Generation and storage of gas from waste decomposition in municipal solid waste landfills
Bibliographic record
Abstract
This Ph.D. thesis focuses on optimizing municipal solid waste flows and modeling and managing landfill gas generation from organic wastes. First, it presents a statistical survey of waste flow in New York and Montreal and a calculation of the energy recovery potential of food and yard waste in these cities. The results indicate a low diversion rate from landfills, with significant biogas generation potential from these wastes, contributing to around 2.5% of the energy supply in these cities. Second, it evaluates the current and proposed waste management systems in Montreal, applies a life cycle assessment using the IWM-2 software, and optimizes waste flows using a genetic algorithm to decrease energy consumption, greenhouse gas emissions and costs. The optimized waste flow considers 58% landfilling and shows the importance of further research on landfills. \nThe following chapters study the generation and storage of gas from waste decomposition in municipal solid waste landfills in the province of Quebec, Canada. The fifth chapter addresses the modeling scenarios of landfill gas generation based on a modified first-order decay model. It uses a genetic algorithm to independently fit parameters to methane and hydrogen sulfide generation models. The results show that differentiating more waste types improves the modeling accuracy, and the changes in waste management strategies within a landfill’s decade-long lifetime require various modelling assumptions. Also, the work reveals the importance of considering how different landfill sectors are filled over time. The sixth chapter explores the potential of utilizing stored methane in landfills as an energy source. The study investigates the gas collection system shutdown and restart periods, determining the duration required to maximize collected stored methane. The results show that it takes 0.6 hours to start methane collection and 2.5 hours to reach the maximum collected stored methane. Additionally, the collected stored methane represents 10.5% of landfill gas flow.
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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 | 0.000 |
| 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".