State of the art about metaheuristics and artificial neural networks applied to open pit mining

Giovanni Franco-Sepúlveda, Juan Camilo Del Rio-Cuervo and María Angélica Pachón-Hernández

Resources Policy, 2019, vol. 60, issue C, 125-133

Abstract: In search of the best way to extract and take advantage of minerals, highlighting that these are part of the most important raw materials for the economic development of today's society, the following bibliographical review is presented, which covers the main metaheuristic techniques highlighted in the optimization of mining processes and artificial neural networks (ANN), fundamental for predicting them; With this, the applications and results of these methods can be observed in mining unit operations such as: blasting, transport and mineral processing, which until now have models or techniques for their prediction that are not applicable in all mining complexes, as well as metaheuristics for three fundamental variables of open-pit planning, which are: geological uncertainty, cutting law and extraction programming. In addition to this, the proposals that have been developed in the global optimization of mining complexes are shown. There is also a brief description of how these techniques were applied to optimize the operations and previous variables of the mining planning, as well as their implementation in several mines around the world. The information shown shows available alternatives for the implementation of new actions in favor of reaching the objectives for real and hypothetical sites, yielding satisfactory results. Finally, the conclusions of this work are presented.

Keywords: Metaheuristics; Artificial neural networks; Open pit mining; Optimization; Prediction (search for similar items in EconPapers)
Date: 2019
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (5)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:jrpoli:v:60:y:2019:i:c:p:125-133

DOI: 10.1016/j.resourpol.2018.12.013

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