Thermodynamic modelling of gas hydrate dissociation conditions in porous medium in the presence of <scp>NaCl</scp> /methanol aqueous solution
Bibliographic record
Abstract
Abstract Due to the growing significance of the existence of gas hydrates in natural media like the ocean floor/permafrost regions and the extraction of natural gas from hydrate reservoirs using thermodynamic hydrate inhibitors, investigating the dissociation of gas hydrates in porous media in the presence of inhibitors is crucial. This work examines a broad range of laboratory data on the dissociation conditions of gas hydrates in the porous mediums when salt/alcohol aqueous solutions are present. The temperature of gas hydrate dissociation in the presence of pure water is calculated using the van der Waals–Platteeuw solid solution theory. The water activity in the porous medium is then calculated by taking into account a number of variables, including the radius of the porous medium, molar volume, shape factor, wetting angle, and surface tension. The Pitzer and Margules activity coefficient models are used to determine the water activity in the presence of salt and alcohol, respectively. Lastly, the gas hydrate dissociation temperature in a porous medium in the presence of salt and/or alcohol aqueous solution is determined by combining Piereon's model with an enthalpy‐based correlation that was proposed by Azimi et al. The selected package can consistently correlate the gas hydrate dissociation conditions in a porous medium in the presence of alcohol or salt aqueous solution. The average absolute deviation (AAD) of 0.67 K for the whole data bank (90 experimental data points) shows the precision of the model.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.000 | 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".