Assessment of Anaerobic Membrane Bioreactors in High-Strength Synthetic Wastewater Treatment
Bibliographic record
Abstract
This study investigates the performance of an Anaerobic Membrane Bioreactor (AnMBR) treating high-strength synthetic wastewater.The system was acclimated using a phased approach, progressively increasing Chemical Oxygen Demand (COD) concentrations from 500 to 2500 mg/L over 120 days under mesophilic conditions (30 ± 1°C).The AnMBR, utilizing a 10L reactor with ceramic microfiltration membranes (pore size: 0.1 μm, area: 0.04 m²), demonstrated exceptional treatment efficiency.Peak COD removal reached 99.26%, while sustained BOD removal efficiencies ranged from 74.59 to 98.79%.Biomass characterization revealed continuous growth, with MLSS and MLVSS increasing from 4.71 to 8.19 g/L and 2.48 to 6.79 g/L, respectively, and the MLVSS/MLSS ratio maintained between 0.53-0.86.Biogas production increased significantly from 0.05 to 2.89 L/day, with methane content rising from 45% to 68%.Effluent VFA concentrations increased from 31.20 to 191 mg/L, indicating efficient organic matter decomposition, while alkalinity remained stable, demonstrating the system's pH buffering capacity.These findings highlight the effectiveness of AnMBR technology for treating high-strength wastewater and its potential for energy recovery through biogas production, but also emphasize the need for fouling mitigation strategies.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".