Development and testing of a low sludge discharge membrane bioreactor for greywater reclamation
Bibliographic record
Abstract
A low sludge discharge membrane bioreactor (LSDMBR) for greywater reclamation was developed and tested in this study. LSDMBR was designed by combining an aerobic activated sludge process with an immersed membrane filtration process. LSDMBR offers the following advantages over conventional activated sludge processes. Firstly, LSDMBR has a high biomass concentration attributed to membrane filtration of the effluent and low sludge wasting, which results in reduced disposal costs. Secondly, LSDMBR provides for a favorable environment for nitrifying bacteria and the promotion of their growth. Finally, LSDMBR provides for higher treatment efficiencies and produces higher-quality effluent for reuse over conventional processes. It was found from laboratory testing that the contaminant removal efficiencies achieved by LSDMBR within 2.5 h of hydraulic retention time (HRT) were as follows: 95% removal of anionic surfactants and 90% removal of 5-day biochemical oxygen demand (BOD5), respectively. It was also found that the effluent ammonia and total Kjeldahl nitrogen (TKN) concentrations were reduced to less than 1 mg/L and 6 mg/L, respectively. Results of the study suggested that LSDMBR is a small-scale and self-sustaining greywater reclamation system that requires low installation space and low sludge discharge.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".