Research on multi-objective recycling network for used electromechanical products
Bibliographic record
Abstract
Used electromechanical products containing hazardous components pose a significant threat to both the environment and human health. Therefore, society must emphasize and explore strategies for constructing a recycling network to enhance the recycling efficiency of used electromechanical products. In this paper, we propose a multi-institutional recycling network that includes recycling centers, reprocessing centers, and disposal centers, with recyclers responsible for the recycling operations. To enhance the effectiveness of this recycling network, we establish three optimization objectives: economic, environmental, and social. We develop a multi-objective mathematical optimization model specifically designed for the recycling network of used electromechanical products. This model aims to assist government entities and recyclers in addressing environmental challenges while simultaneously balancing economic and social impacts. By applying the multi-objective Gray Wolf optimization algorithm, we analyzed the results pertaining to the utilized electromechanical products. A sensitivity analysis was conducted on key parameters, which illuminated the relationships among economic costs, carbon emissions, and employment opportunities, thereby confirming the model’s validity and practicality.Implications: The study of recycling networks for used electromechanical products carries significant economic, environmental, and social implications. Firstly, an efficient recycling network can diminish reliance on primary resources, thereby alleviating the pressures associated with resource scarcity. By recycling and reusing components from used electromechanical products, it directly contributes to the advancement of a circular economy. Secondly, such networks play a critical role in mitigating environmental pollution caused by waste. Furthermore, the establishment of a recycling network for used electromechanical products can give rise to a new industrial chain, encompassing recycling, dismantling, and remanufacturing processes. The development of these interconnected links can lead to the formation of a large-scale industry, subsequently promoting employment opportunities. Finally, the findings of this research align with national strategies, particularly China’s “14th Five-Year” Plan for Circular Economy Development, which identifies waste material recycling as a key objective. The results of this study can assist the country in gradually refining its recycling policies for used electromechanical products and accelerating the establishment of a comprehensive waste materials recycling system.
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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 | 0.007 | 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 | 0.001 | 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".