Performance of anaerobic treatment of blackwater collected from different toilet flushing systems: Can we achieve both energy recovery and water conservation?
Bibliographic record
Abstract
Source-diverted blackwater (toilet wastewater) contains most of the organic energy in domestic wastewater and can be treated anaerobically to maximize energy recovery. Blackwater collected from toilets of different water saving options (e.g., conventional, dual and vacuum toilets) represents different characteristics, but their digestibility has not been discussed. In the present study, blackwater collected from different toilet flushing systems were characterized and compared in terms of chemical composition, digestibility and microbial population development during biochemical methane potential (BMP) tests. Interestingly, the highest BMP (48%) was achieved for conventional/dual flush toilet (5–9 L water/flush) blackwaters, whereas vacuum toilet (0.5–1.2 L water/flush) blackwater BMP was only 34%. Elevated free ammonia (FA) concentration (>205 mg L −1 ) appeared to contribute to the reduced digestibility of high-water saving toilet (< 1.5 L water/flush) blackwaters. Methanogenesis was the major FA inhibition step in anaerobic digestion as evident by batch kinetics studies; where Methanosarcina methanogens predominate in all blackwater, but ammonia-tolerance methanogens Methanoculleus and Methanomicrobiales were also predominant in blackwater collected from vacuum toilets. This work underlines that overall measures of sustainability also need to consider blackwater characteristics when designing resource recovery based source-diverted sanitary treatment systems.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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 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".