A method for assessing the performance of sorting unit operations in a material recovery facility based on waste characterizations
Bibliographic record
Abstract
Abstract The determination of the separation efficiencies of mechanical sorting equipment is essential to improve the performance of material recovery facilities (MRFs). However, it is often a challenge to obtain these efficiencies due to the high complexity of MRFs, which often comprise several recirculation streams. In this paper, a methodology to determine the equipment separation efficiencies of a complex MRF is described and applied to an actual sorting centre located in the province of Quebec, Canada. Transfer coefficients for every unit operation and several material types, together with all material flows within the facility, have been determined for an MRF processing a stream of commingled recyclable materials. This work also provides a rare dataset of separation efficiencies for several mechanical sorting unit operations. The methodology is validated by comparing experimental data and model predictions for the recovery and purity of all main output streams. The results contribute to identify several avenues for process performance improvement, like adding a magnetic separation at the beginning of the sorting sequence or improving the separation of the 2D‐type materials collected from the second ballistic separator by changing the operation conditions or adding a quality control step. Moreover, the results also provide valuable information about material recovery and purity to help managers improve the process performance. Finally, a scenario analysis demonstrates that the performance of a second ballistic separator has an important impact on the sorting process's global efficiency and that recirculating a fraction of the rejects output stream has a negligible impact on the global performance.
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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 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".