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Towards mining 4.0: A novel multi-criteria simulation-optimization fuzzy framework for effective slag utilization overcoming green waste management promotion

Author

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  • Shamsi, Mohammad
  • Zakerinejad, Mohammad

Abstract

The transition to Mining 4.0 requires robust waste management strategies. A critical research gap exists in understanding novel waste utilization techniques and their associated societal implications. This study proposes a comprehensive assessment framework that incorporates criteria of economic, technical, environmental, energy&water, regulations&policy, social, and resiliency. Thirty sustainability indicators were evaluated across nineteen potential slag utilization alternatives. To determine the weights of criteria and indicators, three advanced Fuzzy-Analytical Hierarchy Process (F-AHP) methods including Spherical F-AHP (SF-AHP), interval-valued Pythagorean F-AHP (IVPF-AHP), and randomized weighted F-AHP (RWF-AHP) were compared and the optimal method was identified. Moreover, a novel Fuzzy-Likert Scaling Analysis (FLSA) was proposed to prioritize alternatives. FLSA compared different membership functions (MFs) of triangular, Pythagorean, parabolic, and eighth-order polynomials to find the best MFs. By combining optimized F-AHP, fuzzy-Delphi method (FDM), and FLSA, the framework provides a measurable Slag Management Sustainability Score (SMSS). In a case study examining optimal strategic planning for supplying steel slags, Foolad Mobarakeh Company (FMC) was selected. The most sustainable alternative, “Feed for factories,” achieved an SMSS of 63.633 and 65.049 based on SF-AHP and RWF-AHP, respectively. This framework offers a practical and versatile solution for Mining 4.0, fostering a greener future through sustainable and optimal waste management.

Suggested Citation

  • Shamsi, Mohammad & Zakerinejad, Mohammad, 2025. "Towards mining 4.0: A novel multi-criteria simulation-optimization fuzzy framework for effective slag utilization overcoming green waste management promotion," Technological Forecasting and Social Change, Elsevier, vol. 219(C).
    • Handle: RePEc:eee:tefoso:v:219:y:2025:i:c:s0040162525003026
    DOI: 10.1016/j.techfore.2025.124271
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