Effect of microwave pre-treatment of thickened waste activated sludge on biogas production from co-digestion of organic fraction of municipal solid waste, thickened waste activated sludge and municipal sludge
Bibliographic record
Abstract
Anaerobic co-digestion of organic fraction of municipal solid waste, with thickened waste activated sludge and primary sludge has the potential to enhance biodegradation of solid waste, increase longevity of existing landfills and lead to more sustainable development by improving waste to energy production. This study reports on mesophilic batch and continuous studies using different concentrations and combinations (ratios) of organic fraction of municipal solid waste, thickened waste activated sludge (microwave pre-treated and untreated) and primary sludge to assess the potential for improved biodegradability and specific biogas production. Improvements in specific biogas production for batch assays, with concomitant improvements in total chemical oxygen demand and volatile solid removal, were obtained with organic fraction of municipal solid waste:thickened waste activated sludge:primary sludge mixtures at a ratio of 50:25:25 (with and without thickened waste activated sludge microwave pre-treatment). This combination was used for continuous digester studies. At 15 d hydraulic retention times, the co-digestion of organic fraction of municipal solid waste:organic fraction of municipal solid waste:primary sludge and organic fraction of municipal solid waste:thickened waste activated sludge microwave:primary sludge resulted in a 1.38- and 1.46-fold increase in biogas production and concomitant waste stabilisation when compared with thickened waste activated sludge:primary sludge (50:50) and thickened waste activated sludge microwave:primary sludge (50:50) digestion at the same hydraulic retention times and volumetric volatile solid loading rate, respectively. The digestion of organic fraction of municipal solid waste with primary sludge and thickened waste activated sludge provides beneficial effects that could be implemented at municipal wastewater treatment plants that are operating at loading rates of less than design capacity.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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".