Efficiently Combining Water Reuse and Desalination through Forward Osmosis—Reverse Osmosis (FO-RO) Hybrids: A Critical Review
Bibliographic record
Abstract
Forward osmosis (FO) is a promising membrane technology to combine seawater desalination and water reuse. More specifically, in a FO-reverse osmosis (RO) hybrid process, high quality water recovered from the wastewater stream is used to dilute seawater before RO treatment. As such, lower desalination energy needs and/or water augmentation can be obtained while delivering safe water for direct potable reuse thanks to the double dense membrane barrier protection. Typically, FO-RO hybrid can be a credible alternative to new desalination facilities or to implementation of stand-alone water reuse schemes. However, apart from the societal (public perception of water reuse for potable application) and water management challenges (proximity of wastewater and desalination plants), FO-RO hybrid has to overcome technical limitation such as low FO permeation flux to become economically attractive. Recent developments (i.e., improved FO membranes, use of pressure assisted osmosis, PAO) demonstrated significant improvement in water flux. However, flux improvement is associated with drawbacks, such as increased fouling behaviour, lower rejection of trace organic compounds (TrOCs) in PAO operation, and limitation in FO membrane mechanical resistance, which need to be better considered. To support successful implementation of FO-RO hybrid in the industry, further work is required regarding up-scaling to apprehend full-scale challenges in term of mass transfer limitation, pressure drop, fouling and cleaning strategies on a module scale. In addition, refined economics assessment is expected to integrate fouling and other maintenance costs/savings of the FO/PAO-RO hybrid systems, as well as cost savings from any treatment step avoided in the water recycling.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".