Current challenges in <scp>hydrate‐based</scp> desalination: Kinetic and thermodynamic perspective
Bibliographic record
Abstract
Abstract Water scarcity is becoming a severe problem worldwide due to inadequate freshwater resources and swift population growth. Seawater desalination is one of the vital approaches to meet the demand for freshwater. However, energy and associated costs with conventional seawater desalination techniques are incentivizing non‐conventional water desalination processes. Water desalination using gas hydrates formation is one of the emerging non‐conventional processes. In this perspective article, recent advances in hydrate‐based seawater desalination (HBSD) have been critically analyzed to outline a future path towards a clean and efficient hydrate‐based desalination process. It provides a detailed comparison of various processes developed over decades, and measured desalination efficiencies with their process details. Moreover, the current challenges, limitations, and future perspectives of hydrate‐based desalination are also discussed. The study also recapitulates the thermodynamics and kinetics aspects of the hydrate‐based desalination process. In addition, various factors controlling the desalination efficiencies, such as control of the separation of hydrate crystals, salt deposition on hydrate particles, and hydrate morphology, were thoroughly investigated with their proposed process designs. The kinetics of hydrate formation is also assessed, with the possibility of a zero‐induction regime and its consequent impact on hydrate morphology. The current capabilities of the thermodynamics models (Gibbs energy minimization + electrolyte equation of state) were discussed using various commercially available software. Additionally, the role of hydrate promotors is also discussed, which can reduce the higher cost associated with the hydrate‐based desalination process.
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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".