A Review of Clathrate Hydrate Based Desalination To Strengthen Energy–Water Nexus
Bibliographic record
Abstract
Water resource management impacts almost all aspects of the economy, in particular health, food production and security, domestic water supply and sanitation, energy, industry and environmental sustainability. For the last several decades, seawater has become an important source of fresh water as it is one of the most abundant resources on earth. Desalination is the process of removal of salts from seawater and is postulated to be a core technology in alleviating this problem. Clathrate hydrate based desalination (HyDesal) is a potential technology for seawater desalination. Salts are excluded from hydrate formation, thereby resulting in solid hydrate and concentrated brine. After separation from brine, the solid hydrate crystals upon dissociation produce pure water. In this work, a detailed review of the literature (both patents and publications) so far on HyDesal is critically evaluated, and prospects and directions to commercialize the HyDesal process are presented. Further, innovation by coupling LNG cold energy with HyDesal can make it economically attractive and can strengthen the energy–water nexus.
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 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".