Scielo RSS<![CDATA[Journal of the Southern African Institute of Mining and Metallurgy]]>
http://www.scielo.org.za/rss.php?pid=0038-223X20080002&lang=en
vol. 108 num. 2 lang. en<![CDATA[SciELO Logo]]>http://www.scielo.org.za/img/en/fbpelogp.gif
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<![CDATA[<b>Investigating continuous time open pit dynamics</b>]]>
http://www.scielo.org.za/scielo.php?script=sci_arttext&pid=S0038-223X2008000200001&lng=en&nrm=iso&tlng=en
Current mine production planning, scheduling, and allocation of resources are based on mathematical programming models. In practice, the optimized solution cannot be attained without examining all possible combinations and permutations of the extraction sequence. Operations research methods have limited applications in large-scale surface mining operations because the number of variables becomes too large. The primary objective of this study is to develop and implement a hybrid simulation framework for the open pit scheduling problem. The paper investigates the dynamics of open pit geometry and the subsequent material movement as a continuous system described by time-dependent differential equations. The continuous open pit simulator (COPS) implemented in MATLAB, based on modified elliptical frustum is used to model the evolution of open pit geometry in time and space. Discrete open pit simulator (DOPS) mimics the periodic expansion of the open pit layouts. Function approximation of the discrete simulated push-backs provides the means to convert the set of partial differential equations (PDEs), capturing the dynamics of open pit layouts, to a system of ordinary differential equations (ODEs). Numerical integration with the Runge-Kutta scheme yields the trajectory of the pit geometry over time with the respective volume of materials and the net present value (NPV) of the mining operation. A case study of an iron ore mine with 114 000 blocks was carried out to verify and validate the model. The optimized pit limit was designed using Lerchs-Grossman's algorithm. The best-case annual schedule, generated by the shells node in Whittle Four-X yielded an NPV of $449 million over a 21-year mine life at a discount rate of 10% per annum. DOPS best scenario out of 2 500 simulation iterations resulted in an NPV of $443 million and COPS yielded an NPV of $440 million over the same time span. The hybrid simulation model is the basis for future research using reinforcement learning based on goal-directed intelligent agents.<![CDATA[<b>Application of fuzzy set theory in the selection of underground mining method</b>]]>
http://www.scielo.org.za/scielo.php?script=sci_arttext&pid=S0038-223X2008000200002&lng=en&nrm=iso&tlng=en
Decision making can be defined as the selection process of the best routes from the alternatives in order to achieve a goal, and mostly involves uncertainty. Additionally, mostly qualitative variables (weak rock, massive ore deposit, etc.) are in question. Mine planning engineers often use their intuition and experience in decision making. As fuzzy set theory has been used since 1970, these uncertainties are easily evaluated in the decision-making process. Real world study is decision making under vague constraints of different importance, involving uncertain data (qualitative variables), where compromises between antagonistic criteria are allowed. This paper presents a new approach in the selection of an underground mining method based on fuzzy set theory for the Ciftalan Lignite Site located close to Istanbul in Turkey. The physical parameters such as geology and the geotechnical properties of ore, hanging and foot wall, economic effects, environmental effects, are established with field and laboratory tests together with the determination of other qualitative variables. Meanwhile, some qualitative variables dealing with the matter was described according to the view of a number of experts. Then fuzzy set theory is applied to these parameters, considering the available underground methods in order to choose the proper method. At the end of the evaluations, the room and pillar method with filling was determined as the most suitable method for the test site.<![CDATA[<b>An algorithm for quantifying regionalized ore grades</b>]]>
http://www.scielo.org.za/scielo.php?script=sci_arttext&pid=S0038-223X2008000200003&lng=en&nrm=iso&tlng=en
We present a novel hybrid algorithm for quantifying the ore grade variability that has central importance in ore reserve estimation. The proposed algorithm has three stages: (1) fuzzy clustering, (2) similarity measure, and (3) grade estimation. The method first considers data clustering, and then uses the clustering information for quantifying the ore grades by means of a cumulative point semimadogram function. The method provides a measure of similarity and gives an indication of the regional heterogeneity. In addition, grade estimations can be obtained at different levels of similarity using a weighting function, which is the standard regional dependence function (SRDF).<![CDATA[<b>Development of luminescent diamond simulants for X-ray recovery</b>]]>
http://www.scielo.org.za/scielo.php?script=sci_arttext&pid=S0038-223X2008000200004&lng=en&nrm=iso&tlng=en
Specially designed X-ray machines are used extensively on diamond mines for the recovery of diamonds from ores. These machines are extremely expensive and, at present, there are no reliable methods of determining if the equipment is performing efficiently. The objective of this work was to manufacture translucent X-ray diamond simulants, which would emit varying but known luminescence signals when exposed to X-rays. These X-ray diamond simulants could then be used to determine the efficiency of diamond recovery operations.<![CDATA[<b>Early start and late start algorithms to improve the solution time for long-term underground mine production scheduling</b>]]>
http://www.scielo.org.za/scielo.php?script=sci_arttext&pid=S0038-223X2008000200005&lng=en&nrm=iso&tlng=en
Mixed integer programming (MIP) has been used for optimizing production schedules of mines since the 1960s. The major problem in the long-term production scheduling for an entire orebody is that the number of integer variables needed to formulate an MIP model is too large to solve the formulation. This number may reach well over one hundred thousand. To overcome this difficulty, this paper presents two new algorithms to reduce the size of the problem. These algorithms assign an earliest and latest possible start date for each machine placement, eliminating the integer variables that correspond to machine placement before its early start date and after its late start date. A case study based on Kiruna Mine, the second largest underground mine in the world, is summarized in the paper. It shows substantial improvement in the solution time required using the new algorithms. This increased efficiency in the solution time of the MIP model allows it to be applied to Kiruna Mine, with the potential to increase substantially the net present value (NPV) of the project.<![CDATA[<b>Application of variography to the control of species in material process streams: %Fe in an iron ore product</b>]]>
http://www.scielo.org.za/scielo.php?script=sci_arttext&pid=S0038-223X2008000200006&lng=en&nrm=iso&tlng=en
<![CDATA[<b>Water requirements for the recovery of diamonds using grease technology</b>]]>
http://www.scielo.org.za/scielo.php?script=sci_arttext&pid=S0038-223X2008000200007&lng=en&nrm=iso&tlng=en
South Africa is running out of fresh water and is looking at ways to improve water usage. This report addresses how a diamond mine in Gauteng has improved the utilization of its water resources. In the past, water from the local river was deemed the most suitable to use in the grease recovery plant but subsequent tests on the water from the slimes dam have shows that this water source is in fact more suitable for diamond recovery. Utilizing the slimes dam water provides a more stable water source than the local river while also reducing the impact of the mine on the water resources in the area.