Navigating the depths of refuse-derived fuel in Canada: From heterogeneity to insightful analysis
Bibliographic record
Abstract
Refuse-derived fuel (RDF) presents inherent heterogeneity, encompassing diverse physical and chemical compositions. This study aims to comprehensively investigate the thermogravimetric characteristics of key RDF fractions and compare them with RDF compositions from Canada and other countries. A representative RDF sample was obtained using three ASTM standard procedures, including the quartering technique, manual sorting, and laboratory preparation. Five major RDF fractions – cardboard (46%), mixed papers (17%), mixed plastics (19%), other organics (3%), and fines (13%) – were manually separated and subjected to cryogenic grinding for characterization analysis. Thermogravimetric characterization at 20°C/min in a nitrogen atmosphere, along with proximate/ultimate analysis and heating value measurements, revealed significant variability in decomposition behavior. DTG analysis showed that LDPE exhibited the highest thermal stability, which peaks at 483.6°C, whereas cardboard and mixed paper underwent single-step decomposition, peaks at 361.4°C, and 359.3°C, respectively. In contrast, mixed plastics, other organics, fines, and raw RDF displayed complex, multi-step decomposition behaviors, underscoring the heterogeneous nature of RDF and informing thermal processing optimization. The Canadian RDF sample showed notably higher cardboard 45% and fines content 13% compared to other studies from different countries averaged 25% cardboard, and 8% fines. Additionally, sorting a 2 kg representative sample required 5 man-hours per kilogram, highlighting the labor-intensive nature of the process.
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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 | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".