Environmental Impact of Pyrolysis of Mixed WEEE Plastics Part 1: Experimental Pyrolysis Data
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Growth in waste electrical and electronic equipment (WEEE) is posing increasing problems of waste management, partly resulting from its plastic content. WEEE plastics include a range of polymers, some of which can be sorted and extracted for recycling. However a nonrecyclable fraction remains containing a mixture of polymers contaminated with other materials, and pyrolysis is a potential means of recovering the energy content of this. In preparation for a life cycle assessment of this option, described in part 2 of this paper set, data were collected from trials using experimental pyrolysis equipment representative of a continuous commercial process operated at 800 °C. The feedstock contained acrylonitrile-butadiene-styrene and high impact polystyrene with high levels of additives, and dense polymers including polyvinylchloride, polycarbonate, polyphenylene oxide, and polymethyl methacrylate. On average 39% was converted to gases, 36% to oils, and 25% remained as residue. About 35% of the gas was methane and 42% carbon monoxide, plus other hydrocarbons, oxygen and carbon dioxide. The oils were almost all aromatic, forming a similar mixture to fuel oil. The residue was mainly carbon with inorganic compounds from the plastic additives and most of the chlorine from the feedstock. The results showed that the process produced around 70% of the original plastic weight as potential fuel.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.007 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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 it