Optimization of the Heap Leaching Process through Changes in Modes of Operation and Discrete Event Simulation

Optimization of the SAG Grinding Process Using Statistical Analysis and Machine Learning: A Case Study of the Chilean Copper Mining Industry

Considering the continuous increase in production costs and resource optimization, more than a strategic objective has become imperative in the copper mining industry. In the search to improve the efficiency in the use of resources, the present work develops models of a semi-autogenous grinding (SAG) mill using statistical analysis and machine learning (ML) techniques (regression, decision trees, and artificial neural networks). The hypotheses studied aim to improve the process's productive indicators, such as production and energy consumption. The simulation of the digital model captures an increase in production of 4.42% as a function of mineral fragmentation, while there is potential to increase production by decreasing the mill rotational speed, which has a decrease in energy consumption of 7.62% for all linear age configurations. Considering the performance of machine learning in the adjustment of complex models such as SAG grinding, the application of these tools in the mineral processing industry has the potential to increase the efficiency of these processes, either by improving production indicators or by saving energy consumption. Finally, the incorporation of these techniques in the aggregate management of processes such as the Mine to Mill paradigm, or the development of models that consider the uncertainty of the explanatory variables, could further increase the performance of productive indicators at the industrial scale.

Copper Mineral Leaching Mathematical Models—A Review

Mineral leaching is the key unit operation in metallurgical processes and corresponds to the dissolution of metals. The study of leaching is carried out in many areas, such as geology, agriculture and metallurgy. This paper provides an introduction to the theoretical background regarding the mathematical modelling of the leaching process of copper minerals, establishing an overall picture of the scientific literature on technological developments and the generation of representative mathematical and theoretical models, and indicating the challenges and potential contributions of comprehensive models representing the dynamics of copper mineral leaching.

Discrete Event Simulation for Machine-Learning Enabled Mine Production Control with Application to Gold Processing

Interdisciplinary barriers separating data scientists and geometallurgists have complicated systematic attempts to incorporate machine-learning into mine production management; however, experiences in excavating a vein-hosted gold deposit within the Alhué region of Chile have led to methodological advances, which is the subject of the current paper. These deposits are subject to several challenges, from increasing orebody complexity and decreasing gold grades to the significant geological uncertainty that is intrinsic to these systems. These challenges then translate to mineral processing, which is already dealing with increased environmental and technological constraints. Geological uncertainty causes stockout risks that can be mitigated by the approach that is developed within this paper, which features alternate operational modes and related control strategies. A digital twin framework based on discrete event simulation (DES) and a customized machine-learning (ML) model is proposed to incorporate geological variation into decision-making processes, including the setting of trigger point that induces mode changes. Sample calculations that were based on a simulated processing plant that was subject to mineralogical feed changes demonstrated that the framework is a valuable tool to evaluate and mitigate the potential risks to gold mineral processing performance.

Partial Least Squares Regression of Oil Sands Processing Variables within Discrete Event Simulation Digital Twin

Oil remains a major contributor to global primary energy supply and is, thus, fundamental to the continued functioning of modern society and related industries. Conventional oil and gas reserves are finite and are being depleted at a relatively rapid pace. With alternative fuels and technologies still unable to fill the gap, research and development of unconventional petroleum resources have accelerated markedly in the past 20 years. With some of the largest bitumen deposits in the world, Canada has an active oil mining and refining industry. Bitumen deposits, also called oil sands, are formed in complex geological environments and subject to a host of syn- and post-depositional processes. As a result, some ores are heterogeneous, at both individual reservoir and regional scales, which poses significant problems in terms of extractive processing. Moreover, with increased environmental awareness and enhanced governmental regulations and industry best practices, it is critical for oil sands producers to improve process efficiencies across the spectrum. Discrete event simulation (DES) is a computational paradigm to develop dynamic digital twins, including the interactions of critical variables and processes. In the case of mining systems, the digital twin includes aspects of geological uncertainty. The resulting simulations include alternate operational modes that are characterized by separate operational policies and tactics. The current DES framework has been customized to integrate predictive modelling data, generated via partial least squares (PLS) regression, in order to evaluate system-wide response to geological uncertainty. Sample computations that are based on data from Canada’s oil sands are presented, showing the framework to be a powerful tool to assess and attenuate operational risk factors in the extractive processing of bitumen deposits. Specifically, this work addresses blending control strategies prior to bitumen extraction and provides a pathway to incorporate geological variation into decision-making processes throughout the value chain.

A Decision Support System for Changes in Operation Modes of the Copper Heap Leaching Process

Chilean mining is one of the main productive industries in the country. It plays a critical role in the development of Chile, so process planning is an essential task in achieving high performance. This task involves considering mineral resources and operating conditions to provide an optimal and realistic copper extraction and processing strategy. Performing planning modes of operation requires a significant effort in information generation, analysis, and design. Once the operating mode plans have been made, it is essential to select the most appropriate one. In this context, an intelligent system that supports the planning and decision-making of the operating mode has the potential to improve the copper industry’s performance. In this work, a knowledge-based decision support system for managing the operating mode of the copper heap leaching process is presented. The domain was modeled using an ontology. The interdependence between the variables was encapsulated using a set of operation rules defined by experts in the domain and the process dynamics was modeled utilizing an inference engine (adjusted with data of the mineral feeding and operation rules coded) used to predict (through phenomenological models) the possible consequences of variations in mineral feeding. The work shows an intelligent approach to integrate and process operational data in mining sites, being a novel way to contribute to the decision-making process in complex environments.

Effects of Fe+2 and Fe+3 in Pretreatment and Leaching on a Mixed Copper Ore in Chloride Media

A study of the pretreatment stage and subsequent leaching of a mixed copper ore with different chloride solutions containing iron was carried out. The first stage considered pretreatment tests to decide the best conditions. Two levels of each factor were analyzed, 20 and 50 kg/t of NaCl, 17 and 25 kg/t of H2SO4, 0 and 25 kg/t of Fe2(SO4)3·9.2H2O, 0 and 25 kg/t of Fe2SO4·7H2O, and a curing time of 15 and 30 days. The results showed a significant effect of NaCl and curing time on the extraction, and less effect was found with the variation of acid and iron salts. The second stage included column leaching using a solution with 0.5 g/L of Cu+2, 80 g/L of Cl−, 10 g/L of H2SO4, and variable concentrations of ferric and ferrous ions (0 and 2 g/L). The best copper extraction of 80.2% was found considering a pretreatment of 30 days, 25 kg/t of H2SO4, 50 kg/t of NaCl, and a leaching solution concentration described previously with 2 g/L of Fe+2. The results showed the leaching of all copper oxide species and 20% of the copper sulfide species. In addition, there was a reduction in the acid consumption as the resting time increases. Furthermore, to evaluate a possible decrease in time and acid in pretreatment and chloride in leaching, tests including 10 and 25 kg/t of H2SO4 and 1, 15, and 30 days of curing and a diminution of the NaCl concentration to 20 g/L (content from seawater) were executed. The results showed a significant effect on curing time below 15 days. Furthermore, the slight influence of the decrease of acid on copper extraction gives cost reduction opportunities. The diminution of chloride concentration (80 to 20 g/L) in leaching solution decreases the extraction from 79% to 66.5%. Finally, the Mellado leaching kinetic model was successfully implemented.

Optimization of pulp and acid leaching operations in zinc ingot production process with the data mining approach

This study presents a data mining approach to optimize the chemical processes. Typically, these processes are affected by a variety of interactive variables. So, their quality monitoring and detection usually emphasize changing main variables and their interaction effects. Sometimes, the input to the chemical processes lacks access to the raw materials, which causes the manufacturers to use residue instead of high-quality materials. The use of residue has flaws, most notably the low quality of the process output. In this paper, calculating the optimum points of process variables using residue with the data mining approach is suggested. As a real case, one of the operations of the zinc ingot production process, i.e., pulp and acid leaching operations, are studied. In this way, first, by studying the operation in detail, the required data are collected, and key input and output variables are distinguished. Then, by data pre-processing, the optimum points of the process are determined using data mining algorithms. Therefore, the input variable settings of the operation are extracted to optimize the output variables. To validate the results, a set of test data are used to examine the two periods before and after the variable settings. The results show that the operation output is improved significantly. According to the robustness of the proposed method, it can be used as a benchmark for other chemical processes.

A Stochastic Model Approach for Copper Heap Leaching through Bayesian Networks

Multivariate analytical models are quite successful in explaining one or more response variables, based on one or more independent variables. However, they do not reflect the connections of conditional dependence between the variables that explain the model. Otherwise, due to their qualitative and quantitative nature, Bayesian networks allow us to easily visualize the probabilistic relationships between variables of interest, as well as make inferences as a prediction of specific evidence (partial or impartial), diagnosis and decision-making. The current work develops stochastic modeling of the leaching phase in piles by generating a Bayesian network that describes the ore recovery with independent variables, after analyzing the uncertainty of the response to the sensitization of the input variables. These models allow us to recognize the relations of dependence and causality between the sampled variables and can estimate the output against the lack of evidence. The network setting shows that the variables that have the most significant impact on recovery are the time, the heap height and the superficial velocity of the leaching flow, while the validation is given by the low measurements of the error statistics and the normality test of residuals. Finally, probabilistic networks are unique tools to determine and internalize the risk or uncertainty present in the input variables, due to their ability to generate estimates of recovery based upon partial knowledge of the operational variables.

Development of an Analytical Model for the Extraction of Manganese from Marine Nodules

Multivariable analytical models provide a descriptive (albeit approximate) mathematical relationship between a set of independent variables and one or more dependent variables. The current work develops an analytical model that extends a design of experiments for the leaching of manganese from marine nodules, using sulfuric acid (H2SO4) in the presence of iron-containing tailings, which are both by-products of conventional copper extraction. The experiments are configured to address the effect of time, particle size, acid concentration, Fe2O3/MnO2 ratio, stirring speed and temperature, under typical industrial conditions. The recovery of manganese has been modeled using a first order differential equation that accurately fits experimental results, noting that Fe2O3/MnO2 and temperature are the most critical independent variables, while the particle size is the least influential (under typical conditions). This study obtains representative fitting parameters, that can be used to explore the incorporation of Mn recovery from marine nodules, as part of the extended value chain of copper sulfide processing.