Given the active growth of emerging technology industries, it has become essential to have large quantities of critical metals to meet the current demand. In the Chilean mining industry, there is a depletion of high-grade mineral ores, and there is hence a need to increase production levels in the copper industry and diversify its market by extracting other elements. One of the strategies is to foster the production of lithium batteries, but the manufacture requires reserves of cobalt (Co) and manganese (Mn). Currently, Co reserves are not being exploited in Chile, and Mn production is almost negligible. This is due to the apparent shortage of high-grade ores on the land surface of the country. Given this scenario, the seabed manganese nodules are presented as a good alternative due to their high average grades of Co and Mn, which in turn would allow the growth of strategic value-added industries including lithium battery production. Chile’s current environmental regulations prevent the exploitation of marine resources. However, given technological advances worldwide, both in collection mechanisms and extractive processes, in addition to the needs generated from the future strategic plans, leads us to think about a project to exploit manganese nodules locally.

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.

Digital technologies are continually gaining traction in the mining and mineral processing industries. Several studies have shown the benefits of their application to help improve various aspects of the mineral value chain. Nevertheless, quantitatively assessing new technologies using a holistic approach is vital to evaluate whether the potential localized benefits ultimately translate to an overall increase in project net present value (NPV). This study develops an integrated system-wide methodology for open-pit mines, supporting the technoeconomic assessment of implementing new technology that impacts strategic and operational timeframes. The first part of the framework relies on a state-of-the-art mine plan optimization algorithm that incorporates geological uncertainty. The resulting outputs are then fed into the discrete event simulation portion of the framework (second part) to maximize plant throughput using alternate modes of operation (blending strategy) and operational stockpiles to deal with unexpected changes in ore feed attributes. Sample calculations loosely based on a gold deposit located in the Maricunga belt, Chile, are presented in the context of evaluating different intelligent ore sorting technology options.

The Alhué deposit (Melipilla, Chile) is an example of a hydrothermal Au-Ag-Zn(-Pb) vein system hosted within the volcanic rocks of the Las Chilcas Formation. The dominant ore minerals observed are free electrum and native gold associated with silver sulfosalts, and with magnetite and base metal sulphides, including pyrite +/− sphalerite-galena-chalcopyrite. The alteration assemblage in the veins mainly consists of quartz epidote-chlorite-actinolite with lesser smectite, amphibole, and calcite-kaolinite-garnet. Mineralized veins also contain variable amounts of base metals, some of which (e.g., copper and iron) are considered harmful to the extraction of precious metals. Iron and especially copper minerals are known cyanide consumers; ore type classification schemes that do not consider the detrimental effects of such mineralogy or process elements can ultimately result in metal losses from ore feed restrictions, as well as spikes in cyanide consumption and higher operating costs. Mineralogical and geological variation can nonetheless be managed by applying alternating modes of operation as demonstrated in this paper; the decision to switch between modes is governed by current and forecasted stockpile levels feeding into the process. Simulations based on experiences at the Alhué deposit are provided that demonstrate the importance of standardized operational modes and their potential impact on cyanide consumption control.

Mineral and metallurgical processing are crucial within the mineral value chain. These processes involve several stages wherein comminution is arguably the most important due to its high energy consumption, and its impact on subsequent extractive processes. Several geological properties of the orebody impact the efficiency of mineral processing and extractive metallurgy; scholars have therefore proposed to deal with the uncertain ore feed in terms of grades and rock types, incorporating operational modes that represent different plant configurations that provide coordinated system-wide responses. Even though these studies offer insights into how mine planning impacts the ore fed into the plant, the simultaneous optimization of mine plan and metallurgical plant design has been limited by the existing stochastic mine planning algorithms, which have only limited support for detailing operational modes. The present work offers to fill this gap for open-pit mines through a computationally efficient adaptation of a strategic mine planning algorithm. The adaptation incorporates a linear programming representation of the operational modes which forms a Dantzig-Wolfe decomposition, nested within a high-performing stochastic mine planning algorithm based on a variable neighborhood descent metaheuristic. Sample calculations are presented, loosely based on the Mount Isa deposit in Australia, in which a metallurgical plant upgrade is evaluated, showing that the upgraded design significantly decreases the requirement on the mining equipment, without significantly affecting the NPV.

The Alhué mining district, Chile, is an example of a high-grade Au-Ag-Zn(-Pb) deposit with mineralized veins that contain variable amounts of copper sulfides, which are detrimental to the cyanidation process. Similar deposits can be found in the central zone of Chile, with polymetallic veins (Au, Ag, Cu, Pb, and Zn) that are related to subvolcanic intrusive events, the development of collapse calderas, and extensive hydrothermal alteration, such as Bronces de Petorca, the Chancón mining district and Cerro Cantillana; areas of the world with similar formations include the western United States and the Henan Province in central China, for example. Mineralogical variation can be managed within the metallurgical process by alternating its operational modes. The decision to switch between modes is governed by current and forecasted stockpile levels feeding into the process, according to a discrete rate simulation (DRS) framework that has now been developed to incorporate head grade data for gold. Customized simulations that incorporate probability distribution models using head grade have now been developed, following a statistical analysis based on data from the Alhué district. This study applies data-driven simulation modeling to represent standardized operational modes and their impact on the operational performance of gold extraction.