A bibliometric review of smart technologies: IoT and AI for sustainable and efficient desalination
Bibliographic record
Abstract
The scarcity of freshwater, driven by population growth, climate change, and industrial expansion, has rendered seawater desalination an essential solution for addressing global water challenges. Nonetheless, traditional desalination methods face significant obstacles, including high energy consumption and environmental consequences from brine disposal. This study examines how IoT and AI technologies can enhance desalination systems, highlighting growing academic interest between 2011 and 2024 in improving their efficiency, energy consumption, and sustainability. This study demonstrates that integrating IoT sensors and AI-driven analytics into desalination systems can lead to substantial operational improvements. Real-time monitoring of key factors, like temperature, pressure, and CO2 levels, combined with predictive maintenance and adaptive control through AI. The study focuses on three main areas: energy optimization, sustainable brine handling, and advanced membrane technologies, showing how IoT and AI can improve desalination. Although countries like India, Spain, and Saudi Arabia have led progress, challenges remain, including high costs and limited global cooperation. This study introduces a streamlined model that integrates sensor and analytics systems to key plant performance metrics, such as energy use, uptime, fouling, and brine.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | Bibliometrics Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | low |
| gpt | Bibliometrics Domain: not available · Genre: Review About the Canadian research system: no · About a Canadian topic: no | Observational | low |
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 | 0.005 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.059 | 0.106 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 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, unvalidatedLabeled directly by 2 models reading the full record.
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".