Enhancing mine risk assessment through more accurate reproduction of correlations and interactions between uncertain var
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ORIGINAL PAPER
Enhancing mine risk assessment through more accurate reproduction of correlations and interactions between uncertain variables Aldin Ardian 1,2 & Mustafa Kumral 1 Received: 28 April 2020 / Accepted: 1 September 2020 # Springer-Verlag GmbH Germany, part of Springer Nature 2020
Abstract Risk is a significant phenomenon in mineral industries due to several associated social, environmental, technical, and financial uncertainties. Risk assessment is a standard procedure that evaluates the effects of uncertainties on a mining project. To deal with technical and financial uncertainties, the most well-known risk assessment technique is the Monte Carlo simulation (MCS), which requires reproducing correlations between uncertain variables. Correlation does not imply causation, but it does provide information regarding how uncertain variables interact. Given that samples generated in MCS are used in a transfer function (e.g., to produce net present value), transfer function values may mislead risk assessors if the interactions are not reproduced. This study uses historical reference data to compare MCS outcomes based on Pearson and copula correlations with regard to their ability to reproduce interactions. Furthermore, results from a case study on a gold mining project—including gold price, production cost, grade, and recovery as well as interest rate as uncertain parameters—show that if the associations between the variables are non-linear, copulas capture interactions and correlations more accurately than Pearson. Keywords Copula . Pearson . Correlation . Discounted cash flow . Mine project evaluation . Interactions . Design of experiments JEL classification A23 . C15 . C90 . D81 . L72 . and O22
Introduction A key challenge in mine project evaluation is to deal with social, environmental, technical, and financial uncertainties. Social and environmental issues are paramount: risk tolerance under any circumstance is low due to ethical and legal concerns. Therefore, this paper focuses on technical and financial uncertainties, which have a significant impact on a project’s cash flow (Dimitrakopoulos 2018; Groeneveld and Topal 2011; Martinez 2009; Lishchuk and Pettersson 2020).
* Mustafa Kumral [email protected] 1
Department of Mining and Materials Engineering, McGill University, 3450 rue University, Montreal, Quebec H3A 0E8, Canada
2
Department of Mining Engineering, Universitas Pembangunan Nasional “Veteran” Yogyakarta, Jalan Ring Road Utara, Yogyakarta 55283, Indonesia
Risk quantification in mineral industries can be classified into approaches based on the following: (1) stochastic optimization techniques, such as stochastic optimization with recourse, chance-constrained programming, robust optimization, stochastic dynamic programming, and fuzzy programming (Zimmermann 1978), generating one solution to the problem (García and Guzmán 2019; Higle 2005); and (2) the Monte Carlo simulation (MCS), generating a risk profile for a given project (Sauvageau and Kumral 2018; Ugwuegbu 2013). MCS is a standar
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