Evaluation of guide parameters for batch torrefaction experiments ofrefuse-derived fuel
Bibliographic record
Abstract
Refuse-derived fuel (RDF) is a sustainable energy source that shows a lot of promise in replacing fossil fuels and energy sources.Due to the heterogeneous nature of municipal solid waste and resulting RDF, direct gasification of RDF has seen certain drawbacks in efficiency.Torrefaction is therefore being considered for RDF pre-treatment towards improving the yield of the gasification process.This study involved a series of batch experiments for RDF torrefaction to evaluate the three guide parameters namely, energy yield, mass yield, and energy densification.An oven temperature mapping study is also conducted to establish a standard and repeatable torrefaction methodology.The standard quartering technique is used, before and after grinding to a particle size of <1 mm, to produce reasonably homogenous samples.The batch reactor processed about 21 g of sample for torrefaction at 250℃, 300℃, and 350℃ with 30 minutes residence time in all cases.Sealing and nitrogen purging created the required inert atmosphere for the experiments.Mass yield is evaluated as the ratio between the torrefied RDF to the raw RDF.Energy content values are obtained using a bomb calorimeter.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".