Particle size analysis of municipal solid waste for treatment process modeling
Bibliographic record
Abstract
Several unit operations used in municipal solid waste (MSW) processing facilities are based on physical properties of the waste materials, such as particle size, density and shape. Reliable expressions describing particle size distribution (PSD) of the different waste components present in MSW are not readily available in the context of process modeling. In this study, the characterization data for household wastes and construction and demolition (C&D) wastes were analysed with the purpose of selecting the most representative PSD expression for these waste streams. The Rosin-Rammler distribution was identified over the log-normal and the gamma distributions as the best-fitting PSD for the waste samples. This was demonstrated for both raw and processed waste samples. Parameters were derived and validated for every category of MSW materials considered in the characterization. A model for mixed household waste PSD was developed based on the summation of Rosin-Rammler expressions corresponding to each category of waste materials, as the composition was determined to be the main factor influencing particle size. A simplified model was also derived for mixed waste as a bimodal distribution since two main modes were observed in household waste - one for the "organic" fraction and one for the "inorganic" fraction.
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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) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".