gemini surfactant. Results from this study show that surfactants do not significantly affect thermodynamic equilibrium and hydrate former solubility in methane hydrate systems. The promotion effect of both surfactants is studied over a range of concentrations and shows a sigmoid trend. Surfactants are found to significantly increase the gas hydrate former mole fraction during hydrate growth and are estimated to account for half of the increase in growth rate. The remainder of the growth increase is attributed to changes in the hydrate particle area. Thermodynamic promoters are investigated using semi-clathrate systems consisting of tetra-n-butylammonium bromide and water with a guest gas of either carbon dioxide or methane. Equilibrium temperature, pressure and solubility of all components are evaluated for these systems at hydrate-liquid-vapour equilibrium. These data are then used to develop and apply a kinetic model to estimate the intrinsic reaction rate constant of the carbon dioxide semi-clathrate system.
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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) | 0.002 | 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".