Improving Biomethane Recovery from Municipal and Industrial Wastes
Bibliographic record
Abstract
In Ontario, Canada, landfill capacity is rapidly decreasing, and inadequate waste management has resulted in increased greenhouse gas emissions and leachate volumes. Municipalities are left to design and implement their own organics waste management solution. Energy recovery through anaerobic digestion (AD) is attractive. However, AD can be costly for small and medium-sized communities. Two methods of improving economics of AD are studied in this thesis: co-digestion and pre-treatment of wastes. Making use of industrial wastes can be an excellent method of supplementing AD of municipal wastes. The effect of mixing ratios on methane yield, substrate compatibility, and kinetics were studied for AD of distillery wet cake, source-separated organics (SSOs), and wastewater sludges. Mesophilic AD (37 °C) at an F/M ratio of 0.5 in a batch setup was performed using the AMPTS II unit. The addition of SSOs at higher ratios (50% and 75% VS) in the substrate mix resulted in a 14–15% higher yield per gram COD added, as compared to mono-digestion of wet cake. Mesophilic AD of the stillage and SSO mixtures resulted in a considerable lag phase, implying that degradation kinetics could be improved by acclimation of inoculum. This could help reduce operational costs and overall digestion time. Co-digestion studies revealed compatibility between the substrates, thus making AD a feasible alternative. Microwave (MW) pre-treatment on distillery wet cake was investigated at temperatures of 50 °C, 70 °C, and 90 °C at 480 W and 1080 W, respectively. MW pre-treatment of distillery wet cake did not have a significant effect on the solubilization of COD and biomethane yield. At 480 W, 20–35% decreases in methane production rate were observed. At 1080 W, 22–30% decreases were observed. This suggests the production of phenolic compounds that slowed the degradation of stillage.
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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 | 0.001 |
| 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".