Trace organics removal using three membrane bioreactor configurations: MBR, IFAS-MBR and MBMBR
Bibliographic record
Abstract
Seventeen pharmaceutically active compounds and 22 other trace organic pollutants were analysed regularly in the influent and permeate from a semi-real plant treating municipal wastewater. The plant was operated during 29 months with different configurations which basically differed in the type of biomass present in the system. These processes were the integrated fixed-film activated sludge membrane bioreactor (IFAS-MBR), which combined suspended and attached biomass, the moving bed membrane bioreactor (MBMBR) (only attached biomass) and the MBR (only suspended biomass). Moreover, removal rates were compared to those of the wastewater treatment plant (WWTP) operating nearby with conventional activated sludge treatment. Reverse osmosis (RO) was used after the pilot plant to improve removal rates. The highest elimination was found for the IFAS-MBR, especially for hormones (100% removal); this was attributed to the presence of biofilm, which may lead to different conditions (aerobic-anoxic-anaerobic) along its profile, which increases the degradation possibilities, and also to a higher sludge age of the biofilm, which allows complete acclimation to the contaminants. Operating conditions played an important role, high mixed liquor suspended solids (MLSS) and sludge retention time (SRT) being necessary to achieve these high removal rates. Although pharmaceuticals and linear alkylbenzene sulfonates showed high removal rates (65-100%), nonylphenols and phthalate could only be removed to 10-30%. RO significantly increased removal rates to 88% mean removal rate.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".