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Mine-to-crusher policy: Planning of mine blasting patterns for environmentally friendly and optimum fragmentation using Monte Carlo simulation-based multi-objective grey wolf optimization approach

Author

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  • Hosseini, Shahab
  • Mousavi, Amin
  • Monjezi, Masoud
  • Khandelwal, Manoj

Abstract

The quality of rock fragmentation intensively affects downstream operations and operational costs. Besides, Environmental side effects are inevitable due to mine blasting despite improvements in blasting consequences such as fly-rock and back-break. This study concentrates on optimizing mine blasting patterns for environmentally friendly mineral production and minimizing operational costs by achieving environmental-oriented and economic objectives-based on a new framework using artificial intelligence techniques. A gene expression programming (GEP) based on Monte Carlo simulations (MCs) denoted that rock size distribution can be modeled and predicted without any uncertainty. Four main objectives involving operational costs, back-break, fly-rock, and toe volume were highlighted for minimizing in the optimization framework. The multi-objective model was implemented by applying it to a running mine and solved using the grey wolf optimization algorithm. As optimizing, 17 optimal blasting plans were achieved to implement in the different rock types. The multi-objective model was able to reduce mine to crusher cost as well as undesirable blasting consequences considerable favourite of mining managers.

Suggested Citation

  • Hosseini, Shahab & Mousavi, Amin & Monjezi, Masoud & Khandelwal, Manoj, 2022. "Mine-to-crusher policy: Planning of mine blasting patterns for environmentally friendly and optimum fragmentation using Monte Carlo simulation-based multi-objective grey wolf optimization approach," Resources Policy, Elsevier, vol. 79(C).
    • Handle: RePEc:eee:jrpoli:v:79:y:2022:i:c:s030142072200530x
    DOI: 10.1016/j.resourpol.2022.103087
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    References listed on IDEAS

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    1. Jiskani, Izhar Mithal & Yasli, Fatma & Hosseini, Shahab & Rehman, Atta Ur & Uddin, Salah, 2022. "Improved Z-number based fuzzy fault tree approach to analyze health and safety risks in surface mines," Resources Policy, Elsevier, vol. 76(C).
    2. Jiskani, Izhar Mithal & Cai, Qingxiang & Zhou, Wei & Ali Shah, Syed Ahsan, 2021. "Green and climate-smart mining: A framework to analyze open-pit mines for cleaner mineral production," Resources Policy, Elsevier, vol. 71(C).
    3. Huaiting Luo & Wei Zhou & Izhar Mithal Jiskani & Zhiming Wang, 2021. "Analyzing Characteristics of Particulate Matter Pollution in Open-Pit Coal Mines: Implications for Green Mining," Energies, MDPI, vol. 14(9), pages 1-19, May.
    4. Guo, Hongquan & Nguyen, Hoang & Vu, Diep-Anh & Bui, Xuan-Nam, 2021. "Forecasting mining capital cost for open-pit mining projects based on artificial neural network approach," Resources Policy, Elsevier, vol. 74(C).
    5. Zhang, Hong & Nguyen, Hoang & Bui, Xuan-Nam & Nguyen-Thoi, Trung & Bui, Thu-Thuy & Nguyen, Nga & Vu, Diep-Anh & Mahesh, Vinyas & Moayedi, Hossein, 2020. "Developing a novel artificial intelligence model to estimate the capital cost of mining projects using deep neural network-based ant colony optimization algorithm," Resources Policy, Elsevier, vol. 66(C).
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    Cited by:

    1. Zhao, Jue & Hosseini, Shahab & Chen, Qinyang & Jahed Armaghani, Danial, 2023. "Super learner ensemble model: A novel approach for predicting monthly copper price in future," Resources Policy, Elsevier, vol. 85(PB).
    2. Xianan Wang & Shahab Hosseini & Danial Jahed Armaghani & Edy Tonnizam Mohamad, 2023. "Data-Driven Optimized Artificial Neural Network Technique for Prediction of Flyrock Induced by Boulder Blasting," Mathematics, MDPI, vol. 11(10), pages 1-22, May.

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