Test of Membrane Aerated Biofilm Reactors for Nitrogen Removal in Wastewater Treatment
Bibliographic record
Abstract
Abstract Membrane aerated biofilm reactor, as a biological wastewater treatment technology, has been nearly mature on a commercial scale. It uses bubble-free aeration to provide oxygen for biological nitrification and wastewater degradation. A novel oxygen-permeable hollow fiber membrane (Zeelung cord) specifically designed for use in a membrane aerated biofilm reactors (MABR). These fibers are organized into bundles, which are wrapped around the reinforcing core to increase strength. This permeable membrane allows oxygen to diffuse into the attached biofilm, which directly leads to the biological oxidation of pollutants in the wastewater. This study aimed to determine the nitrification and oxygen transfer capacity of Zeelung fibers used in the MABR system. The effects of various C/N ratios (in the range of 1.0 to 3.0) on the membrane modules were studied using three laboratory-scale reactors over the course of 165 days. In this test, the average removal efficiency of COD can reach 74% under selected conditions, up to 90%. Meanwhile, the average nitrification rate is 3.9 g/d/m2, the average ammonia removal rate is 90%, and the maximum value can reach 99%. In addition, the oxygen transfer rate of the fiber in the liquid phase was 19.65 g/d/m2. The experiment also indicated that the nitrification rate is directly proportional to the transfer flux of oxygen and is related to the content of dissolved oxygen in the water. The nitrification rate can be controlled by controlling the concentration of dissolved oxygen in water, thus affecting the removal rate of ammonia nitrogen.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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".