Applying chemometrics to predict metallurgical niobium recovery in weathered ore

Niobium metallurgical recovery measures how much of the metal content in the ore is separated in the concentrate after the mineral processing stages. This information can be obtained through laboratory tests with ore samples obtained during drilling. Thereunto, representative ore samples are subjected to tests mimicking the ore concentration processing flow, but these experiments are time consuming and costly. The main objective of this study was to develop a more efficient way to obtain the metallurgical recovery information from ore samples. Based on the development of chemometrical studies, the chemical components currently analyzed in the ore with correlation to the metallurgical recovery were identified. These correlated variables were used to build a nonlinear multivariate regression model to explain the response variable, i.e. metallurgical recovery. The Principal Component Analysis was used in this work to define which chemical variables contribute most to explain the metallurgical recovery phenomenon. The Second order regression equation (Response Surface) was the most suitable methodology to explain the metallurgical niobium recovery and was created by the interaction of the five most important chemical variables. After the exclusion of outliers, the linear regression coefficient between the metallurgical recovery calculated and the metallurgical recovery analyzed was 82.59%. The use of the second order regression equation contributes to reduce the amount of experimental analysis to assess the geometallurgical niobium ore response, promoting the reduction of costs for metallurgical characterization of the ore samples. The methodology proposed proved to be efficient, maintaining an adequate precision in the forecasted response

Field parametric geostatistics : an alternative to tackle gold grade estimations

High anomalous grades are very common in gold deposits, whose presence requires careful treatment to prevent overestimation of metal content. Mineral resource analysts have worked on the estimation of several gold deposits, and none of the classical methods were able to avoid manual interventions, such as cutting high grades for local estimation or using more information beyond the data for the variogram inference. The Field Parametric Geostatistics (FPG) is presented as an alternative for the application of linear kriging methods to estimate highly skewed distributions, proposing a mathematical model which incorporates the grades and its representativeness into a new variable, reducing the influence of high grades without empirical manual interventions. In this article, the mathematical formulation of the FPG theory is presented, as well as its application in datasets with outliers and high skewed distributions: the Walker Lake dataset and the Amapari gold deposit. The results are compared to results obtained by the application of standard techniques, demonstrating that FPG is a feasible alternative to estimate local grades and local reserves for highly skewed variables

Grade uncertainty embedded in long term scheduling : stochastic mine planning

The inclusion of grade uncertainty for multivariate mineral deposits is of great importance for the correct management of subsequent decisions involved in mining planning. Mapping grade uncertainties allows maximization of profit and resource extraction. In this article, the co-simulation turning band algorithm is applied with the aim of predicting multivariate grade uncertainties. Moreover, a probabilistic analysis in long term mining sequencing is proposed in order to select the best given grade scheduling uncertainty derived from the simulations. A case study in a phosphate mine shows that the correlation of co-simulated variables honors the original data and there is an improvement in the project by an increase in Net Present Value (NPV) planning considering grade uncertainties. A comparison is performed with the results derived from the selected schedule and the results using the model based on kriged grades

Assessing geologic model uncertainty : a case study comparing methods

Evaluating mineral resources requires the prior delimitation of geologically homogeneous stationary domains. The knowledge about the ore genesis and geological processes involved are translated into three dimensional models, essential for planning the production and decision-making. The mineral industry usually considers grade uncertainty for resource evaluation; however, uncertainty related to the geological boundaries are often neglected. This uncertainty, related to the location of the boundary between distinct geological domains can be one of the major sources of uncertainty in a mineral project, and should be assessed due to its potential impact on the ore tonnage, and consequently, on enterprise profitability. This study aims at presenting three different methodologies capable of generating multiple geomodel realizations and thus, assessing uncertainty. A real dataset with high geological complexity is used to illustrate the methodology. The results are compared to a deterministic model used as a reference scenario

Signed distance function implicit geologic modeling

Prior to every geostatistical estimation or simulation study there is a need for delimiting the geologic domains of the deposit, which is traditionally done manually by a geomodeler in a laborious, time consuming and subjective process. For this reason, novel techniques referred to as implicit modelling have appeared. These techniques provide algorithms that replace the manual digitization process of the traditional methods by some form of automatic procedure. This paper covers a few well established implicit methods currently available with special attention to the signed distance function methodology. A case study based on a real dataset was performed and its applicability discussed. Although it did not replace an experienced geomodeler, the method proved to be capable in creating semi-automatic geological models from the sampling data, especially in the early stages of exploratio

Computacao grafica aplicada em modelamento geologico : caso: carbonifera treviso-lauro muller, sc

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Estimating uniaxial compressive strength, density and porosity of rocks from the p-wave velocity measurements in-situ and in the laboratory

Knowledge of the physical properties of rock masses is fundamental for the economics and safety of mining projects. The determination of these properties in rock samples in the laboratory requires time, expensive equipment and qualified personnel, which considerably increases the information's cost. Indirect methods were developed to obtain properties related to rock masses, which have been shown to be a viable alternative to traditional procedures. The determination of the compressional mechanical wave velocity (Vp) and subsequent correlation with lithological mechanical properties are indirectly obtained. This study’s objective was to obtain correlations between Vp and the resistance to uniaxial compression, UCS (Unconfined Compressive Strength), as well as the density and porosity of the siltstone and sandstone lithologies present in the coalfield of Candiota, located in the southern region of Rio Grande do Sul, Brazil. The Vp records were obtained in laboratory samples, using ultrasonic velocity sensors, and in-situ by geophysical well logging (directly in boreholes). The results indicate the possibility of using Vp to determine the physical parameters of the investigated lithologies. In the specific case of the correlations between Vp and Unconfined Compressive Strength, determination coefficients R2 above 0.70 were obtained, indicating sufficiently high reliability for using this information (e.g. in roof support projects). The correlation between Vp and density was also high

Otimização da estratégia de homogenização para controle de qualidade de minérios considerando a variabilidade "in situ" de atributos geológicos

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Modelamento de propagação de onda sísmica causada por detonação com explosivo em maciços rochosos

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