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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 | 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 teacher head, 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".