Abstract
This dissertation develops and evaluates a model for the effective management of a typical gland service system, utilising engineering management technology, for a wide variety of mining applications to innovatively create a method for the correct implementation and maintenance of a gland service system. A Systematic Engineering approach will organise segmented processes that will focus the design of an Effective Management Model (EMM) for a Gland Service System (GSS). The approach will focus on the optimisation of the GSS which is a fundamental pillar for the functionality of the slurry pumps in a tailings plant.
A Production Performance Model (PPM) will be created utilising the Overall Equipment Effectiveness (OEE) Theory to provide an indication of the production capacity of a tailings plant. The subsystem of the PPM will be the EMM; its core focus is on the optimisation of the Gland Service System, which in turn directly relates the improved performance of the tailings plant which ultimately results in the improved capability of the mines production process.
Today’s competitive environment compels businesses to find ways and means to effectively conduct projects in order to satisfy the ever increasing expectations of clients. Cohesion with Systematic Engineering Approach and Engineering Management theory, while including some mechanical engineering concepts such as manufacturing enhancements and performance optimisation, have been applied in the operational structure of a typical gland service system. The unique case study presented provides an opportunity to practically apply these various management techniques, while referring to mechanical engineering practises, in a mining environment to provide an effective management model for the optimum operation of a Gland Service System. Mines located in various locations within South Africa were utilised in the case study.
Simulink has been applied to model and simulate the environment where these engineering management techniques have been applied. Theories such as OEE were used to create the foundation of the model for the PPM. The subsystem of the PPM is the EMM and Simulink was used to incorporate the optimisation inputs, thus forming a model from Qualitative and Quantitative feedback, using triangulation of the two data sampling systems. The combination of non-probability sampling, and purpose sampling systems formed the basis of the triangulation system.
Together with practical experimentation findings and the participants’ feedback, the EMM basis was constructed.
D.Ing. (Engineering Management)