Supplemental information for "Pellet Production from Municipal Solid Wastes Under Various Parametric Conditions in a Cold Canadian Climate"
Bibliographic record
Abstract
In this study, a lab-scale flat plate die mill was used to evaluate the pelletization of three organic municipal solid waste (MSW) streams: digestate from anaerobic processes (DAP), source-separated organics (SSOs), and refuse-derived fuel (RDF). Different particle sizes (4, 8, 12 mm) with moisture contents (MCs) of 10 and 15% were processed through a 6 mm die. RDFs and SSOs demonstrated excellent durability, but DAPs did not. RDFs had a high heating value (HHV) of 21.49 MJ kg-1, twice that of DAPs (10.07 MJ kg-1). The SSOs’ HHV varied seasonally (13.88 MJ kg-1 in spring-summer, 19.47 MJ kg-1 in winter). At a 12 mm particle size, 10 and 15% MC RDF pellets had the lowest throughput capacity (6.99 and 12.20 kg hr-1). Throughput capacity for 10 and 15% MC pelletized SSOs and RDFs decreased with larger particle sizes. The lowest specific energy consumption for 10 and 15% MC pelletized SSO and RDF was at 4 mm (256.99 kJ kg-1) and 8 mm (1432.69 kJ kg-1) particle sizes. Specific energy consumption increased with particle size for 10% MC pellets, except for RDFs. At a 10% MC, RDFs reached a maximum bulk density of 594 kg m-3, while SSO pellets achieved 730 kg m-3. These optimized pelletization parameters are suitable for potential scale-up and future thermochemical conversion research, promoting sustainable use of organic MSW and the circular economy.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.294 | 0.049 |
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".