Stacked multi-electrode design of microbial electrolysis cells for rapid and low-sludge treatment of municipal wastewater
Bibliographic record
Abstract
Microbial electrolysis cells (MECs) can be used for energy recovery and sludge reduction in wastewater treatment. Electric current density, which represents the rate of wastewater treatment and H 2 production, is not sufficiently high for practical applications of MECs with real wastewater. Here, a sandwiched electrode-stack design was proposed and examined in a continuous-flow MEC system for more than 100 days to demonstrate enhanced electric current generation with a large number of electrode pairs. The current density was boosted up to 190 A/m 3 or 1.4 A/m 2 with 10 electrode pairs stacked in an MEC fed with primary clarifier effluent from a municipal wastewater treatment plant. High organic loading rate (OLR) resulted in high electric current density. The current density increased from 40 to 190 A/m 3 when the OLR increased from 0.5–2 kg-COD/m 3 /day to 8–16 kg-COD/m 3 /day. In continuous-flow operation with two stacked MECs in series, the biochemical oxygen demand (BOD) removal was 90 ± 2% and the chemical oxygen demand (COD) removal was 75 ± 9%. In addition, the sludge production was 0.06 g-volatile suspended solids (VSS)/g-COD removed at a hydraulic retention time of only 0.63 h. The electric energy consumption was low at 0.40 kWh/kg-COD removed (0.058 kWh/m 3 -wastewater treated). The MECs with the stacked electrode design successfully enhanced the electric current generation. The high OLR is important to maintain the high electric current. The organics were removed rapidly and the total suspended solids (TSS) and VSS were reduced substantially in the continuous-flow MEC system. Therefore, the MECs with the stacked electrode design can be used for the rapid and low-sludge treatment of domestic wastewater.
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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) | 0.001 | 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 | 0.001 | 0.000 |
| 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".