Thermogravimetric analysis of refuse-derived fuel and its sorted fractions under torrefaction conditions
Bibliographic record
Abstract
Highlighting the rejects of the rejects, this study focuses on refuse-derived fuels (RDF) generated from non-recyclable municipal solid waste. Unlike wood or coal gasification, RDF in its natural state is a poor fuel source, containing different types of materials, such as paper and plastic, which exhibit distinct chemical properties, thermal properties, and energy content. The efficiency of RDF gasification is limited by its heterogeneity. To enhance the yield and efficiency of the gasification process, torrefaction is being considered as a pre-treatment process. Due to the difficulty of predicting the impact of thermochemical processes on RDF, a monitoring system is required to better understand and control the macro-composition of individual fractions. In this study, RDF fractions are studied independently towards modelling a relation between the percentage composition of each RDF individual fraction and the effects of torrefaction. These include papers, plastics, cardboard, other organics, and fines (<1 cm), which made up more than 90% of the RDF as received from waste sorting centres. Through thermogravimetric analysis, the effects of torrefaction on mass loss have been obtained for all fractions at 200°C, 250°C, 300°C, and 350°C. Using the main RDF fractions, a recombined RDF was produced and torrefied in this study. Instead of a synergistic effect, an additive effect was observed when comparing the mass loss obtained in the recombined RDF to the expected theoretical mass loss computed based on the composition of each fraction and its individual mass losses. From 200°C to 300°C, the experimental and theoretical values closely align, with absolute differences of only 0.74%, 0.04%, and 0.28% at 200°C, 250°C, and 300°C, respectively.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.009 | 0.009 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 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; both teacher heads agree on what is shown here.
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".