Effects of salt content on secondary formation of hydrates in complex systems
Bibliographic record
Abstract
Abstract The phenomenon of ‘memory effect’ exists in the secondary generation of natural gas hydrates, which is mainly manifested in the fact that the induction time required for the nucleation of hydrates in the secondary generation of hydrates is significantly shorter than that of the primary generation, thus accelerating the generation of hydrates. At present, the research on the memory effect phenomenon has been proven to exist, and its influencing factors have become a research hotspot. In order to study the influence of decomposition time and salt content on the memory effect, the hydrate was generated under the system of porous medium alumina complexed with surfactant SDS, and hydrate secondary generation experiments were carried out by adding different contents of NaCl according to four different decomposition times. The influence of four different decomposition times on the memory effect phenomenon in the secondary generation of hydrates and the influence of different salt contents on the memory effect phenomenon were analyzed by observing the pressure drop changes in the reactor during the hydrate generation process and calculating the methane gas consumption. The experimental results show that different decomposition times can affect the effect and time of the memory effect, and the effect of decomposition time on the memory effect does not show a linear relationship. The addition of salt may have an inhibitory effect on the memory effect.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| 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".