Advanced tools for fluid and fouling layer characterization applied to the description of membrane fouling phenomena for particle‐organic matter mixtures
Bibliographic record
Abstract
Abstract Fouling by the formation of a complex cake remains a major hindrance of membrane bioreactors. One approach to fouling mitigation is to add particles within the biofluid in order to improve sludge filterability. Despite a number of studies dealing with this topic, fouling mechanisms are still not clear. This study reports the use of an original methodology for better fouling understanding and mitigation. This original approach is based on in situ characterization of cake local structural properties together with a detailed fluid characterization using size exclusion chromatography and the monitoring of global filtration performances. In order to obtain comprehensive elements, the impact of particle addition within cakes having markedly different behaviours (slightly and highly compressible cake, different specific resistance) was investigated considering different biofluids and membranes. The results demonstrated that a potential beneficial effect of particles on filtration performance depended on the cake properties in the absence of particles. It was found that particle addition could only improve biofluid filterability when the fouling layer was highly compressible and consisted mainly of macromolecular protein. In addition, in situ characterization revealed that particles within the fouling layer strongly influenced organic matter deposition at the membrane. It also showed that the effect of particles on fouling mitigation depended on the environment surrounding them within the fouling layer.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".