Prediction of induction time for natural gas components during gas hydrate formation in the presence of kinetic hydrate inhibitors in a flow mini‐loop apparatus
Bibliographic record
Abstract
Abstract The objective of this work is the prediction of induction time (ti) for simple gas hydrate formation in the presence or absence of kinetic hydrate inhibitors at various conditions based on the Kashchiev and Firoozabadi model in a flow mini‐loop apparatus. For this purpose, the ti model is developed for simple gas hydrate formation in batch system for natural gas components during hydrate formation in a flow mini‐loop apparatus. A laboratory flow mini‐loop apparatus is designed and built up to measure the ti for simple gas hydrate formation when a hydrate former (such as C1, C3, CO2 and i‐C4) is contacted with water in the absence or presence of dissolved inhibitor, such as poly vinylpyrrolidone, PVCap and L‐tyrosine. In each experiment, a water blend saturated with pure gas is circulated up to a required pressure. Pressure is maintained at a constant value during experimental runs by means of the required gas make‐up. The average absolute deviation (AAD) of the predicted ti values from the corresponding experimental data are found to be about 11% and 9.4% for gas hydrate formation ti in the presence or absence of kinetic hydrate inhibitors, respectively. © 2012 Canadian Society for Chemical Engineering
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| 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 | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".