Plastics Waste Processing: Comminution Size Distribution and Prediction
Bibliographic record
Abstract
Complex industrial and consumer products, such as automobiles, contain significant amounts of potentially recyclable materials, but these materials are not necessarily recovered after their useful life. The current recovery process for automotive hulks manages to capture nearly all of the metal content, but few of the plastics. Except for selected high value plastic parts, most remain with the vehicle and are shredded. Extensive hand dismantling, which is effective, is currently not cost-efficient. However, if materials or components can be designed to separate by their characteristic sizes after comminution, then the material recovery from product waste can be enhanced. Such efforts would support industry design-for-environment and design-for-recycling initiatives. In this study, samples made from acrylonitrile butadiene styrene and polyvinyl chloride were assembled with a variety of thicknesses, configurations, and fastening methods, and were comminuted in two passes through a plastics granulator to determine if they resulted in size-based separation characteristics that could be exploited in the recovery process. A detailed evaluation found that particle size distributions fit a modified Gaudin size distribution relationship well. Of all the sample variations studied, only the granulator exit screen size had a significant impact on the average and distribution of comminuted particle sizes. The pi breakage theory, generally advocated for its use in waste processing, did not hold well in this situation of more complex material configurations. The selection function values were found to decrease with the decreasing size of feed particles.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".