Mining capital cost estimation using Support Vector Regression (SVR)

Hamidreza Nourali and Morteza Osanloo

Resources Policy, 2019, vol. 62, issue C, 527-540

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.

Keywords: CAPEX; Capital cost estimation; Support Vector Regression (SVR); Kernel Ridge Regression (KRR) (search for similar items in EconPapers)
Date: 2019
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (8)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:jrpoli:v:62:y:2019:i:c:p:527-540

DOI: 10.1016/j.resourpol.2018.10.008

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