Structural and Microbial Dynamics Analyses of MABR Biofilms
Bibliographic record
Abstract
Membrane aerated biofilm reactor (MABR) technology is currently garnering wide acceptance as a wastewater treatment technology due to process and operational advantages that have accrued during many years of investigation, but despite these achievements, the challenge of biofilm thickness control still persists. This work was therefore designed to expand the current knowledge of MABR operations, particularly with respect to biofilm thickness management and reactor performance stability. Biofilm thickness was controlled with intermittent washing of the membrane bundle based on a bulk dissolved oxygen (DO) concentration set-point of 0.2 mg/L. This nonaggressive membrane cleaning mechanism subdued biofilm sloughing, but maintained biofilm erosion, which supported the development of a multifunctional biofilm with the thickness and microbial activity adequate to stabilize reactor performance over 185 days. Applied in the treatment of municipal wastewater, the MABR demonstrated average organic carbon and ammonia nitrogen (NH4+─ N) removal efficiencies of 92%±2% and 100%±7.8%, respectively. The total inorganic nitrogen removal reached 84%±5% at mean surface loading rates of 10%±0.7 g COD/m2/d and 0.93±0.07 g N/m2/d within a hydraulic retention time of 2.5 h and with minimal reactor down time. The average biofilm density determined at the end of the study was 17.6 g/L, while the biofilm thickness determined to be 0.49 mm. A 16S rRNA analysis of the MABR microbial population at each stage indicated that a microbial community with sufficient biodiversity and relative abundance for stable reactor performance was sustained. Demonstrating that intermittent membrane cleaning with water effectively stabilized the MABR biofilm thickness, reduced process upsets, and maintained high performance at high substrate loading.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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) | 0.001 | 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 teacher head, 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".