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Mining capital cost estimation using Support Vector Regression (SVR)

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  • Nourali, Hamidreza
  • Osanloo, Morteza

Abstract

Determination of Capital Expenditure (CAPEX) is a challenging issue for mine designers. Underestimating the capital cost in mining projects may postpone the construction and accordingly the production phases. In addition, overestimating the capital cost may decrease value of the project. Currently available capital cost estimation models cannot predict mining CAPEX in a reliable range of error. Since, current models are not considering all effective parameters other than capacity, annual ore production and waste stripping, they cannot turn out to a reliable result, although they can be used for a rough estimation of CAPEX. In this paper, to estimate the capital cost of mining projects, a model based on Support Vector Regression (SVR) is developed. To establish this model the technical and economic data of 52 open pit porphyry copper mines were collected. Robust design of this model led to negligible error of estimation of CAPEX anticipation procedure. According to the results, the capability of presented model to estimate the mining CAPEX in a wide range of mining capacity is proved. So, as a whole, with a view of evaluation results, this model can be used as a reliable model for estimating of mining CAPEX.

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  • Nourali, Hamidreza & Osanloo, Morteza, 2019. "Mining capital cost estimation using Support Vector Regression (SVR)," Resources Policy, Elsevier, vol. 62(C), pages 527-540.
    • Handle: RePEc:eee:jrpoli:v:62:y:2019:i:c:p:527-540
    DOI: 10.1016/j.resourpol.2018.10.008
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    References listed on IDEAS

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    1. Pohl, Gerhard & Mihaljek, Dubravko, 1992. "Project Evaluation and Uncertainty in Practice: A Statistical Analysis of Rate-of-Return Divergences of 1,015 World Bank Projects," The World Bank Economic Review, World Bank, vol. 6(2), pages 255-277, May.
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    1. 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).
    2. Zheng, Xiaolei & Nguyen, Hoang & Bui, Xuan-Nam, 2021. "Exploring the relation between production factors, ore grades, and life of mine for forecasting mining capital cost through a novel cascade forward neural network-based salp swarm optimization model," Resources Policy, Elsevier, vol. 74(C).
    3. Noriega, Roberto & Pourrahimian, Yashar, 2022. "A systematic review of artificial intelligence and data-driven approaches in strategic open-pit mine planning," Resources Policy, Elsevier, vol. 77(C).
    4. Maryke C. Rademeyer & Richard C. A. Minnitt & Rosemary M. S. Falcon, 2020. "A characterisation of the mechanisms transforming capital investment into productive capacity in mining projects with long lead-times," Mineral Economics, Springer;Raw Materials Group (RMG);Luleå University of Technology, vol. 33(3), pages 349-357, October.
    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).
    6. 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).
    7. Yıldız, Taşkın Deniz, 2023. "Changes in the salaries of mining engineers as they obtain managerial and OHS specialist positions in Turkey: By what criteria can salaries be increased?," Resources Policy, Elsevier, vol. 84(C).
    8. Walsh, Stuart D.C. & Northey, Stephen A. & Huston, David & Yellishetty, Mohan & Czarnota, Karol, 2020. "Bluecap: A geospatial model to assess regional economic-viability for mineral resource development," Resources Policy, Elsevier, vol. 66(C).

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