Numerical modeling of clogging of landfill leachate collection systems with co-disposal of municipal solid waste (MSW) and incinerator ash
Bibliographic record
Abstract
The influence of co-disposal of municipal solid waste (MSW) and incinerator ash used as daily cover on the clogging of leachate collection systems (LCSs) from landfills is examined. The “BioClog” model is used to simulate the fate and transport of the nine leachate constituents most responsible for clogging the LCSs as they move through the porous media. It then calculates the thicknesses of five films that attach to the porous media and the effect of this clog mass–volume on the porosity and hydraulic conductivity of the granular material. Then it models the consequent growth in the leachate mound with increasing clog mass over time until the service life of the LCS is reached. The modeling shows that the concentrated source of leachable minerals in the incinerator ash accelerates the clogging rate and reduces the service life of the LCSs compared to inert daily cover. If an LCS is not designed to accommodate these higher concentrations of cations in the influent leachate during the landfill operating period, the ash can significantly reduce the LCS service life. Means of extending LCS service life are discussed. A practical technique is also utilized to estimate the service life of LCSs with conservative and reasonable agreement with BioClog.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".