IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v17y2024i3p718-d1332201.html
   My bibliography  Save this article

Empirical Analysis of Mining Costs Amid Energy Price Volatility for Secondary Deposits in Quarrying

Author

Listed:
  • Michał Patyk

    (Department of Mining Engineering and Work Safety, Faculty of Civil Engineering and Resource Management, AGH University of Krakow, Al. Mickiewicza 30, 30-059 Cracow, Poland)

  • Przemysław Bodziony

    (Department of Mining Engineering and Work Safety, Faculty of Civil Engineering and Resource Management, AGH University of Krakow, Al. Mickiewicza 30, 30-059 Cracow, Poland)

Abstract

The outlined methodology for calculating operating costs in open-cast mines and quarries not only facilitates the selection of optimal mining equipment and systems for working lower-grade secondary deposits but also adds significant value in navigating the challenges of fluctuating prices of energy carriers and fuels. Moreover, the study rigorously assesses the impact of mining operations on the performance of deployed mining equipment and the overall viability of the rock mining project. The selection procedure relies on a comprehensive analysis of the technical and economic parameters of selected solutions, providing critical insights to guide decisions regarding the continuation or discontinuation of mining operations. We analyse, based on empirical data, the technical and economic parameters of several variants of mining equipment to be used for the extraction of rocks and stones from secondary deposits in conditions of fluctuation depending on the level of energy prices, in order to find the best configuration in terms of operating costs and potential revenue. In addition to analysing the structure of operating costs, the article presents their correlation with the required profit from the sale of raw materials using the linear correlation method. The results clearly demonstrate the economic viability of mining secondary deposits, taking into account the actual costs incurred by mining companies.

Suggested Citation

  • Michał Patyk & Przemysław Bodziony, 2024. "Empirical Analysis of Mining Costs Amid Energy Price Volatility for Secondary Deposits in Quarrying," Energies, MDPI, vol. 17(3), pages 1-19, February.
    • Handle: RePEc:gam:jeners:v:17:y:2024:i:3:p:718-:d:1332201
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/17/3/718/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/17/3/718/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Sheo Shankar Rai & Vemavarapu M. S. R. Murthy & Nalamasa Sukesh & Akkiraju Sairam Teja & Simit Raval, 2021. "Operational efficiency of equipment system drives environmental and economic performance of surface coal mining—A sustainable development approach," Sustainable Development, John Wiley & Sons, Ltd., vol. 29(1), pages 25-44, January.
    2. Stenis, Jan & Hogland, William, 2011. "Optimization of mining by application of the equality principle," Resources Policy, Elsevier, vol. 36(3), pages 285-292, September.
    3. 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).
    4. Dehghani, Hesam & Ataee-pour, Majid, 2012. "Determination of the effect of operating cost uncertainty on mining project evaluation," Resources Policy, Elsevier, vol. 37(1), pages 109-117.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Zbigniew Krysa & Przemysław Bodziony & Michał Patyk, 2024. "Exploitation of Mineral Resources in Conditions of Volatile Energy Prices: Technical and Economic Analysis of Low-Quality Deposits," Energies, MDPI, vol. 17(14), pages 1-19, July.
    2. Yıldız, Taşkın Deniz, 2022. "Considering the recent increase in license fees in Turkey, how can the negative effect of the fees on the mining operating costs be reduced?," Resources Policy, Elsevier, vol. 77(C).
    3. Burlakovs, Juris & Kriipsalu, Mait & Klavins, Maris & Bhatnagar, Amit & Vincevica-Gaile, Zane & Stenis, Jan & Jani, Yahya & Mykhaylenko, Valeriy & Denafas, Gintaras & Turkadze, Tsitsino & Hogland, Mar, 2017. "Paradigms on landfill mining: From dump site scavenging to ecosystem services revitalization," Resources, Conservation & Recycling, Elsevier, vol. 123(C), pages 73-84.
    4. Odai Y. Dweekat & Sarah S. Lam & Lindsay McGrath, 2023. "An Integrated System of Braden Scale and Random Forest Using Real-Time Diagnoses to Predict When Hospital-Acquired Pressure Injuries (Bedsores) Occur," IJERPH, MDPI, vol. 20(6), pages 1-18, March.
    5. Kamel, Ahmed & Elwageeh, Mohamed & Bonduà, Stefano & Elkarmoty, Mohamed, 2023. "Evaluation of mining projects subjected to economic uncertainties using the Monte Carlo simulation and the binomial tree method: Case study in a phosphate mine in Egypt," Resources Policy, Elsevier, vol. 80(C).
    6. 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).
    7. Rahimi, Esmaeil & Ghasemzadeh, Hasan, 2015. "A new algorithm to determine optimum cut-off grades considering technical, economical, environmental and social aspects," Resources Policy, Elsevier, vol. 46(P1), pages 51-63.
    8. 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).
    9. Díaz, Juan D. & Hansen, Erwin & Cabrera, Gabriel, 2020. "A random walk through the trees: Forecasting copper prices using decision learning methods," Resources Policy, Elsevier, vol. 69(C).
    10. Dehghani, Hesam & Bogdanovic, Dejan, 2018. "Copper price estimation using bat algorithm," Resources Policy, Elsevier, vol. 55(C), pages 55-61.
    11. Jiao Liu & Shuang Li & Weijun Bao & Kun Xu, 2022. "Could the Management System of Safety Partnership Change Miners’ Unsafe Behavior?," Sustainability, MDPI, vol. 14(20), pages 1-14, October.
    12. Kuangyuan Zhang & Richard Olawoyin & Antonio Nieto & Andrew N. Kleit, 2018. "Risk of commodity price, production cost and time to build in resource economics," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 20(6), pages 2521-2544, December.
    13. 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).
    14. Savolainen, Jyrki, 2016. "Real options in metal mining project valuation: Review of literature," Resources Policy, Elsevier, vol. 50(C), pages 49-65.
    15. Yıldız, Taşkın Deniz, 2022. "Supervisor fund expectation for the guarantee of salaries in the presence of the effect of permanent supervisor salaries on mining operating costs in Turkey," Resources Policy, Elsevier, vol. 77(C).
    16. Parviz Sohrabi & Hesam Dehghani & Behshad Jodeiri Shokri, 2022. "Determination of optimal production rate under price uncertainty—Sari Gunay gold mine, Iran," Mineral Economics, Springer;Raw Materials Group (RMG);Luleå University of Technology, vol. 35(2), pages 187-201, June.
    17. Wang, Chao & Zhang, Xinyi & Wang, Minggang & Lim, Ming K. & Ghadimi, Pezhman, 2019. "Predictive analytics of the copper spot price by utilizing complex network and artificial neural network techniques," Resources Policy, Elsevier, vol. 63(C), pages 1-1.
    18. Michał Patyk & Przemysław Bodziony, 2022. "Application of the Analytical Hierarchy Process to Select the Most Appropriate Mining Equipment for the Exploitation of Secondary Deposits," Energies, MDPI, vol. 15(16), pages 1-16, August.
    19. Evatt, Geoffrey William & Soltan, Mousa Omid & Johnson, Paul V., 2012. "Mineral reserves under price uncertainty," Resources Policy, Elsevier, vol. 37(3), pages 340-345.
    20. Fan, Ying & Mo, Jian-Lei & Zhu, Lei, 2013. "Evaluating coal bed methane investment in China based on a real options model," Resources Policy, Elsevier, vol. 38(1), pages 50-59.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jeners:v:17:y:2024:i:3:p:718-:d:1332201. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.