Thesis (M.S.)--West Virginia University, 2007. / Title from document title page. Document formatted into pages; contains xii, 145 p. : ill. (some col.). Includes abstract. Includes bibliographical references (p. 129-132).

Thesis (Ph.D.) - University of Queensland, 2005. / Includes bibliography.

Thesis (M.S.)--West Virginia University, 2005. / Title from document title page. Document formatted into pages; contains xi, 65 p. : ill. (some col.). Vita. Includes abstract. Includes bibliographical references (p. 63-64).

Thesis (M.S.)--University of Missouri, School of Mines and Metallurgy, 1947. / The entire thesis text is included in file. Typescript. Title from title screen of thesis/dissertation PDF file (viewed July 9, 2010) Includes bibliographical references (p. 48).

Thesis (Professional Degree)--University of Missouri, School of Mines and Metallurgy, 1939. / The entire thesis text is included in file. Typescript. Illustrated by author. Title from title screen of thesis/dissertation PDF file (viewed March 25, 2010) Includes bibliographical references (p. 123).

Thesis (Ph. D.)--West Virginia University, 2006. / Title from document title page. Document formatted into pages; contains xviii, 193 p. : ill. (some col.). Includes abstract. Includes bibliographical references (p. 191-193).

M.Phil. / In an ever - increasing competitive business world it is essential to optimise the replacement of expensive mining equipment. The decisions regarding the replacement of assets used in a coal mine are usually based on life cycle costing models. Financial methods such as Net Present Value, Internal Rate of Return or Payback are applied to determine the feasibility of replacement of the asset. Whereas these methods and other models such as life cycle costing, challenger / defender and the Non-Homogeneous Poisson Process models can be applied in most cases, it is deemed to be insufficient as the sole decision making tool for the replacement of mining equipment. The development of another tool to assist in the decision making process is required for specific use by the engineer to be used in conjunction with the traditional financial models. Key performance indicators are used extensively in the mining industry to manage the performance of equipment and are deemed to be essential components in achieving the organisation's objectives. There are certain limitations when using only the traditional financial life cycle costing methods when viewed from the engineer's perspective, since they do not directly incorporate the level of the maintenance function and the performance effectiveness of the asset. The engineer usually requires more insight into the performance of the asset to assess the feasibility of replacement, hence the need for an additional tool that incorporates these key performance indicators. In most cases there are relationships between the various key performance indicators themselves as well as between them and the operating and maintenance cost of the asset. The key performance indicators used are availability, reliability (mean time to failure), maintainability (mean time to repair) and the operability (tons per direct operating hour). There are certain factors that may lead to the excessive operating and maintenance cost of an asset, especially if there is no investigation into the reasons for the excessive cost. Examples are sub-standard maintenance practices and an insufficient level of service from suppliers. Both are issues that can be resolved with a consequent decrease in the cost of ownership of the asset. Cost as the only indicator of the feasibility of replacement may therefore not be a true reflection of the real status of the performance of the asset. Weighting factors are used to allocate values to the key performance indicators in terms of their contribution towards achieving the organisational objectives. The equipment effectiveness is derived from these values to give an indication of how well the equipment is performing against predetermined benchmarks. This dissertation attempts tb find a solution to the problem through the use of the key performance indicators in addition to the existing models that focus on the financial aspect in order to provide a more accurate assessment of the replacement requirement of an asset in an opencast coal mine.

Recently, competition has increased in the surface coal mining industry, which has necessitated the development of more efficient methods for coal removal. Despite this trend, very little emphasis has been placed on the development of optimization techniques applicable to the surface coal industry. The available methods are inadequate in that they recognize neither the complex equipment interactions present in a surface mining operation nor the interdependence of overburden removal and spoil placement. The lack of available techniques prompted the development of a mixed integer model to optimize the scheduling of equipment and the distribution of overburden in a typical mountaintop removal operation. Using this format, a (0-1) integer model and transportation model were constructed to determine the optimal equipment schedule and optimal overburden distribution, respectively. To solve this mixed integer program, the model was partitioned into its binary and real-valued components. Each problem was successively solved and their values added to form estimates of the value of the mixed integer program. Optimal convergence was indicated when the difference between two successive estimates satisfied some pre-specified accuracy value. The performance of the mixed integer model was tested against actual field data to determine its practical applications. To provide the necessary input information, production data was obtained from a single seam, mountaintop removal operation located in the Appalachian coalfield. As a means of analyzing the resultant equipment schedule, the total idle time was calculated for each machine type and each lift location. Also, the final overburden assignments were analyzed by determining the distribution of spoil material for various overburden removal productivities. Subsequent validation of the mixed integer model was conducted in two distinct areas. The first dealt with changes in algorithmic data and their effects on the optimality of the model. The second area concerned variations in problem structure, specifically those dealing with changes in problem size and other user-inputted values, such as equipment productivities or required reclamation. For each of these optimal schedules and assignments obtained from the model, analyses were conducted in manner similar to that discussed above. / Ph. D.