Abstract

SEBUTSOE, T.C.  and  MUSINGWINI, C.. Characterizing a mining production system for decision-making purposes in a platinum mine. J. S. Afr. Inst. Min. Metall. [online]. 2017, vol.117, n.2, pp.199-206. ISSN 2411-9717.  http://dx.doi.org/10.17159/2411-9717/2017/v117n2a11.

The primary objective of any mining business unit is to make profit by extracting, processing, and selling minerals from a particular mineral deposit. It is important to optimize extraction of the mineral resource given time, capital, and space constraints. Mineral extraction is often associated with uncertainty due to variable technical and human factors. Technical factors such as grade distribution, ground conditions, and equipment reliability influence the performance of a mining production system (MPS). The performance of the MPS is also impacted by human factors such as employee skills, health, and attendance. Uncertainty associated with technical and human factors often leads to actual output differing from what was planned. Therefore an in-depth analysis of the significant causes of deviations from the planned outcomes is relevant. In this paper we investigate the empirical relationships between inputs and outputs in a MPS in order to assist management in directing efforts at key production drivers. Once a MPS has been empirically characterized, more effort and resources can be focused on the key decision-making variables (DMVs) in order to meet the planned outcomes. For a typical underground, tabular, narrow-reef conventional drill-and-blast platinum mine the face advance, face length mined, number of teams, team efficiencies, and team size have a statistically significant relationship with the centares (m²) produced, which is a key performance indicator (KPI). The production function derived in this study can be used to align the physical, technical, and human factors together to predict the optimal output level. The most significant production lever of the MPS is the face advance.

Keywords : mining production system (MPS); productivity; key production drivers; decision-making variables (DMVs); optimization.

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