Fouling Behavior in a High-Rate Anaerobic Submerged Membrane Bioreactor (AnMBR) for Palm Oil Mill Effluent (POME) Treatment
Bibliographic record
Abstract
The characteristics of foulant in the cake layer and bulk suspended solids of a 10 L submerged anaerobic membrane bioreactor (AnMBR) used for treatment of palm oil mill effluent (POME) were investigated in this study. Three different organic loading rates (OLRs) were applied with prolonged sludge retention time throughout a long operation time (270 days). The organic foulant was characterized by biomass concentration and concentration of extracellular polymeric substances (EPS). The thicknesses of the cake layer and foulant were analyzed by confocal laser scanning microscopy and Fourier transform infrared spectroscopy. The membrane morphology and inorganic elements were analyzed by field emission scanning electron microscope coupled with energy dispersive X-ray spectrometer. Roughness of membrane was analyzed by atomic force microscopy. The results showed that the formation and accumulation of protein EPS in the cake layer was the key contributor to most of the fouling. The transmembrane pressure evolution showed that attachment, adsorption, and entrapment of protein EPS occurred in the membrane pores. In addition, the hydrophilic charge of proteins and polysaccharides influenced the adsorption mechanism. The composition of the feed (including hydroxyl group and fatty acid compounds) and microbial metabolic products (protein) significantly affected membrane fouling in the high-rate operation.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".