Mine production index: Development and application

Detta är en avhandling från Luleå tekniska universitet

Sammanfattning: Assuring production forms a crucial part of mining business profitability. Factors related to various mine operations, activities and business processes can threaten required/planned mine production. To address problems and ensure production level in mining, it is necessary to implement a mine production assurance program (MPA). Since such a guideline does not exist for mining as a process industry, this study started by reviewing four such techniques used in similar industries. These methods include: total productive maintenance, six sigma, a method prescribed by European foundation of quality management, and production assurance program (PAP) used in the oil and gas industry. These methods and techniques were reviewed according to their objectives and applications. Their implementation and achieved success was determined through a literature review and field participation/study. Comparing the tools, techniques and focus with mining productivity and production factors, it was observed that applicability of these methods for mining is limited due to a lack of tools for specific analysis or a lack of consideration of the requirements of mining. However, given certain similarities in objective and methods, PAP from the oil and gas industry may provide some guidance for MPA. As a basis of MPA, an index is required to create a clear relationship between different situations which can occur in mining operation and production loss. A literature review on mining productivity improvement methods shows availability, utilisation and production performance of equipment are the key factors in determining overall production. A single index applicable for chain operation in mining is needed. Overall equipment effectiveness (OEE) which includes these three elements has some limitations for application in mining. A Mine Production index (MPi) is thus proposed. This index involves all three parameters for equipment productivity mentioned above. It also consists of weights for each parameter. The weights in this study are determined through expert opinions/judgements using fuzzy analytical hierarchy process (FAHP). Equipment with low MPi can be labelled as bottlenecks. Weights associated with MPi calculation for bottleneck equipment can point out critical factors in equipment operation. Once bottleneck equipment and relevant critical factors are known, further analysis can be carried out to determine the exact cause of production loss. By using MPi for machine operations, it is possible to rank machines in terms of production effectiveness. When the study applied MPi to chain operations in a mining case study, a crusher was determined as bottleneck equipment. Further root cause analysis and uncertainty detection for bottleneck equipment is also possible, and this forms the basis for MPA.

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