Design and Optimization of a Waste Glass Recycling Network
Bibliographic record
Abstract
Rising sustainability concerns underscore the importance of efficient closed-loop supply chains, especially for energy-intensive materials like glass. Despite being fully recyclable, a significant amount of glass waste ends up in landfills. Recent studies have highlighted the potential of recycled glass as a supplementary cementitious material in concrete production, replacing 10–30% of Portland cement. This substitution can lower CO 2 emissions and mitigate waste challenges but involves trade-offs related to processing costs, environmental benefits, and material performance. This paper presents the first integrated reverse supply chain (RSC) model for Waste Glass (WG) recycling, designed to optimize profit while reducing carbon emissions. The proposed model aims to determine facility sizing, location costs, and reprocessing strategies within the recycling network. The model is validated through a case study with a regional waste recovery firm, offering actionable insights for advancing sustainable glass recycling practices.
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How this classification was reachedexpand
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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".