Development of a computer-aided application using Lane's algorithm to optimize cut-off grade.

Githiria, J.
Musingwini, C.
Muriuki, J.
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The Southern African Institute of Mining and Metallurgy.
The maximization of net present value (NPV) is a primary objective in open pit mine planning processes. In an attempt to meet this objective, cut-off grades are considered in all the stages of mining. There are three stages involved in resource extraction, namely mining, processing, and refining/marketing, which are all defined in Lane's approach. Using Lane's approach, the economics involved in each stage are identified independently and interact to provide an optimum cut-off grade. A hypothetical block model is used to illustrate how a computer application (Cut-off Grade Optimiser) that was developed in this study is used in the optimization of cut-off grades using Lane's algorithm. This paper analyses the application of Lane's approach for a single element in cut-off grade optimization as applied in the calculation of the optimum cut-off grade through a computer-Aided application. Although Lane's approach is not complex it is not widely applied to maximize the NPV of mining operations as the iterative calculations can be lengthy. The computer application developed in this study shows how Lane's algorithm combined with a linear programming function can optimize cut-off grade with regard to the complex situations faced by mining operations. The application saves users from performing otherwise lengthy, iterative calculations.
The work reported in this paper is part of a PhD research study at the University of the Witwatersrand.
C++ programming, Computer aided analysis, Computer programming, Linear programming, Open pit mining, Optimization, Cut-off grade, Mining operations, Iterative calculation
Githiria, J., Musingwini, C. and Muriuki, J. 2016. Development of a computer-aided application using Lane's algorithm to optimize cut-off grade. Journal of the Southern African Institute of Mining and Metallurgy 116(11), pp. 1027-1035.