Electro-membrane bioreactor for the treatment of landfill leachate: Effect of SRT, HRT and current density on contaminant removal and membrane fouling
Bibliographic record
Abstract
The coupling of membrane bioreactor (MBR) with electrocoagulation (EC) in a submerged single reactor (E-MBR) is proposed for the treatment of landfill leachate (LFL) over a 240-days continuous operation. The E-MBR was designed to simultaneously remove total phosphorus (P tot ) (50 ± 12 mg·L −1 ), ammonia nitrogen (N-NH 4 + ) (351 ± 20 mg·L −1 ), chemical oxygen demand (COD) (3409 ± 394 mg·L −1 ), and recalcitrant residual organics. The lab-scale aerobic E-MBR was comprised of two electrodes (Fe-anode and Ti/Pt-cathode) immersed in the mixed liquor of a hollow fiber membrane bioreactor (0.04 μm pore size). The effects of current densities (CD) (2.2, 4.5 and 9.1 A·m −2 ), hydraulic retention time (HRT) (24 h and 16 h), and solid retention time (SRT) (30 days and infinite) on pollutant's removal and membrane clogging were evaluated. The best performance for pollutants removal was obtained in E-MBR-3, operated at a CD of 9.1 A·m −2 , HRT of 24 h and SRT of 30d, with removal percentages of 90 %, 97 %, and ≈100 % for N-NH 4 + , COD and P tot , respectively. However, when HRT was reduced to 16 h (E-MBR-4), N-NH₄ + removal efficiency declined significantly from 90 % to 58 %, despite maintaining the same current density, highlighting the critical role of HRT in nitrification performance. Furthermore, prolonged SRT was found to accelerate membrane clogging, emphasizing the importance of optimizing SRT in E-MBR to mitigate fouling. This study provides valuable insights into the interactions between SRT, HRT, Fe coagulant dosage, and membrane performance in submerged E-MBRs. • E-MBR demonstrated excellent COD, nitrogen and phosphorus removal for LFL treatment. • Iron coagulants increased flocs size and avoided blockage of membrane pores. • Lower SRT and higher CD in E-MBR favoured COD removal and reduced membrane fouling. • Phosphorus was completely precipitated by iron coagulants released from electrodes. • E-MBR also assisted in recalcitrant organics removal.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".