Competitiveness and performance prediction of surface coal Mining Engineering

Abstract

The purpose of this research is to formulate mathematical models for assisting the management of either a new or operating surface coal mine to assess its competitiveness relative to other coal producers for a given market of thermal coal. As an alternative of e ciency measurement to provide a new way to assess better the competitiveness of surface mining, Data Envelopment Analysis (DEA) method is proposed. DEA uses linear programming to determine the relative e ciencies of (competing) mines, each referred to as a Decision Making Unit (DMU). In this research, the methodology applied involves three stages: First, applying DEA to formulate the mathematical models basing on the structure of coal extraction, processing and supply to the markets. Second, evaluate the models performance and illustrate the use case, and thirdly develop predictive model for the e ciency and performance of a new mine. Three DEA models were developed, each representing a speci c con guration of extraction, processing and sale of coal to the markets. The main model, referred to as Combined System for Local and Export (CSLE), supplied both the local and export markets. Two special cases, referred to as Local Coal Mine Supply (LCMS) and Export Coal Mine Supply (ECMS) respectively, looked at the individual markets in isolation. The results from the numerical illustrations of the application of the DEA models showed that the models were able to discriminate between the e cient (best practice) and ine cient mines. This provides a quantitative measure that mining companies can use to benchmark themselves against other competitors in a multi-dimensional manner. Also, the proposed method allows for generating realistic, quantitative targets for those DMUs that are considered ine cient. After formulating the three DEA models, use cases are presented for the CSLE model to demonstrate the signi cance of the proposed model for decisions making. Predictive models for technical e ciency and mine performance developed in this thesis, target new mining operations wanting to enter the market. A statistical method known as supervised learning was employed in this case. It was found that the predictor variables in the model can only explain 54.5% of the variation in technical e ciency. To test the prediction accuracy, the mining entities were separated into training and test sets. On the test set, the model predicted e ciency scores within 20% of the actual (known) values. To improve the performance of this model, this thesis suggests investigating the in uence of qualitative variables on mining e ciency. Such qualitative variables may include worker morale, work satisfaction and salary disputes. Mine planning is non-trivial as it requires various perspectives and involves the interdependence of many variables with di erent units of measure. This research is signi cant as it provides mining management with a sound and rigorous model to handle the multiplexity of the decision variables. The quantitative approach provides for evidence-based decision support where large capital amounts are at risk. Mine planning parameters can be evaluated taking the mine's particularities into account before proceeding to the production stage. The DEA approach is useful both for current mining operations to evaluate its competitiveness in given markets, as well as new mining operations who need to anticipate the type and quantity of capital to invest given their project characteristics. Therefore, the mine management can use the models to determine the optimal technical inputs such as capital, labour and the stripping ratio while considering mine-speci c challenges that in uence the competitiveness of the project, such as the location of the mine from the market and coal seam thickness that can not be controlled.