Biochemical Methane Potential of Landfilled Municipal Solid Waste Using a Non-Slurry Approach
Bibliographic record
Abstract
The most widely-used procedure for forecasting landfill methane production in the laboratory is the biochemical methane potential (BMP) assay. Conventional BMP assays for assessing landfilled municipal solid waste (LMSW) use a slurry-based approach which simulates an environment that is predominantly liquid versus a predominance of solids in a landfill, which is likely to misrepresent actual landfill conditions, and could consequently lead to false gas volume predictions. This research was undertaken as a first-step towards modifying the current BMP assays to be more representative of natural landfill conditions termed; the Landfill BMP (LBMP) assay. Three sets of statistically-designed laboratory batch experiments were conducted using organic fraction of MSW to compare the CH4 generation potential (Lo), the rate of CH4 production (Rm) and the first-order rate coefficient (k) values from slurry-phase and solid-phase BMP experiments. The results showed statistically significant differences occurred between slurry-phase and solid phase BMP assays with Lo values obtained from slurry-phase experiments being overestimated by as much as 47 ±12%. Biosolids from Bonnybrook wastewater treatment plant, Calgary, was found to perform poorly compared to a laboratory-derived inoculum. Particle size reduction had a significant effect on Lo and Rm values with smaller particle sizes (< 10 mm) being optimal for CH4 gas production in solid-phase experiments in this study. The Lo values obtained from the LBMP method fell within the range of those obtained from lysimeter and field studies, indicating a possibility of solid-phase BMPs being more likely reliable in forecasting CH4 production from landfills than conventional BMP methods. However, k values were overestimated from both slurry and solid-phase conditions of moisture, suggesting that obtaining k values from laboratory experiments might not be the best approach. The highest coefficient of variation between duplicates in this study was less than 30% indicating good repeatability.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.000 | 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".