Challenges Facing Pressure Retarded Osmosis Commercialization: A Short Review
Bibliographic record
Abstract
Pressure-retarded osmosis (PRO) is a promising technology that harvests salinity gradient energy. Even though PRO has great power-generating potential, its commercialization is currently facing many challenges. In this regard, this review highlights the discrepancies between the reported power density obtained by lab-scale PRO systems, as well as numerical investigations, and the significantly low power density values obtained by PRO pilot plants. This difference in performance is mainly due to the effect of a pressure drop and the draw pressure effect on the feed channel hydrodynamics, which have significant impacts on large-scale modules; however, it has a minor or no effect on small-scale ones. Therefore, this review outlines the underlying causes of the high power density values obtained by lab-scale PRO systems and numerical studies. Moreover, other challenges impeding PRO commercialization are discussed, including the effect of concentration polarization, the solution temperature, the pressure drop, and the draw pressure effect on the feed channel hydrodynamics. In conclusion, this review sheds valuable insights on the issues facing PRO commercialization and suggests recommendations that can facilitate the successful development of PRO power plants.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".