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Introduction
Coal is one of the major sources of energy, contributing approximately 29% of the total primary energy. This is only after fuel oil, which accounts for 31% (International Energy Agency 2016). Coal is used to generate 41% of the electricity in the world, and this is predicted to increase to 46% by 2030 (Schernikau 2010). Meanwhile, the demand for energy is expected to increase at an average Cumulative Annual Growth Rate (CAGR) of 1.7% per annum in the period between 1990 and 2030 (Schernikau 2010). This provides incentive to increase coal production and start new coal mines. However, new mines cannot be started unless they are protable.
To start a protable new coal mine is challenging as it requires that once the mine begins operation, it produces tonnage for selling in a competitive market with many external factors that can hamper its protability. Suppose a group of surface coal mines located in Queensland, Australia and Mpumalanga, South Africa produce and supply coal to the international market. A xed price for coal of a specied energy content is oered. Each mine has its own production rate based on its coal characteristics, such as the varying thickness in the ground, the caloric value, which is the amount of energy present when the coal is burnt for electricity and price of coal oered by the market. The mines apply dierent technical variables in producing coal, such as the type and quantity of capital and the stripping ratio, which is the quantity of overburden to be removed per tonne of coal extracted. Some of these mines achieve their production target using minimum technical variables 1, while operating with good safety records and managing the impact of the operations to the environment such as that of water pollution. These mines are ecient and cost-eective. They considered competitive and to exhibit best practices.
On the other hand, some mines are inecient. They use excess inputs to achieve their production target, and thus they cannot survive as a competitive business. Assume that a 1This refers to the optimum inputs used to produce target coal tonnages. The operation achieve its target by using less quantity of controllable inputs such as capital spending new mine located in South Africa, say Mine A, is about to start production. It will face the challenge of determining whether it is competitive locally and internationally given its unique characteristics, such as the thickness of the coal in the ground. If mine production starts and is inecient, it will fail to generate a return on investment and will not survive in the business. It remains challenging for a new mine to predict its competitiveness among the existing producers or to identify the best practices and position itself competitively.
1 Introduction
1.1 The process for starting a new mine
1.2 Mining as a turbulent operation
1.2.1 Controllable variables of a new mine
1.2.2 Non-controllable variables of a new mine
1.2.3 Measuring the competitiveness of coal mines
1.3 Research question
1.4 Research design
1.4.1 The model for technical eciency
1.4.2 The predictive models for a new mine
1.5 Research methodology
1.5.1 Modelling
1.5.2 Evaluating the models
1.6 Structure of the thesis
2 Performance and eciency of a new mine project
2.1 Selecting a surface coal mine method for a new project
2.2 Estimating performance for a new mine project
2.2.1 Estimation of production rate for a new mine
2.2.2 Estimation of costs for a new mine
2.3 New mine production planning
2.4 Technical eciency and application in mines
2.4.1 Concept of technical ecienc
2.4.2 Methods of measuring technical eciency
2.4.3 Mathematical representation of the basic DEA models
2.4.4 Previous applications of DEA in coal mine projects
2.5 Conclusion .
3 Source of data and simulation
3.1 Data compilation
3.2 Statistics of data collected and choice of simulation method .
3.3 Simulation of multivariate data for the research
3.4 Secondary attributes of the mining supply systems
3.5 Conclusion
4 Modelling the eciency and performance of a surface coal mine
4.1 Formulation of models for measuring the technical eciency of mining supply
systems
4.2 Combined System for Local and Export (CSLE) model
4.3 Combined System for Local and Export (CSLE) with non-discretionary variables
4.4 Export Coal Mine Supply (ECMS) model .
4.5 Local Coal Mine Supply (LCMS) model
4.6 Models for the improvement to the best practices
4.7 Limitation of the formulated Data Envelopment Analysis (DEA) models
4.8 Conclusion
5 Evaluation of the models for the technical eciency of a mine
5.1 Application of the technical eciency models
5.2 Results and interpretations
5.3 A use case of the models .
5.4 Conclusion
6 Predictive modelling of the eciency and performance of a mine
7 Conclusion
Bibliography
Appendices
