EVALUATION OF THE ELEMENTAL COMPOSITION OF MUNICIPAL SOLID WASTE BOTTOM ASH: A NEW METHODOLOGY FOR SAMPLE PREPARATION
Bibliographic record
Abstract
The disposal of municipal solid waste (MSW) to thermochemical treatments promotes the formation of ashes as a by-product, which constitutes an important role in the design and operation of energy recovery plants, as the ash can cause corrosion and fouling problems. In this sense, this work analyzed samples of bottom ashes from combustible fractions (organic matter, plastics, textiles, paper/cardboard/Tetra Pak® and sanitary waste) of MSW in natura from Santo André – SP, Brazil. For this, a new methodology for sample preparation was proposed to evaluate the elemental composition of the bottom ashes, for later analysis by Scanning Electron Microscopy with Energy Dispersive X-ray Spectroscopy (SEM-EDS). The obtained data showed a standard deviation of less than 10%, guaranteeing a greater reliability of the results and corroborating with standard deviations presented in the scientific literature. In addition, the elemental composition of the bottom ash was similar between the different combustible fractions evaluated. Keywords: MSW, Combustible fractions, SEM-EDS.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".