Environmental Impact of Pyrolysis of Mixed WEEE Plastics Part 1: Experimental Pyrolysis Data
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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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 | 0.001 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".