Glass waste circular economy - Advancing to high-value glass sheets recovery using industry 4.0 and 5.0 technologies
Bibliographic record
Abstract
Due to the ongoing development of urban infrastructure and higher living standards, waste glass has become a significant component of municipal solid waste that cannot be disregarded. To address this issue, researchers have been looking for sustainable solutions to establish Circular Economy best practices for the full value chain of products made of glass, particularly for waste from high-quality glass sheets. Unfortunately, most of this waste is either dumped in landfills or recycled in an open-loop manner, due to its diverse composition and low volume. However, with the advent of Industry 4.0 and 5.0 technologies, including machine-to-machine communication, artificial intelligence, Internet of Things, collaborative robots, dynamic life cycle assessment, and advanced remanufacturing techniques, it could now become possible to characterize and recover end-of-life high-quality glass sheets like chemically strengthened glasses, e.g. used in smartphone screens, real time in smart factories for use in other high-tech applications. Such smart factories can economically produce on-demand batch-size 1 products, marking a fundamental transition from conventional recycling methods to a sustainable solution. This paper delves into the integration of Industry 4.0 technologies in glass recycling research and the potential contributions of Industry 5.0, addressing its societal implications. It also introduces an intelligent method to assess and optimize the lifespan of smartphone screen glass, suggesting potential reuse or remanufacturing solutions.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".