A Practical Approach to Mine Equipment Sizing in Relation to Dig-Limit Optimization in Complex Orebodies: Multi-Rock Type, Multi-Process, and Multi-Metal Case

Ruiseco, Julian Ramirez; Kumral, Mustafa

doi:10.1007/s11053-016-9301-8

Cited by 17 publications

(4 citation statements)

References 17 publications

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“…The penalization function is nonlinear, so they rely on a genetic algorithm to solve the model. This tool was later used to evaluate the relationship between selectivity, equipment size and dig-limits definitions [Ruiseco and Kumral, 2017]. Williams et al [2021] propose a neural network to evaluate the dig-limits definition made by the genetic algorithm in order to improve the efficency of this approach.…”

Section: Literature Reviewmentioning

confidence: 99%

Column Generation for Mining Cut Definition with Geometallurgical Interactions

2021

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In this work we propose a novel approach to solve the mining cut definition problem in open pit mines with geometallurgical interactions. Mining cut definition deals with the aggregation of blocks into clusters which are extracted and processed as a single unit. The aggregation must fulfill operational considerations given by the loading equipment selectivity and should maximize the objectives given by the mine operation.The proposed approach utilizes mixed integer programming and a model inspired by column generation that, contrarily to previous works, has its decision variables defined directly on the set of all feasible cuts. The advantage of this is that the model does not require linear approximations of the geometallurgical behavior of the cuts based on the blocks it contains, and therefore, can utilize any non-linear function.We apply our methodology to a real-size case study to show that the model can be solved in reasonable time, but also that non-linear recovery functions influence the destination policy and expected profit, i.e., that following the traditional free selection policy (based on cut-off grade) is not the best strategy when realistic geometallurgical interactions are considered.

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Section: Literature Reviewmentioning

confidence: 99%

Column Generation for Mining Cut Definition with Geometallurgical Interactions

2021

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“…Due to large spatial variations and the uncertainties inherent to the geological contact, any excavation surface inevitably causes dilution and ore losses. Although this problem shows a similarity with the dig-limit problems in open-pit mining, which has been covered by several studies such as Deutsch (2001, 2002); Richmond (2004); Richmond and Beasley (2004); Isaaks et al (2014); Ruiseco et al (2016); Ruiseco and Kumral (2017); Sari and Kumral (2018), the problem with lateritic deposits is rather specific due to the nature of free-digging mining method. Research on finding the optimum elevation values for a lateritic nickel mine has been carried out by McLennan et al (2006), but the focus was to optimize the dilution and ore losses.…”

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confidence: 91%

Pilot Point Optimization of Mining Boundaries for Lateritic Metal Deposits: Finding the Trade-off Between Dilution and Ore Loss

et al. 2018

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Geological contacts in lateritic metal deposits (footwall topographies) often delineate the orebody boundaries. Spatial variations seen in such contacts are frequently higher than those for the metal grades of the deposit. Therefore, borehole spacing chosen based on the grade variations cannot adequately capture the geological contact variability. Consequently, models created using such boreholes cause high volumetric uncertainties in the actual and targeted ore extraction volumes, which, in turn, lead to high unplanned dilution and ore losses. In this paper, a method to design optimum ore/mining boundaries for lateritic metal deposits is presented. The proposed approach minimizes the dilution/ore losses and comprises two main steps. First, the uncertainty on the orebody boundary is represented using a set of stochastic realizations generated with a multiple-point statistics algorithm. Then, the optimal orebody boundary is determined using an optimization technique inspired by a model calibration method called Pilot Points. The pilot points represent synthetic elevation values and they are used to construct smooth mining boundaries using the multilevel B-Spline technique. The performance of a generated surface is evaluated using the expected sum of losses in each of the stochastic realizations. The Simulated Annealing algorithm is used to iteratively determine the pilot point values which minimize the expected losses. The results show a significant reduction in the dilution volume as compared to those obtained from the actual mining operation.

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“…Recently various researchers have developed algorithms to automate and optimize the dig limit delineation process, which rely on heuristics and metaheuristic algorithms. The techniques used include greedy algorithm-based methods (Richmond and Beasley, 2004;Vasylchuk and Deutsch, 2019), mixed integer programming (MIP) (Hmoud and Kumral, 2022;Nelis and Morales, 2021;Nelis, and Meunier, 2022;Sari and Kumral, 2017;Tabesh and Askari-Nasab, 2011), genetic algorithms (Ruiseco, 2016;Ruiseco, Williams, and Kumral, 2016;Ruiseco and Kumral, 2017;Williams et al, 2021), simulated annealing (Deutsch, 2017;Hanemaaijer, 2018;Isaaks, Treloar, and Elenbaas 2014;Kumral, 2013;Neufeld, Norrena, and Deutsch, 2003;Norrena and Deutsch, 2001), block aggregation by clustering (Salman et al, 2021) and convolutional neural networks (Williams et al, 2021), or a combination of techniques. With these algorithms, the definition of 'mineability' or 'digability' varied greatly between different investigators.…”

Section: Introductionmentioning

confidence: 99%

“…For this constraint, they assigned a minimal mining width (MMW) that prevented the individual selection of blocks. Similarly, Ruiseco (2016) and Ruiseco and Kumral (2017) used a GA algorithm for the dig limit selection problem. In addition, they assigned a penalty, when blocks deviated from the correct clustering size.…”

Section: Introductionmentioning

confidence: 99%

Stochastic analysis of dig limit optimization using simulated annealing

Duijvenbode

Shishvan

2023

J. S. Afr. Inst. Min. Metall.

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The results of dig limit delineation in open pit mining are never truly optimized due to gaps in the underlying data, such as insufficient sampling. Aside from the data uncertainty, there is also an influence on the final dig limit by either humans or by the heuristic character of an optimization method like simulated annealing. Several dig limit optimizers have been published, which can replace the manual dig-limits designing process. However, these dig limit designs are generally not adapted to account for this heuristic character. In this paper we present a stochastic analysis tool that can be used with the results of heuristic dig-limit optimization to increase confidence in the obtained results. First, an enhanced simulated annealing algorithm for dig limit optimization is presented. Then, this algorithm is tested on ten different blasts at the Marigold mine, Nevada, USA, as a case study. Finally, the results are analysed with a destination-based ensemble probability map and an analysis conducted of the final solution data distribution. The generated dig-limit designs of the algorithm include high revenue areas that are excluded in comparable manual designs and show improved objective and revenue values. The analysis tool provides block destination probabilities and box plots with the distribution of opportunity value for the dig limit. Furthermore, with the analysis tool, it is possible to make well-informed design decisions in areas of uncertainty.

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A Practical Approach to Mine Equipment Sizing in Relation to Dig-Limit Optimization in Complex Orebodies: Multi-Rock Type, Multi-Process, and Multi-Metal Case

Cited by 17 publications

References 17 publications

Column Generation for Mining Cut Definition with Geometallurgical Interactions

Column Generation for Mining Cut Definition with Geometallurgical Interactions

Pilot Point Optimization of Mining Boundaries for Lateritic Metal Deposits: Finding the Trade-off Between Dilution and Ore Loss

Stochastic analysis of dig limit optimization using simulated annealing

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