Pelletization of Refuse-Derived Fuel Fluff to Produce High Quality Feedstock
Bibliographic record
Abstract
Municipal solid waste (MSW) may be a suitable feedstock for thermochemical conversion. Current technologies process the MSW into refuse-derived fuel (RDF) fluff before conversion. Bench- and pilot-scale densification trials were conducted to determine the parameters required to produce a high quality feedstock from the MSW-RDF material in pellet form. The RDF was densified, as well as the biodegradable (paper and wood) fraction of the RDF stream to compare quality of pellets for the two material compositions. A single pelleting trial was conducted to examine the compaction parameters that would produce high quality pellets: sample material, grind size, moisture content, temperature, and pelleting pressure. It was determined that quality pellets, for both materials, were formed at a grind size of 6.35 mm at 16% moisture under pelleting conditions of 90 °C and 4000 N applied load. Pilot-scale pelleting was then completed to emulate industrial pelleting process utilizing the parameters from the single pelleting trials that were deemed to produce quality pellets. All of the samples produced durable pellets (88–94%), with the ash content around 20%. A techno-economic feasibility study determined that 6.35 mm diameter pellets could be produced for an average cost of $38/Mg, although the aggressive process of the size reduction required indicates that it may not be a technically feasible option.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".