Abstract 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 SiO2 values at a major iron ore mine.

Abstract One of the basic factors in mine operational optimization is knowledge regarding mineral deposit features, which allows to predict its behavior. This could be achieved by conditional geostatistical simulation, which allows to evaluate deposit variability (uncertainty band) and its impacts on project economics. However, a large number of realizations could be computationally expensive when applied in a transfer function. The transfer function that was used in this study was the NPV net present value. Hence, there arises a necessity to reduce the number of realizations obtained by conditional geostatistical simulation in order to make the process more dynamic and yet maintain the uncertainty band. This study made use of machine-learning techniques, such as multidimensional scaling and hierarchical cluster analysis to reduce the number of realizations, based on the Euclidean distance between simulation grids. This approach was tested, generating 100 realizations by the sequential Gaussian simulation method in a database. Proving that similar uncertainty analysis results can be obtained from a smaller number of simulations previously selected by the methodology described in this study, when compared to all simulations.

A modelagem geológica de depósitos estratiformes pode-se tornar muito complexa, muitas vezes fazendo uso de envelopes geológicos de pequena espessura e exigindo o uso de sub-blocos (com base em coordenadas cartesianas) para produzir um modelo de blocos coerente. No entanto, eventos geológicos após a formação do depósito (dobras, falhas, etc.) podem mudar a direção da continuidade espacial de certos atributos, com mistura de amostras pertencentes a épocas de formação geológicas diferentes (no caso de depósitos estratiformes) na mesma elevação. Esse estudo apresenta uma solução para depósitos com teores estratigráficos combinados com amostras de diferentes origens. A solução é uma estimativa bidimensional obtida pela acumulação da espessura de P2O5 em um depósito de fosfato (em comparação com a análise estatística tradicional em três dimensões).

The geological modeling of stratiform deposits can become very complex, often making use of geological envelopes of small thickness and requiring the use of subblocks (based on Cartesian coordinates) to produce a coherent block model. However, geological events after the formation of the deposit (folds, faults, etc.) can change the direction of spatial continuity of certain attributes, with the mixing of samples belonging to different formation eras (in the case of stratiform deposits) in the same elevation. This study presents a solution for deposits with stratigraphic grades combined with samples of different origins. The solution is a two-dimensional estimate obtained by accumulating the thicknesses of P2O5 in a phosphate deposit (as compared to traditional statistical analysis in three dimensions).

As usinas de beneficiamento de minério, para garantir elevados índices de recuperação no produto final, dependem de inúmeros fatores, entre os quais destaca-se a estabilidade no teor de alimentação. As pilhas de homogeneização têm se mostrado uma ferramenta eficaz no atendimento dessa necessidade, atenuando, significativamente, a variabilidade do minério proveniente de diferentes frentes de lavra. Esse estudo testa a eficiência da simulação geoestatística quando aplicada à previsão da variabilidade in situ dos teores do minério formador das pilhas de homogeneização. Isto porque os métodos tradicionalmente utilizados, para o planejamento desses sistemas, não são capazes de incorporar, adequadamente, a incerteza associada ao valor estimado. Surge, então, a simulação geoestatística como alternativa, método que possibilita a geração de múltiplos cenários equiprováveis dos teores de determinado depósito. O conjunto de realizações dará origem a um grupo de possíveis valores para os blocos formadores de cada pilha, possibilitando, assim, a quantificação da variabilidade in situ. Os teores simulados foram comparados com os amostrados pela empresa. A reconciliação entre previsto e realizado mostrou precisão e acuracidade dos modelos gerados, reproduzindo, adequadamente, os teores de referência.

Mineral processing in order to ensure high ore recovery at the final product depends on many factors, among them, the low variability in the head grades feeding the plant. For this, homogenization piles have been an effective tool for significantly diminishing variability. This study tests the efficiency of geostatistical simulations to predict in situ grade variability and how this variability is transposed to the ore, forming blending piles. Traditionally, the methods used in designing these systems are not capable of properly incorporating the uncertainty associated with the estimated value. Ordinary kriging, for example, results in best local estimates, but with a smooth global variance. Conversely, geostatistical simulations provide equiprobable scenarios for the grades of a given deposit. These scenarios honor the sample values, their histogram and spatial distribution. They also provide a set of possible values for each block which forms each homogenization pile. Finally, it is possible to predict the in situ grade variability, given the real grades. The results were compared against the real (reference) mined grades through reconciliation. Results demonstrated that the methodology is accurate and precise enough to be applied in blending piles design.

Combinações de minério provenientes de diferentes frentes de lavra e pilhas de homogeneização são, comumente, empregadas para reduzir a variabilidade dos teores nas plantas de beneficiamento. A metodologia proposta, nesse trabalho, visa a quantificar a variabilidade das diversas etapas em um sistema de homogeneização. O parâmetro químico avaliado é sílica (SiO2) de minério de ferro, na faixa granulométrica inferior a 0,15 mm. Este é um contaminante errático, nesse tipo de depósito. Os resultados mostram a variância esperada para os diversos tamanhos de pilhas. Fica demonstrado que a variância, reduz com o aumento da massa da pilha.

Blending and homogenization piles are usually employed for reducing head grade fluctuation at a processing plant. The methodology suggested herein aims at quantifying the variability at various stages in a homogenization system. The chemical parameter evaluated is silica (SiO2) from an iron ore at a granulometry below 0.15mm. This is a highly variable contaminant in iron ore deposits. The results show the expected variance among the grades for various pile sizes. It is demonstrated that the rate of variance decreases as the pile size increases.

Um dos grandes problemas existentes na indústria mineira são as flutuações no teor do minério, que afetam, significativamente, o desempenho das plantas de beneficiamento. Assim, faz-se necessário um controle dessa variabilidade. Para tanto, pode-se usar combinações de minério provenientes de diferentes frentes de lavra e/ou pilhas de homogeneização. As pilhas de homogeneização são caracterizadas pela sua forma, tamanho, arranjo e número de camadas. O estudo em questão se propõe a minimizar a variabilidade dos depósitos minerais, utilizando pilhas longitudinais do tipo chevron, montadas com informações de teores do modelo de blocos gerados a partir de simulação geoestatística. As análises são feitas simulando diferentes modelos de pilhas, a partir de combinações de massas e diferentes números de camadas. Os resultados demonstram uma redução de até 90% da variabilidade, possibilitando um dimensionamento mais adequado do pátio e dos equipamentos de construção da pilha.

Blending and homogenization piles are usually employed for reducing grade fluctuation from the ore feeding a processing plant. These piles are characterized by their form, size (length and height), construction lay-out and number of layers. Various methods are found for blending piles design and most fail to incorporate the in situ grade variability intrinsic of a mineral deposit. The methodology suggested in this study is able to quantify the variability of the homogenization system using multiple equally probable realizations derived from a geostatistical simulation for the grade block model. The variable tested was silica (SiO2) in a grannulometry below 0,15mm (SI3), an erratic contaminant in iron deposits. The results demonstrated a reduction exceeding 90% in the original variability (in situ compared to grades feeding the processing plant) of the system in favorable conditions. Finally, the reduction in the uncertainty in the head grades feeding the processing plant, leads to cost savings and lower economical risks.