Optimizing Ore–Waste Dig-Limits as Part of Operational Mine Planning Through Genetic Algorithms

Ruiseco, Julian Ramirez; Williams, Jacob; Kumral, Mustafa

doi:10.1007/s11053-016-9296-1

Cited by 28 publications

(7 citation statements)

References 15 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Sari and Kumral [2017] propose a mixed-integer program to solve the dig-limit optimization problem based on valid frames: each block must belong to a pre-defined valid frame, and all the blocks belonging to the same frame must be sent to the same destination. Ruiseco et al [2016] also propose a heuristic approach to the dig-limit problem. The authors penalize sectors that do not comply with operational constraints.…”

Section: Literature Reviewmentioning

confidence: 99%

Column Generation for Mining Cut Definition with Geometallurgical Interactions

2021

View full text Add to dashboard Cite

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.

show abstract

Section: Literature Reviewmentioning

confidence: 99%

Column Generation for Mining Cut Definition with Geometallurgical Interactions

2021

View full text Add to dashboard Cite

show abstract

“…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.…”

mentioning

confidence: 91%

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

et al. 2018

View full text Add to dashboard Cite

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.

show abstract

“…In recent years, artificial intelligence (AI) has become more popular and widely applied in many different fields. Review of literature showed that AI had been involved in many aspects such as mineralizing geochemical anomalies [33][34][35], optimizing operational mine planning [36], civil engineering [37,38], analyzing mineral systems [39], mineral potential mapping [40,41], resourcing future generations [42,43], predicting blast-induced problems [44][45][46][47]. In predicting blast-induced PPV, the feasibility of a support vector machine (SVM) algorithm was studied and applied by Hasanipanah et al [7] to predict blast-induced PPV in Bakhtiari Dam, Iran.…”

Section: Introductionmentioning

confidence: 99%

Support vector regression approach with different kernel functions for predicting blast-induced ground vibration: a case study in an open-pit coal mine of Vietnam

Nguyen

2019

SN Appl. Sci.

View full text Add to dashboard Cite

Blast-induced ground vibration (PPV) is one of the effects of the hazard in open-pit mines. It can make vibration of the structure, instability of slope and bench, impact on underground water, and the surrounding residential. Therefore, the precise prediction of blast-induced PPV is needed to minimize undesirable effects on the surrounding environment. Also, one of the most important objectives of this study is to determine the site characteristics for the application of controlled blasting techniques. In this study, the support vector regression (SVR) approach was considered and developed for predicting blast-induced PPV in an open-pit coal mine (Vietnam) as a case study. Three forms of the equation include linear (L), polynomial (P), and radius basis function (RBF) were applied to develop the SVR models. For comparison purpose, an empirical technique was also referred to as estimate blast-induced PPV in this study on the same training dataset. A database with 181 blasting events was used for this aim. Performance indicators such as root-mean-square error (RMSE) and the coefficient of determination (R 2 ) were used to compare and evaluate the performance of the predictive models. The results showed that SVR was an effective approach for predicting blast-induced PPV in this study. Among three forms of the equation, the SVR model with RBF was the most superior model for predicting blast-induced PPV in this study with an RMSE of 0.396, R 2 of 0.924, and MAE of 0.135, whereas the empirical model only obtained performance with an RMSE of 0.856, R 2 of 0.643, and MAE of 0.575. This study provided an overview of the SVR approach in predicting blast-induced PPV. A comparison of different kernel functions for the selection of the SVR model is needed to find out the best model for predicting blast-induced PPV.

show abstract

Optimizing Ore–Waste Dig-Limits as Part of Operational Mine Planning Through Genetic Algorithms

Cited by 28 publications

References 15 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

Support vector regression approach with different kernel functions for predicting blast-induced ground vibration: a case study in an open-pit coal mine of Vietnam

Contact Info

Product

Resources

About