The effect of <scp>PVP</scp> and <scp>PVA</scp> on hydrate formation kinetics of ethane and propane mixture in the presence of kaolin nanoparticles
Bibliographic record
Abstract
Abstract The blockage of gas transmission pipelines due to gas hydrates poses a significant challenge for the gas industry. Low‐dosage hydrate inhibitors (LDHI) are utilized to influence the kinetics of hydrate formation, including nucleation, growth, and/or agglomeration. In the current study, a series of batch, isochoric, and isothermal tests were carried out to investigate the impacts of two LDHIs, poly N‐vinylpyrrolidone (PVP) and polyvinyl alcohols (PVA). This study examined the influence of these inhibitors on nucleation and growth of ethane+propane mixture in the presence of kaolin nanoparticles. In laboratory studies and using pure constituents, the rate of hydrate formation is low to investigate the inhibitors effect. To enhance the rate of hydrate formation, kaolin nanoparticles were utilized as they serve as a suitable representative for solid minerals commonly found in pipelines. The gas phase's compositional change were measured using a gas chromatograph. The results revealed that the growth stage of mixed‐gas exhibited two distinct steps, attributed to the formation of two different structures known as structure II and structure I. During the first step, structure II was formed, and the cavities were occupied by both gas components. In the second stage, structure I was formed by ethane. The effect of temperature on the induction time (nucleation rate) and the growth rate during the second stage was lower than that at the first stage. The increase in PVA concentration resulted in an increase in the induction time and a decrease in the rate of growth during the first stage. The performance of PVA was found to be affected by temperature. Lower temperatures resulted in the formation of less foam, providing improved performance for PVA at −0.5°C. PVP had a stronger impact on induction time and both growth steps compared to PVA, indicating stronger inhibitory impact of PVP on nucleation of hydrates initiated by propane and the growth of both propane and ethane hydrates.
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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".