Waste to emerging and sustainable wealth: An integrated mining 4.0-recycling 4.0-decision making 4.0 framework overcoming greener red mud recycling technologies promotion
Bibliographic record
Abstract
This study presents a comprehensive, three-phase framework for selecting an optimal technological solution for repurposing Red Mud (RM) based on a green mining approach. The framework introduces a novel ZE-FSIWEC method in Phase 1 for Mining 4.0 criteria weighting. In Phase 2, Fuzzy-Delphi method (FDM) screens key Recycling 4.0 alternatives, followed by a combination of eight multi-criteria decision-making (MCDM) methods including ARLON (2024), RAWEC (2024), MARA (2022), COBRA (2022), RAFSI, MAIRCA, MABAC, and ARAS for ranking. To address uncertainties, enhance decision reliability, and improve group decision credibility, fuzzy ZE-numbers are merged with Decision-Making 4.0 methods, which are then consolidated using Borda-count and Copeland ranking for a robust assessment. Iran was chosen as the analysis subject, with Phase 1 evaluating the national participants' perspectives, and Phase 2 focusing on the local site participants' viewpoints. In the final phase, the framework culminates in the development of a quantifiable RM Management Sustainability Score (RMMSS) to identify the most suitable strategic supply planning of RM residues among different Recycling 4.0 technologies and enhance mining waste management standards. Therefore, the proposed tech-paradigm based framework demonstrates its efficiency in a scenario where mining enterprises aim to market their RM. Sensitivity analysis shows the reliability of ZE-FSIWEC-ZE-RAWEC method, with a Spearman's correlation over 86.9 %, making it promising for future research. The Jajarm alumina complex case study showcases the framework's remarkable impact. This framework demonstrates its practicality and region-independent adaptability, contributing to a greener future by promoting sustainable practices and transforming hazardous RM waste into valuable assets. • Evaluating 15 different Recycling 4.0 technologies ranks for cleaner RM consumption. • Determining 18 various Mining 4.0 criteria weights using a hybrid ZE-FSIWEC method. • Embedding Decision-Making 4.0 across all framework phases considering uncertainty-reliability-group decision credibility. • Introducing a quantifiable RMMSS and certifications for greener mining waste management. • Consolidating 8 ZE-MCDMs using Borda-count & Copeland ranking for optimal RM utilization.
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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 | 0.006 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".