Coupling Microbial Electrolysis Cell and Activated Carbon Biofilter for Source-Separated Greywater Treatment
Bibliographic record
Abstract
Reclamation and reuse of wastewater are increasingly viewed as a pragmatic tool for water conservation. Greywater, which includes water from baths, washing machines, dishwashers, and kitchen sinks, is a dilute wastewater stream, making it an attractive stream for extraction of non-potable water. However, most previous studies primarily focused on passively aerated biological and physicochemical treatment processes for greywater treatment. Here, we investigated an integrated process of a microbial electrochemical cell (MEC) followed by granular activated carbon (GAC) biofilter for greywater treatment. The integrated system could achieve 99.3% removal of total chemical oxygen demand (TCOD) and 98.7% removal of the anionic surfactants (linear alkylbenzene sulphonates) from synthetic greywater at a total hydraulic residence time (HRT) of 25 h (1 day for MEC and 1 h for GAC biofilter). For one-day HRT, the maximum peak volumetric current density from MEC was 0.65 A/m3, which was comparable to that achieved at four-day HRT (0.66 A/m3). The adsorption by GAC was identified as a key mechanism for the removal of organics and surfactants. In addition, recirculation of liquid within the GAC biofilter was identified as a critical factor in achieving high-rate treatment. Although results indicated that GAC biofilter could be a standalone process for greywater, MEC can provide an opportunity for potential energy recovery from greywater. However, further studies should focus on developing high-rate MECs with higher energy recovery potential for practical operation.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".