Catalyzing Refuse-Derived Fuel Understanding: Quantified Insights From Thermogravimetric Analysis
Bibliographic record
Abstract
Abstract This study employs thermogravimetric analysis (TGA) to investigate the thermal degradation behavior of various components of refuse-derived fuel (RDF). The analysis is conducted individually for different RDF fractions, including cardboard, mixed papers, mixed plastics, other organics, and fines, alongside raw RDF. TGA experiments are performed in triplicate to ensure repeatability and homogeneity assessment. The results reveal distinct degradation profiles for each material, influenced by moisture content. Cardboard and mixed papers exhibit similar decomposition characteristics attributed to their cellulose content. Cardboard undergoes initial moisture-driven mass loss (5.52%), followed by cellulose and hemicellulose decomposition (58.86%) at 250–400 °C and lignin degradation (10.1%) at 400–500 °C. In contrast, mixed plastics, with an initial moisture content of 0.81%, manifest multiple decomposition steps: polyvinyl chloride (PVC) degradation (3.84%) at 200–335 °C, polystyrene (PS) degradation (6.63%) at 335–400 °C, polypropylene (PP) degradation (24.41%) at 400–450 °C, and high-density polyethylene (HDPE)/low-density polyethylene (LDPE) degradation (54.6%) at 400–500 °C. Other organics, with 1.47% initial moisture content, undergo cellulose decomposition (37.98%) at 200–381 °C and polyester/microfilament degradation (21.3%) at 381–450 °C. Fines display cellulose and hemicellulose decomposition (29.8%) at 200–383 °C and plastics/polyester degradation (43%) at 383–550 °C. LDPE in mixed plastics undergoes pure polymer decomposition at 483.6 °C.
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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 | 0.001 | 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".