Sustainable electrification and water desalination with distributed renewable energy sources
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This study examines how distributed renewable energy systems can simultaneously meet electricity, thermal, and freshwater needs on three remote Persian Gulf islands: Failaka, Larak, and Lavan. By integrating renewable energy with reverse osmosis (RO) desalination, the study enhances resource management. The proposed configurations were assessed based on technical, economic, environmental, and social criteria, with Failaka emerging as the most efficient solution. Failaka achieved a net present cost (NPC) of $1.09 M, a cost of electricity (COE) of $0.091/kWh, and a renewable fraction (RF) of 17.8% while reducing diesel and natural gas consumption by 21.6% and 1.4%, respectively, compared to Larak and Lavan. A comparative analysis highlights significant cost and performance variations, with Failaka demonstrating the lowest energy costs and highest renewable integration, whereas Larak (RF = 14.3%, NPC = $1.21 M, COE = $0.101/kWh) and Lavan (RF = 13.7%, NPC = $1.22 M, COE = $0.101/kWh) exhibit higher costs and lower renewable contributions. Sensitivity analysis highlights the influence of wind speed, derating factors, and fuel prices, with increased wind penetration reducing COE from $0.12/kWh to $0.08/kWh. Heat recovery integration further optimizes costs and emissions. Failaka also demonstrates superior socio-economic benefits, including job creation and the lowest carbon footprint, reinforcing sustainability. This work differs from existing energy-water-heat studies by introducing an integrated cogeneration framework that couples renewable electricity, waste-heat utilization, and RO desalination within a single optimized system.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 it