The Experts below are selected from a list of 9519 Experts worldwide ranked by ideXlab platform
Erkan Topal - One of the best experts on this subject based on the ideXlab platform.
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application of predictive data mining to create Mine Plan flexibility in the face of geological uncertainty
Resources Policy, 2017Co-Authors: Ajak Duany Ajak, Eric Lilford, Erkan TopalAbstract:Abstract Geological uncertainty represents an inherent threat for all mining projects. Mining operations utilise resource block models as a primary source of data in Planning and in decision making. However, such operational decisions are not free from risk and uncertainty. For the majority of iron ore Mines, as an example, uncertainties such as clay pods and variability in grades and tonnages can have dramatic impacts on projects’ viability. However, a paradigm shift on how uncertainty is treated and a willingness to invest in areas that create operational flexibility can mitigate potential losses. Data analytics is touted as one of the major disruptions in the 21st century and operations that properly utilise data can create real opportunities in the face of an uncertain future. Since organisations have abundant definite geological data, a combination of data mining and real options can provide a competitive advantage. In the present study, predictive data mining algorithms were applied to a real case Mine operation to predict the probability of encountering problematic ore in a mining schedule. The data mining model outputs were used to generate possible real options that the operations could exercise to deal with clay uncertainty. The most suitable data mining algorithm chosen for this task was the classification tree, which predicted the occurrence of clay with 78.6% precision. Poisson distribution and Monte Carlo simulations were applied to analyse various real options. The research revealed that operations could minimise unscheduled losses in the processing Plant and could increase a project's present value by between 12% and 21% if the predictive data mining algorithm was applied to create real options.
Ajak Duany Ajak - One of the best experts on this subject based on the ideXlab platform.
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application of predictive data mining to create Mine Plan flexibility in the face of geological uncertainty
Resources Policy, 2017Co-Authors: Ajak Duany Ajak, Eric Lilford, Erkan TopalAbstract:Abstract Geological uncertainty represents an inherent threat for all mining projects. Mining operations utilise resource block models as a primary source of data in Planning and in decision making. However, such operational decisions are not free from risk and uncertainty. For the majority of iron ore Mines, as an example, uncertainties such as clay pods and variability in grades and tonnages can have dramatic impacts on projects’ viability. However, a paradigm shift on how uncertainty is treated and a willingness to invest in areas that create operational flexibility can mitigate potential losses. Data analytics is touted as one of the major disruptions in the 21st century and operations that properly utilise data can create real opportunities in the face of an uncertain future. Since organisations have abundant definite geological data, a combination of data mining and real options can provide a competitive advantage. In the present study, predictive data mining algorithms were applied to a real case Mine operation to predict the probability of encountering problematic ore in a mining schedule. The data mining model outputs were used to generate possible real options that the operations could exercise to deal with clay uncertainty. The most suitable data mining algorithm chosen for this task was the classification tree, which predicted the occurrence of clay with 78.6% precision. Poisson distribution and Monte Carlo simulations were applied to analyse various real options. The research revealed that operations could minimise unscheduled losses in the processing Plant and could increase a project's present value by between 12% and 21% if the predictive data mining algorithm was applied to create real options.
Eric Lilford - One of the best experts on this subject based on the ideXlab platform.
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application of predictive data mining to create Mine Plan flexibility in the face of geological uncertainty
Resources Policy, 2017Co-Authors: Ajak Duany Ajak, Eric Lilford, Erkan TopalAbstract:Abstract Geological uncertainty represents an inherent threat for all mining projects. Mining operations utilise resource block models as a primary source of data in Planning and in decision making. However, such operational decisions are not free from risk and uncertainty. For the majority of iron ore Mines, as an example, uncertainties such as clay pods and variability in grades and tonnages can have dramatic impacts on projects’ viability. However, a paradigm shift on how uncertainty is treated and a willingness to invest in areas that create operational flexibility can mitigate potential losses. Data analytics is touted as one of the major disruptions in the 21st century and operations that properly utilise data can create real opportunities in the face of an uncertain future. Since organisations have abundant definite geological data, a combination of data mining and real options can provide a competitive advantage. In the present study, predictive data mining algorithms were applied to a real case Mine operation to predict the probability of encountering problematic ore in a mining schedule. The data mining model outputs were used to generate possible real options that the operations could exercise to deal with clay uncertainty. The most suitable data mining algorithm chosen for this task was the classification tree, which predicted the occurrence of clay with 78.6% precision. Poisson distribution and Monte Carlo simulations were applied to analyse various real options. The research revealed that operations could minimise unscheduled losses in the processing Plant and could increase a project's present value by between 12% and 21% if the predictive data mining algorithm was applied to create real options.
H. E. Frimmel - One of the best experts on this subject based on the ideXlab platform.
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Algorithmic Optimization of an Underground Witwatersrand-Type Gold Mine Plan
Natural Resources Research, 2020Co-Authors: G. T. Nwaila, S. E. Zhang, L. C. K. Tolmay, H. E. FrimmelAbstract:In the mining environment, Mine Planning is complicated by the presence of unfavorable environmental conditions, limited knowledge of the shape and size of the deposit, ore body characteristics, and volatile market conditions. In this paper, we propose a top-down algorithmic approach to strategically optimize the cutoff grade and net present value (NPV), and implement its solutions at the operation level, while simultaneously mitigating operation risks, to maximize the life of an ultra-deep gold Mine from the Witwatersrand Basin, South Africa. To date, the Witwatersrand Basin has contributed about 28% of the world’s total gold supply from a series of Mesoarchaean quartz pebble conglomerate units (referred to as reefs). Through a quantitative analysis using algebraic and stochastic methods, we ranked mining variables in terms of their margin sensitivity and impact/adjustability efficacy. The results of this study showed the following. By using our proposed approach, an underground Mine Plan can be optimized by focusing on few key variables. Strategic mining of combinations of high-grade panels with low-grade panels and counter-balancing their risk profiles can yield optimal executable Mine Plan results (i.e., higher NPV, ideal profit margin, and lower risk) without sterilizing a given Mineral resource for underground mining operations.
Jenny Greberg - One of the best experts on this subject based on the ideXlab platform.
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evaluation of the impact of commodity price change on Mine Plan of underground mining
International journal of mining science and technology, 2015Co-Authors: Abubakary Salama, Micah Nehring, Jenny GrebergAbstract:Fluctuations in commodity prices should influence mining operations to continually update and adjust their Mine Plans in order to capture additional value under new market conditions. One of the adjustments is the change in production sequencing. This paper seeks to present a method for quantifying the net present value (NPV) that may be directly attributed to the change in commodity prices. The evaluation is conducted across ten copper price scenarios. Discrete event simulation combined with mixed integer programming was used to attain a viable production strategy and to generate optimal Mine Plans. The analysis indicates that an increase in prices results in an increased in the NPV from $96.57M to $755.65M. In an environment where mining operations must be striving to gain as much value as possible from the rights to exploit a finite resource, it is not appropriate to keep operating under the same Mine Plan if commodity prices alter during the course of operations.