Classical and stochastic mine planning techniques, state of the art and trends.

• Autor(a) principal: Torres, Vidal Félix Navarro
• Data de Publicação: 2018
• Outros Autores: Nader, Beck, Arroyo Ortiz, Carlos Enrique, Souza, Felipe Ribeiro, Burgarelli, Hudson Rodrigues, Chaves, Leonardo Soares, Carvalho, Luiz Alberto, Câmara, Taís Renata, Fernandes, Eunírio Zanetti, Galery, Roberto
• Tipo de documento: Artigo
• Idioma: eng
• Título da fonte: Repositório Institucional da UFOP
• Texto Completo: http://www.repositorio.ufop.br/handle/123456789/11264; http://dx.doi.org/10.1590/0370-44672016710165
• topic: Deterministic mine planning; Direct block schedule; Uncertainty

Determination of the best possible ultimate pit for an open pit mine is a fundamental subject that has undergone a highly evolutionary process, reviewed in this study, since the correct choice carries substantial economic impact for the industry. The correct choice can be very beneficial for project analysis, whereas an incorrect choice has the potential to mask huge financial and economic future losses that could render a project unfeasible. The advent of computers in the 1960s allowed sophisticated analysis for the selection of the best ultimate pit determination, under specific modifying factors such as economic, social, environmental, and political, but only in deterministic situations, i.e., when the problem and variables for the ultimate pit determinations were considered deterministically and almost always based on average values. Techniques such as the Lerchs– Grossman algorithm and mixed-integer programming are among many standard tools now used by the mineral industry. Geological uncertainty and the associated risks as well as the need to consider the appropriate time to mine a block during a mine operation have a significant impact on the net present value of the resulting ultimate pits. Stochastic aspects embed a probabilistic component that varies in time and are now under intense investigation by researchers, who are creating algorithms that can be experimented with and tested in real mine situations. One can expect that once these algorithms demonstrate their efficiency and superior results, they will readily dominate the industry.