Jair Carlos Koppe

Simulation — An Approach to Risk Analysis in Coal Mining

Traditional mine planning based on block models built with interpolation techniques such as kriging does not take into account the uncertainty associated with the estimates. These models are inadequate for short-range mine planning. In contrast, conditionally simulated models reproduce the actual variability (histogram) and spatial continuity (variogram) of the attributes of interest. Conditional simulation can be used to address the problem of measuring the uncertainty associated with an estimate. This paper outlines how the sequential Gaussian conditional simulation algorithm can be used to assess uncertainty of grade estimates and also illustrates how simulated models can be incorporated into mine planning and scheduling. A case study demonstrates the efficiency of the method in assessing risk associated with geological and engineering variables.

Gamma-Ray Data Processing and Integration for Lode-Au Deposits Exploration

A large number of mineral deposits are associated with hydrothermal processes, especially auriferous deposits. In such processes, studies on percolating fluids may indicate the presence of potash (K), among other elements. In this study, aerogammaspectrometric data-processing methodologies are evaluated, especially those methods based on the suppression of the primary contribution of potassium, the result of lithological and soil variations, and to environmental conditions. Resulting maps point out the contribution of hydrothermal K. This processing procedure was used because of the association of hydrothermal K and auriferous mineralizations according to the deposit model defined for the studied region. Intensity maps locate the areas with great influence of hydrothermal K. Data integration required to improve a change in the gammaspectrometric data processing in order to positively correlate hydrothermalised areas. Data integration could distinguish high and medium favorable targets for mineral exploration of lode-Au deposits in the studied region.

Additional samples: Where they should be located

Information for mine planning requires to be close spaced, if compared to the grid used for exploration and resource assessment. The additional samples collected during quasimining usually are located in the same pattern of the original diamond drillholes net but closer spaced. This procedure is not the best in mathematical sense for selecting a location. The impact of an additional information to reduce the uncertainty about the parameter been modeled is not the same everywhere within the deposit. Some locations are more sensitive in reducing the local and global uncertainty than others. This study introduces a methodology to select additional sample locations based on stochastic simulation. The procedure takes into account data variability and their spatial location. Multiple equally probable models representing a geological attribute are generated via geostatistical simulation. These models share basically the same histogram and the same variogram obtained from the original data set. At each block belonging to the model a value is obtained from the n simulations and their combination allows one to access local variability. Variability is measured using an uncertainty index proposed. This index was used to map zones of high variability. A value extracted from a given simulation is added to the original data set from a zone identified as erratic in the previous maps. The process of adding samples and simulation is repeated and the benefit of the additional sample is evaluated. The benefit in terms of uncertainty reduction is measure locally and globally. The procedure showed to be robust and theoretically sound, mapping zones where the additional information is most beneficial. A case study in a coal mine using coal seam thickness illustrates the method.

O problema de amostragem manual na indústria mineral

Discrepancies in raw materials properties informed by both producer and consumer are a common problem in most areas of the industry. Commercial contracts in the mineral industry normally penalize the producer, if contaminants in the ore exceed established limits. Differences in lab analysis are due many sources of error, such as distinct practices used in two laboratories for sampling, preparation or analysis. Sampling theory provides the tools to analyze errors involved in sampling, preparation and analysis. This paper proposes a methodology to check the precision and accuracy of a given sampling protocol emphasizing the application of Gys sampling theory to broken ore. The results show the applicability of the method and its relevance to audit sampling procedures aiming at error minimization. The methodology is illustrated in a case study at a major coal producer in Brazil.

Assessing Uncertainty Associated with the Delineation of Geochemical Anomalies

A pedogeochemical exploratory survey of gold deposits was carried out in the region of São Sepé (southernmost Brazil). The region comprises a predominantly metamorphosed belt of volcanoclastics, sediments, serpentinites, basalts, gabbros, chert, tuffs, and banded iron formation of the Proterozoic age. The anomalies were identified first by stream sediment heavy mineral survey at the regional scale of exploration. Once spatial continuity was modeled, ordinary block kriging was performed to generate geochemical maps. Indicator block kriging also was used as an alternative in analyzing and interpreting geochemical data. A novel approach is proposed, which combines both ordinary and indicator kriging for delineating geochemical anomalies. Probability maps proved to be appropriate for selecting new sites for further exploration. Gold anomalies in soils trending NE were well defined by geostatistical analysis and subsequently confirmed by drilling.

Planning Tunnel Construction Using Markov Chain Monte Carlo (MCMC)

Tunnels, drifts, drives, and other types of underground excavation are very common in mining as well as in the construction of roads, railways, dams, and other civil engineering projects. Planning is essential to the success of tunnel excavation, and construction time is one of the most important factors to be taken into account. This paper proposes a simulation algorithm based on a stochastic numerical method, the Markov chain Monte Carlo method, that can provide the best estimate of the opening excavation times for the classic method of drilling and blasting. Taking account of technical considerations that affect the tunnel excavation cycle, the simulation is developed through a computational algorithm. Using the Markov chain Monte Carlo method, the unit operations involved in the underground excavation cycle are identified and assigned probability distributions that, with random number input, make it possible to simulate the total excavation time. The results obtained with this method are compared with a real case of tunneling excavation. By incorporating variability in the planning, it is possible to determine with greater certainty the ranges over which the execution times of the unit operations fluctuate. In addition, the financial risks associated with planning errors can be reduced and the exploitation of resources maximized.

Estratégia de controle de qualidade de minérios na lavra utilizando simulação geoestatística

The new generation of coal fired power plants in Brazil are expected to use ROM coal as fuel. Thus, variations of coal quality tend to be transferred from mine to the final user. Contracts frequently establish max-min limits for geological and technological parameters. Lots of ore with quality beyond the agreed limits can be rejected or penalized. The high costs of these penalties require quality control of the final product (ROM). The utilization of geostatistical methods aims for quality and variability characterization of ore in the deposit. The geostatistical method uses a block model with grades assigned to each block normally by ordinary kriging. This block model ignores or is inappropriate for accessing the uncertainty associated with the geological model. Consequently, this procedure fails in predicting grade fluctuations correctly. Contrary to kriging, simulation methods aim at reproducing in situ grade variability and spatial continuity. Once a block model has each grade uncertainty evaluated, quality fluctuation can be predicted for a given mining route and size of lot delivered to the customer. Different mining routes and sizes of ore lots are tested until a desirable level of grade oscillation is achieved. Results from a case study at a Brazilian coal mine proved the adequacy and functionality of the method. Simulation geostatistical makes possible predictions of quality fluctuations at certain volumes of ore in the deposit.

Impacto do agrupamento preferencial de amostras na inferência estatística: aplicações em mineração

Preferential sampling or clustering is frequently found in mining and earth sciences applications. Reliable statistics for a population are obtained when representative samples are available. Such representativeness can be achieved by a proper sample design covering evenly the area. This paper investigates two declustering methods to obtain unbiased statistics where clustered samples are available, namely the polygonal and the cell-declustering method. The impact of clustering is analysed for two different datasets. Polygonal method proved to be simpler as it provides an unique solution easily to be understood by the user. In relation to the cell-declustering method, a methodology to calculate the statistical entropy was implemented to help in determining the most appropriate cell size. The two methods lead to similar declustered statistics. However the final statistics showed a large difference when compared to the statistics obtained for the clustered dataset.

Cokrigagem de razões logarítmicas aditivas (alr) na estimativa de teores em depósitos de ferro

Iron ore products are defined by their iron and contaminant grades and also by the granulometric partitions. Data from iron ore deposits constitute compositional data, involving a vector of variables adding up to a constant sum given by the mass balance among granulometric partitions or among chemical species in each granulometric fraction. The closed sums lead to spurious correlations and to a negative bias condition. This condition leads to estimates that do not satisfy the balances and estimates that take negative values or do not belong to the interval of values of the original data. Classic geostatistical methodologies do not take these facts into account and the common practices force the sum through determining one variable by difference, distributing the sum error or using an intrinsic corregionalization model and substituting of the negative values by valid ones. In this paper, cokriging of additive log-ratios (alr), a transformation developed for compositional data, is presented as an alternative methodology to estimate grades in iron ores, in presence of multiple correlated variables with a closed sum. Results obtained through this methodology are better than the ones obtained by direct cokriging of the original data, leading to positive estimates, all in the original data interval and satisfying the considered constant sums, without post-processing.

Forecasting fluctuations in coal quality delivered to a power plant via stochastic simulation

Abstract The new generation of coal-fired power plants in Brazil are expected to use run-of-mine (ROM) coal as fuel; however, variations in the quality of the coal are likely to be transferred from the mine to the final user. Contracts frequently establish maximum/minimum limits for geological and technological parameters, allowing for rejection or imposition of penalties when the quality of the lots is outside the agreed limits. The high costs associated with such penalties necessitate quality control of the final product (ROM). Geostatistical methods can be used to predict in situ ore quality and variability. Geostatistical methods employ a block model with grades assigned to each block. The block model obtained through the use of ordinary kriging is inappropriate in accessing the uncertainty associated with the geological or technological parameters being modelled. Consequently, this procedure fails to predict grade fluctuations correctly. Contrary to kriging, simulation methods aim at reproducing in situ grade variability and spatial continuity. Once one produces a block model, with each grade uncertainty evaluated, quality fluctuation can be predicted for any given mining route. Also the fluctuations associated with the size of a lot to be delivered to the customer can be predicted. Different mining routes and sizes of ore lots are tested until the desired level of grade oscillation is achieved. Results from a case study at a Brazilian coal mine prove the adequacy and functionality of the method and that geostatistical simulation adequately predicts quality fluctuations.