Kinetic investigation of <scp> CO <sub>2</sub> </scp> and <scp> N <sub>2</sub> </scp> clathrate hydrate formation using cyclopentane: Application in desalination
Bibliographic record
Abstract
Abstract Hydrate‐based desalination could be a promising technique for producing fresh water from saline water, as it is an eco‐friendly process and suitable for large‐scale implementation. To make the hydrate‐based desalination technology easily scalable, we looked at using air (or N 2 ) or CO 2 as a hydrate former, along with cyclopentane (CP). Hydrate former CP helps to reduce the operating conditions, as CP forms hydrate at ambient pressure. However, hydrate formation kinetics due to water‐insoluble CP is slow. In this work, the kinetics of hydrate formation in saline water were investigated and compared to identify the utility of CO 2 and N 2 as hydrate formers for desalination work. The addition of CP as a hydrate former should transform the structure of CO 2 hydrate from structure I (sI) to structure II (sII), as CP occupies the large cages (5 12 6 4 ) in the gas hydrate. A set of three similar reactors were used for this study to collect data quickly. Furthermore, the triple reactor setup is a unique reactor design mounted on a shaker, and a set of SS‐316 balls present inside the horizontal reactor imparts the mixing. Experiments with the CO 2 ‐CP mixture and N 2 ‐CP mixture have been studied in the presence or absence of 3 wt.% NaCl at 274 K and 3 MPa pressure. The gas uptake kinetics, water recovery, and separation efficiency have been investigated.
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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".