Submerged Osmotic Processes: Design and Operation to Mitigate Mass Transfer Limitations
Bibliographic record
Abstract
Submerged forward osmosis (FO) is of high interest for bioreactors, such as osmotic membrane bioreactor, microalgae photobioreactor, food or bioproduct concentration where pumping through pressurized modules is a limitation due to viscosity or breakage of fragile components. However, so far, most FO efforts have been put towards cross flow configurations. This study provides, for the first time, insights on mass transfer limitations in the operation of submerged osmotic systems and offer recommendations for optimized design and operation. It is demonstrated that operation of the submerged plate and frame FO module requires draw circulation in the vacuum mode (vacuum assisted osmosis) that is in favor of the permeation flux. However, high pressure drops and dead zones occurring in classical U-shape FO draw channel strongly disadvantage this design; straight channel design proves to be more effective. External concentration polarization (ECP) is also a crucial element in the submerged FO process since mixing of the feed solution is not as optimized as in the cross flow module unless applying intense stirring. Among the mitigation techniques tested, air scouring proves to be more efficient than feed solution circulation. However, ECP mitigation methodology has to be adapted to application specificities with regards to combined/synergetic effects with fouling mitigation.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".