The Water–Energy Nexus: Solutions towards Energy‐Efficient Desalination
Bibliographic record
Abstract
Abstract Global water shortages across all continents have led to the explosive practice of desalination. However, desalination is undeniably recognized as one of the most energy‐intensive techniques for creating a clean and safe water supply. Cost reduction in different aspects is necessary to make desalination processes affordable and accessible. In fact, the cost of water from desalination facilities is momentously impacted by the energy requirements for water production. As the water production cost cannot be separated from the issue of energy, the desalination community is continuously seeking ways to reduce energy consumption further. Current research focuses on assessing and alleviating the major energy issues by finding ways to improve the energy efficiency of desalination facilities, which would pave the way for overall cost reduction. Improving the process and the efficiencies of materials implies improved water quality and an increase in the quantity produced per unit of energy consumed. This review highlights recent emerging approaches that aim to reduce the energy consumption and, hence, the water production cost of desalination technology. In brief, the advances made in membrane science and technology, the development of emerging desalination processes and their integrated systems, as well as the use of renewable energy and energy‐recovery systems are recognized as effective and feasible solutions towards energy‐efficient desalination to address the water crisis.
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 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 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".