Now showing items 1-15 of 15

    • A mechanism and thermodynamic model for the interaction of silicate minerals with lead-assisted gold electro-oxidation in a dilute cyanide medium 

      Kim, Rina; Ghahreman, Ahmad (Elsevier BV, 2019-07-22)
      Gold (Au) oxidation kinetics in cyanide (CN) solutions can be increased by the addition of low concentrations of aqueous lead [Pb(II)]. However, the Pb(II) addition is not always effective in improving Au oxidation kinetics. ...
    • Geometallurgical Modeling Framework 

      Ortiz, Julian M. (Queen's University, 2019)
      Geometallurgical modeling allows understanding and transferring the variability of input variables into the downstream processes. In this paper, we present the overview of the geometallurgical modeling framework, and show ...
    • Knowledge Discovery from Geochemical Data with Supervised and Unsupervised Methods 

      Cevik, S. Ilkay; Olivo, Gema Ribeiro; Ortiz, Julian M. (Queen's University, 2019)
      As mineral exploration activities tend to aim at deeper targets, costs per discoveries are getting higher. Therefore, the effective utilization of all the available data is critical in the decision-making process. In recent ...
    • Exploring the RCNN Technique in a Multiple-Point Statistics Framework 

      Avalos, Sebastian; Ortiz, Julian M. (Queen's University, 2019)
      This work explores the Recursive Convolutional Neural Network (RCNN) technique in terms of describing (1) the inner architecture, (2) the training process and (3) the simulation algorithm in a multiple-point statistics ...
    • Updating Geological Codes Through Iterative Jack-Knife 

      Riquelme, Alvaro I.; Ortiz, Julian M. (Queen's University, 2019)
      We present a methodology to classify spatial data carrying only continuous variables into different categories, where categorical clustering is suitable to be applied to the data. The methodology is based in a very simple ...
    • Modeling the Uncertainty in Geologic Volumes: The Log-Normal Case 

      Riquelme, Alvaro I.; Ortiz, Julian M. (Queen's University, 2019)
      The aim of the study is to develop a methodology that allows the quantification of the uncertainty in an arbitrary volume conditioned by sampling data, without the use of the traditional geostatistical simulation, which ...
    • A General Approach to the Assessment of Uncertainty in Volumes by Using the Multi-Gaussian Model 

      Riquelme, Alvaro I.; Ortiz, Julian M. (Queen's University, 2019)
      The goal of this research is to derive an approach to assess uncertainty in an arbitrary volume conditioned by sampling data, without using geostatistical simulation. We have accomplished this goal by deriving an numerical ...
    • Multivariate Geostatistical Simulation using Principal Component Analysis 

      Bolgkoranou, Maria; Ortiz, Julian M. (Queen's University, 2019)
      Multivariate geostatistical simulation is aimed at reproducing the statistical relationships between variables and their spatial distribution. We present a methodology whereby grades and a filler variable are transformed ...
    • A Literature Review on P-wave Velocities in Rock Under Compression 

      Midkiff, William; Rielo, Oscar; Ortiz, Julian M. (Queen's University, 2019)
      P-waves are acoustic waves propagated through geological mediums. P-wave velocities are increasingly being used in natural resource industries. The purpose of this paper is to present a literature background on the behavior ...
    • MultiGaussian Kriging: A Review 

      Ortiz, Julian M. (Queen's University, 2019)
      The kriging estimate has an associated kriging variance which measures the variance of the error between the estimate and the true value. However, this measure of uncertainty does not depend on the actual value of the ...
    • Using LSTM and GRU to Predict SAG Mill Energy Consumption 

      Avalos, Sebastian; Kracht, Willy; Ortiz, Julian M. (Queen's University, 2019)
      In mine operations, comminution is the most demanding energy consumer. Within comminution, semi-autogenous grinding mills are by far the most intensive consumers. Classic techniques to forecast their future energy consumption ...
    • A Simple, Synthetic and Two Dimensional Geometallurgical Modeling Application 

      Avalos, Sebastian; Ortiz, Julian M. (Queen's University, 2019)
      Geometallurgy seeks to provide a holistic framework across the mine value chain. Although it is possible to find real world applications in the literature, in most cases the integration is just between geological modeling ...
    • Convolutional Neural Networks Architecture: A Tutorial 

      Avalos, Sebastian; Ortiz, Julian M. (Queen's University, 2019)
      Deep learning techniques have found an increasing number of applications in the field of geosciences. Among the most applied ones, Convolutional Neural Networks stand out by their ability to extract features from grid-like ...
    • Machine Learning in Mineral Exploration: A Tutorial 

      Cevik, S. Ilkay; Ortiz, Julian M. (Queen's University, 2019)
      This tutorial aims to demonstrate how to conduct some machine learning methods in geoscience to enhance mineral exploration studies. The document does not intent to present an exhaustive list of all the methods available ...
    • Predictive Geometallurgy and Geostatistics Lab - Annual Report 2019 

      Avalos, Sebastian; Bolgkoranou, Maria; Cevik, S. Ilkay; Kracht, Willy; Midkiff, William; Olivo, Gema Ribeiro; Ortiz, Julian M.; Rielo, Oscar; Riquelme, Alvaro I. (Predictive Geometallurgy and Geostatistics Lab, 2019)
      The research developed during the last year is summarized in the Annual Report 2019. This report is made available to the general public for reference. Collaboration from other research groups and from industry is encouraged. ...