Optimization of landfill gas generation based on a modified first-order decay model: A case study in Quebec province
Bibliographic record
Abstract
Abstract Landfills will likely remain an essential part of integrated solid waste management systems in many developed and developing countries for the foreseeable future. This paper uses a genetic algorithm to fit parameters to a CH 4 and H 2 S generation model independently based on a modified first-order decay model. In the case of CH 4 generation modeling, biodegradable organic waste (OW) was segregated into food waste, yard waste, paper, and wood. In addition to optimizing the OW fractions, key modeling parameters of OW, such as CH 4 generation potential (\({L}_{0}\)) and CH 4 decay rate (\({k}_{C{H}_{4}}\)), were determined independently for different periods in the life of the landfill. Similarly, in the case of H 2 S generation modeling, the construction and demolition waste (CD) was classified into fines (FCD) and bulky materials (BCD), and H 2 S generation potential (\({S}_{0}\)) and H 2 S decay rate (\({k}_{{H}_{2}S}\)) of FCD and BCD were determined. Landfill gas (LFG) collection data from a site in Quebec province (Canada) was used to validate the LFG generation model. A range of scenarios was analyzed using the validated model, including twelve scenarios for CH 4 and two for H 2 S modeling, respectively. The results showed that the differentiation of more waste types improves the modeling accuracy for CH 4 . Moreover, within the decade-long lifetime of a landfill, the waste management strategies change, requiring different assumptions for the modeling. Also, the work showed the importance of considering how different sectors of a landfill are filled over time. Finally, scenario twelve, which assumed four waste types, constant three periodic waste fractions, and six sectors, had the lowest residual sum of squares (RSS) value. For H 2 S generation modeling, both scenarios, with or without separate fits of \({S}_{0}\) and \({k}_{{H}_{2}S}\) for FCD and BCD, predicted the generated H 2 S well and had a very similar RSS value. Further data could improve H 2 S generation modeling.
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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 | 0.001 | 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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 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".