Integration of Membrane-Based Pretreatment Methods with Pressure-Retarded Osmosis for Performance Enhancement: A Review
Bibliographic record
Abstract
Osmotic energy provides an emerging renewable alternative by leveraging the salinity gradient between two solutions. Among these technologies, pressure-retarded osmosis (PRO) has attracted attention; however, its deployment is hindered by obstacles resulting from impurities in feed and draw solutions and lack of suitable membranes. This review explores the integration of membrane-based pretreatments with PRO, highlighting their influence on resolving the technical drawbacks of standalone PRO systems. Membrane-based pretreatments have shown considerable potential to overcome these challenges by improving the quality of water, reducing membrane fouling and enhancing its performance, and ultimately contributing to recovery of energy, resulting in higher power density. Additionally, the use of different nanomaterials has been proposed for membrane modification to optimize PRO performance. Moreover, the study investigates recent advancements in hybrid configurations for harnessing existing infrastructure and to enhance energy efficiency. Offering a comprehensive review on this integrated approach contributes to valuable insights for advancing membrane-based hybrid systems toward commercial viability. Consequently, investment in developing advanced computational modeling and experimental validation, utilization of advanced membrane materials with higher fouling resistance, and optimization of system configurations by using dual-stage and multi-stage designs are required to overcome these limitations.
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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 | 0.003 | 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 | 0.002 |
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".