On the Dynamics of Mining Operations in Open Pit Mines

In this paper, mining dynamics is defined as the relationship between the mining rate and movement of mining operations conducted on the benches of a surface mine. This relationship describes the intensity of the pit development in space, in order to meet ore demand at the mill over time. Meeting the mill ore demand is a key factor in optimizing production scheduling in surface mines. Displacement velocity of mining operations within cutbacks, or independent pit units, is introduced in the context of long-term mine planning. Displacement velocity allows the place and time of transition of the mining operations from one independent pit unit to another to be determined as the condition for meeting the mill ore demand. An application using data from Mt Keith Nickel Operations in Western Australia is used to elaborate on the methods presented.

Towards rope shovel automation operation information system

The road to electric rope shovel automation is marked with technological innovations that include an increase in operational information available to mining operations. The CRCMining Shovel Operator Information System not only collects machine operational data but also provides the operator with knowledge-of-performance and influences his/her performance to achieve higher productivity with reduced machine duty. The operator’s behaviour is one of the most important aspects of the man-machine interaction to be considered before semi- or fully-automated shovel systems can be realised. This paper presents the results of the rope shovel studies conducted by CRCMining between 2002 and 2004, provides information on current research to improve shovel performance and briefly discusses the implications of human-system interactions on future designs of autonomous machines.

Data mining and simulation: a grey relationship demonstration

Fuzzy data has grown to be an important factor in data mining. Whenever uncertainty exists, simulation can be used as a model. Simulation is very flexible, although it can involve significant levels of computation. This article discusses fuzzy decision-making using the grey related analysis method. Fuzzy models are expected to better reflect decision-making uncertainty, at some cost in accuracy relative to crisp models. Monte Carlo simulation is used to incorporate experimental levels of uncertainty into the data and to measure the impact of fuzzy decision tree models using categorical data. Results are compared with decision tree models based on crisp continuous data.