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Sandton, South Africa

Tholana T.,University of Witwatersrand | Musingwini C.,University of Witwatersrand | Njowa G.,Venmyn Rand Pty Ltd
Journal of the Southern African Institute of Mining and Metallurgy | Year: 2013

South Africa hosts some of the world's largest known resources and reserves of minerals that are strategically important to the global economy. The country's mining industry contributes significantly to the national economy in terms of gross domestic product, export earnings, corporate tax receipts, remuneration earnings, and employment. Platinum, gold, and coal are significant minerals because they make the largest individual contributions to these economic indicators. Several factors impact the cash cost performance of operations that mine these minerals, thus affecting the future sustainability of these operations, which are generally 'price-takers'. It is therefore important that the cash cost performance of operations for these key minerals is analysed. Commercially available industry cost curves can be used for such analysis. However, some companies may not be able to afford to purchase these curves. This paper presents a simple Microsoft Excel® algorithm for constructing the curves. It also demonstrates how the constructed industry curves were used to analyse the cash cost performance of South African mining operations for the three selected minerals for the period 2007 to 2011, which included the 2008 global financial crisis. The analysis revealed that the operations were affected by several global and local factors during the review period. The work reported in this paper is part of an MSc research study at the University of the Witwatersrand. © The Southern African Institute of Mining and Metallurgy, 2013.

Clay A.N.,Venmyn Rand Pty Ltd | Myburgh J.A.,Venmyn Rand Pty Ltd | Orford T.C.,Venmyn Rand Pty Ltd | Lemmer C.,Geological and Geostastistical
Journal of the Southern African Institute of Mining and Metallurgy | Year: 2012

In recent times, there has been criticism of the minerals industry over the lack of quantifiable boundaries between Inferred, Indicated, and Measured resources. A recent initiative through the United Nations aims to try to converge the mineral resource classification systems. Since the oil and gas industry uses a probabilistic approach to defining reserve boundaries, it is appropriate to introduce a similar statistical methodology for minerals. The Venmyn Variance Tower has been developed based upon traditional statistics to utilize historical and ongoing information in order to quantify variance of geological and chemical parameters and the boundaries and logic for quantitative mineral resource classification. It is proposed that a less than 50% variance from the mean of all sample parameters is required to achieve the classification threshold to define an Inferred Resource whereas between 20-10% is needed for an Indicated Resource and less than 10% variance from the mean is needed to declare a Measured Resource. These limits are similar to those used by the oil and gas industry, and it is suggested that these thresholds be adopted as an industry standard to ensure consistent quantitative reporting. While this process is intended to use statistics of an orebody to provide quantifiable and defendable boundaries, it cannot be carried out unless the geology of the mineral deposit is understood and the borehole samples can be categorized into appropriate populations for which the statistics are valid. This means that competent geologists are always required to work with and understand the implications of the Variance Tower results. This paper is intended to form the basis of a series of publications that establish a process to take mineral projects along a quantifiable and logical development path. Hence, no specific field example of the practical application of this process is given here. © The Southern African Institute of Mining and Metallurgy, 2012.

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