Improving short-term grade block models: alternative for correcting soft data

Araújo, Cristina da Paixão; Costa, João Felipe Coimbra Leite; Koppe, Vanessa Cerqueira

doi:10.1590/0370-44672016710007

Cited by 2 publications

(1 citation statement)

References 2 publications

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“…Thus, it is necessary to improve the accuracy of block models. Also, the re-estimation of models is usually performed using geostatistical methods based on data provided by densified meshes, using samples obtained from channels and drilling dust (Araújo et al, 2018;Nelis and Morales, 2021). Consequently, a more agile and adaptive planning process is needed.…”

Section: Introductionmentioning

confidence: 99%

Short-term mine scheduling targeting stationary grades

et al. 2022

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In short-term mine planning, mining scheduling is generally defined by designing dig-lines, allocated on benches. The mined ore will be sent to stockpiles, homogenization piles, or a concentration plant. The process to design dig-lines is usually done manually, whereby multiple simultaneous mining fronts are time-consuming and labour-intensive. The manual design of dig-lines tends to produce high variability of the grades throughout certain periods. Due to the limited time to manually multiple test dig-line design alternatives in short term planning, it is impossible to ensure production under stationary mean grades and variance. This article proposes an alternative to design short-term dig-lines, through an optimization process that joins and sequences the blocks in the block model over weeks or months, ensuring low variability of grades among periods. The methodology proposed generates multiple random paths starting at seed-points representing the locations and numbers of shovels previously selected by the mine planner. It tests multiple polygons representing a set of first dig-lines, comparing them with others, and keeping the dig-lines of low variability closer to a specific ore grade probability distribution, discarding the rest of the iterations. The process is repeated for the next dig-line. The block grades' probability distribution of all iterations is compared to a reference-grade histogram, and the iterations with the grade histogram more adherent are selected. Union-find and genetic algorithms were used to optimize the dig-lines aiming at the possible stationary grade distribution. The mean and variance of the reference model are 2.13% and 0.64% 2 , respectively. The mean for the automated draw dig-lines is closer to these values than the ones manually drawn. The method ensures more constant quality and quantity of ore production along a period planned, matching a target grade probability distribution. The methodology is illustrated using SiO 2 values at a major iron ore mine.

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Section: Introductionmentioning

confidence: 99%

Short-term mine scheduling targeting stationary grades

et al. 2022

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Grade 3D Block Modeling and Reserve Estimation of the C-North Iron Skarn Ore Deposit, Sangan, NE Iran

Rezaei

Hassani

Moarefvand

et al. 2019

Glob. J. Earth Sci. Eng.

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Estimation of ore grade is a time and cost consuming process that requires laboratory-based and exploratory information to present the shape and the ore grade distribution of ore deposit in three dimensional space. The block size is one of the most important parameters which impacts the quality of grade estimates in a resource model. This study aims at spatial modeling of iron ore deposit using geostatistical estimation methods such as Ordinary Kriging based on error estimation, selection of the appropriate size for mining blocks using Vlse Kriterijumsk Optimizacija Kompromisno Resenje method, and performing a three-dimensional block modeling along a grade estimation study for the resource estimation in the C-North iron ore deposit, NE Iran. The variogram that was used in OK estimation was cross validated. Cross validation results showed that compared with the local model, OK with the global model was the most appropriate model for the ore body. Detailed distribution maps of total iron contents in the C-North ore deposit showed a close relationship between structural features and higher iron contents, relative to other areas of the ore deposit. Structural features included the major faults and fault zones along the axial plane. These structures are interpreted to have played a significant role in (re) mobilisation and concentration of the metals, in agreement with observations made elsewhere in the Sangan iron ore complex. Based on the estimation results, 83 million tons of resource was estimated at an average grade of 41.86 % Fe using OK method. The C-North ore deposit has been classified based on the relative estimation error variance and the Australasian Code for Reporting of Mineral Resources and Ore Reserves. It is hoped that this example, taken from very different application fields, will encourage practitioners in applying an OK method with variety of ore deposits.

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Improving short-term grade block models: alternative for correcting soft data

Cited by 2 publications

References 2 publications

Short-term mine scheduling targeting stationary grades

Short-term mine scheduling targeting stationary grades

Grade 3D Block Modeling and Reserve Estimation of the C-North Iron Skarn Ore Deposit, Sangan, NE Iran

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