The effect of lignocellulosic waste on treatment of municipal wastewater in anaerobic digestion process
Bibliographic record
Abstract
The biological purification of municipal wastewater as an organic waste with a low carbon (C)/nitrogen (N) ratio was explored in the presence of a carbon-rich lignocellulose substrate with a high carbon/nitrogen ratio in this study. The composition of the substrate was optimised to obtain the best biogas yield while maintaining reactor stability. The best composition was modelled and determined using response surface methods and mixture design. The findings revealed that anaerobic co-digestion of municipal wastewater with high-carbon/nitrogen-ratio substrates such as the sugarcane plant and wasted black tea improves biogas generation. This behaviour was observed in reactors with more than 70% (w/w) cane waste during testing in this study. Spent black tea as a co-substrate is suitable for co-digestion with municipal wastewater. However, due to the antibacterial properties of polyphenol and tannins in it, the presence of this substance at a high percentage in combination leads to the loss of useful microorganisms of anaerobic digestion and reduces the biogas production yield. The best substrate composition contains 25% (w/w) lignocellulose waste of sugarcane, 34% (w/w) spent black tea and 41% (w/w) municipal wastewater, which produced 239 ml/g volatile solids of biogas.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".