Stochastic characterisation of a mining production system
Date
2016
Authors
Magagula, Nancy Selemagae
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Abstract
There are discrepancies between deterministic mine planning and the actual mining process
due to geological uncertainties associated with mineral deposits and inherent production
system variabilities. The misalignment between the planning process and the actual mine
production process often leads to non-achievement of production outcomes. Stochastic mine
planning has been developed to minimise these misalignments but it is computationally intense
and requires constraint functions to operate effectively. However, the stochastic mine
planning approaches in literature do not have an embedded process analysing the interactions
between the Key Performance Indicators (KPIs) and the mine production activities.
This dissertation proposes an approach to study the interactions/correlations between KPIs
used to measure the progress of a mining operation and the mining activities. The Multinomial
Logistic Regression (MLR) approach is a non-linear and non-normal measurement
method which can assist in understanding the behaviour of mine production activities when
compared to assessed KPIs. The MLR model can also assist in establishing which production
activities require maximisation or minimisation in attaining the desired KPIs.
This study shows that 71% of the KPIs for a case study in mining production system are
influenced by the movements of the production activities in the mining process and the
level of uncertainty on the forecasted KPIs is reduced through applying the MLR model.
This method will help mining companies in assessing in the initial stages of mine planning
the mine production activities that management should focus on to achieve desired KPIs
by directing more effort and resources to these statistically significant activities.
Description
A dissertation submitted to the Faculty of Engineering and the Built Environment, University
of the Witwatersrand, Johannesburg, in fulfilment of the requirements for the degree
of Master of Science in Engineering.
Johannesburg, 2016
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Citation
Magagula, Nancy Selemagae (2016) Stochastic characterisation of a mining production system, University of the Witwatersrand, Johannesburg, <http://hdl.handle.net/10539/23001>