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Developing accurate and accessible geoscientific data for sustainable mining in Africa
Published in Saleem H. Ali, Kathryn Sturman, Nina Collins, Africa’s Mineral Fortune, 2018
Judith A. Kinnaird, Raymond J. Durrheim
If exploration is successful and a viable mineral deposit is discovered, work will begin on a mining feasibility study. This research seeks a host of non-geological data, such as availability and cost of electricity and water, as well as availability of a skilled workforce. The collection of geoscientific data becomes important once a mine opens, to ensure that an ore body is optimally exploited, and continues long after the mine closes, to ensure that negative impacts on the environment and population are mitigated.
A regression-tree-based model for mining capital cost estimation
Published in International Journal of Mining, Reclamation and Environment, 2020
Hamidreza Nourali, Morteza Osanloo
One of the fundamental components of mining feasibility study is capital cost estimation. Spending capital cost, during the early years of mine life, has an impressive impact on the net present value (NPV) of projects. Generally, the main goal of mine planning, designing and scheduling is the fact of determination of optimal ultimate pit limit with regard to production scheduling horizon to achieve the maximum NPV. Fixation of the factor of production per year and specification of suitable mine fleet and equipment are both key factors of design and production scheduling to meet maximum profit. These are the determinant factors for mining capital cost estimation [1]. Equipment size has a direct relation with capital cost [2]. In addition, the new technologies have an impact on mining capital cost that includes the potential of increasing capital cost [3]. Capital cost assessment can play a critical role in deciding whether projects will be proceeded, delayed or abandoned [1]. It is, therefore, important that the capital cost estimation is carried out accurately as determined by the estimation guidelines based on the level of estimation conducted [4].
Genetic algorithms for the optimisation of the Schwartz–Smith two-factor model: a case study on a copper deposit
Published in International Journal of Mining, Reclamation and Environment, 2018
Mathieu Sauvageau, Mustafa Kumral
The first part of this paper focuses on generating an artificial commodity spot and future price data-set with known parameters to test the robustness of GA for the optimisation of the KF parameters. Then, the workflow is applied to the same crude oil futures data-set presented in Schwartz and Smith. In the third part, the workflow is applied on copper prices, comparing KF parameters estimated with the GA to gradient search methods [19]. Finally, a mining feasibility study is undertaken using the NPV approach and an active trading strategy based on the difference between short-term and long-term prices derived with the KF. The active trading strategy is compared to the conventional NPV analysis to assess the added value of managing the stockpile. The originality of this paper rests on the use of a new optimisation approach to find the optimal parameters of the SSTF model. The use of GA requires less user intervention than the conventional EM approach because they study a population of initial parameters vectors instead of optimising one point at a time.