Chemical inhibitors as potential allied for CO2 replacement in gas hydrates reservoirs: sodium chloride case study
Bibliographic record
Abstract
In previous experimental works we proved how the presence of sodium chloride may influence the replacement of methane contained into hydrate with carbon dioxide. Even if its chemical inhibitor effect is well known and documented in literature, the possibility of having different behaviours in function of the gaseous species involved in hydrate formation has not been explored. The first step of our research proved how NaCl inhibitor effect is more pronounced in methane hydrate formation rather than carbon dioxide one. That leads to a higher difference between temperature-pressure conditions describing the two species equilibrium curves and, thus, to greater possibilities of intervening in the replacement process in order to perform it and increasing both the amount of methane recovered and the quantity of carbon dioxide permanently stored. Two replacement tests were carried out in presence of 40 g/l of salt dissolved in water; then results were compared with two other tests previously realized with the same experimental apparatus but using pure demineralised water. The inhibitor effect of sodium chloride is well visible in the significantly lower quantity of methane hydrate formed in the first phase of tests. The same effect was observed during the carbon dioxide formation step, but its intensity was lower. In conclusion the use of NaCl led to a higher percentage of methane recovered and carbon dioxide stored, even if both quantities are lower than the respective value reached in tests carried out using demineralised water.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".