A Review of Applications of Rotating and Vibrating Membranes Systems: Advantages and Drawbacks
Bibliographic record
Abstract
Dynamic filtration (DF) consists in creating a high membrane shear rate by disks rotating near a fixed membrane or by rotating or vibrating membranes. The shear rate can exceed 3 105s-1 in some modules and significantly increases permeate flux and membrane selectivity as compared to cross flow (CF) devices. This paper describes several DF industrial modules and gives equations for calculating shear rates at rotating and vibrating membranes. It reviews 23 recent articles from 2008 to 2014, dealing with diverse applications: separation of microalgae from sea water by UF, clarification of rough beer, concentration of CaCO3 suspensions, treatment of dairy effluents and shipboard wastewaters, inulin extraction from chicory juice, treatment of oil field water, and separation of bovine albumin from yeast. In several applications, the maximum permeate flux at initial concentration ranged from 270 to 760 Lh-1m-2. Modules with ceramic membranes rotating around several shafts inside a housing seem to be preferable to the concept of multi-compartments modules with metal disks rotating between fixed membranes. Since the cost of DF modules is higher than that of spiral wound ones, it is better to apply DF to ”end of pipe treatment” after an initial concentration by CF.
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 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| 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".