Gangues and Clays Minerals as Rate-Limiting Factors in Copper Heap Leaching: A Review

Heap leaching is a firm extractive metallurgical technology facilitating the economical processing of different kinds of low-grade ores that are otherwise not exploited. Nevertheless, regardless of much development since it was first used, the process advantages are restricted by low recoveries and long extraction times. It is becoming progressively clear that the selection of heap leaching as an appropriate technology to process a specific mineral resource that is both environmentally sound and economically feasible very much relies on having an ample understanding of the essential underlying mechanisms of the processes and how they interrelate with the specific mineralogy of the ore body under concern. This paper provides a critical overview of the role of gangues and clays minerals as rate-limiting factors in copper heap leaching operations. We aim to assess and deliver detailed descriptions and discussions on the relations between different gangues and clays minerals and their impacts on the operational parameters and chemical dynamics in the copper heap leaching processes.

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.

Stabilizing Arsenic in Copper Heap Leaching Residues

The need to sustainably produce raw materials encourages mining companies to develop and incorporate new economically and environmentally efficient processes. Therefore, there is a need to investigate the behavior and stabilization of hazardous elements present in effluents from metal recovery processes such as arsenic. This study evaluates the incorporation of an effluent solution from a copper smelter that is to be treated in a copper hydrometallurgical plant (heap leaching). The treatment is applied to recover compounds of interest such as copper, acid and water, in addition to confining impurities as stable residues in the leach residues. Here, we assess the capacity of the mineral to retain arsenic. To do this, a mixed solution of effluent and process solution was prepared, with a concentration of 1 g/L of arsenic. The solution was irrigated in leach columns loaded with a heap mineral with varying pH levels (0.8; 1.5 and 2) and solution potentials (510 and 540 mV). The concentrations of arsenic and iron in the solution and in the solid residues were measured to determine the capacity of the mineral to retain arsenic and how it was retained. The pH level plays an important role since, at a higher pH, the presence of arsenic and iron in the solution decreases, therefore increasing in the solid residue. Finally, a retention of 57% of arsenic is reached at pH 2. The characterization of the residues by scanning electron microscopy (SEM) confirms that arsenic is associated with Fe, S and O, forming ferric arsenates, while an X-Ray analysis identifies the arsenic compounds as crystalline scorodite.

Management of Copper Heap Leach Projects: A Geologist’s Perspective

Editor’s note: The Geology and Mining series, edited by Dan Wood and Jeffrey Hedenquist, is designed to introduce early-career professionals and students to a variety of topics in mineral exploration, development, and mining, in order to provide insight into the many ways in which geoscientists contribute to the mineral industry. Abstract Copper production by heap leaching, coupled with solvent extraction and electrowinning (SX-EW), is a well-established technology, with an annual output of about 3.7 million tonnes (Mt) of copper metal. Ores presently amenable to copper heap leaching include copper oxides and secondary copper sulfides. Most copper deposits amenable to acid sulfate heap leaching result from supergene processes within porphyry copper systems, although copper heap leaching has been applied to sandstone and shale-hosted deposits, among others. Copper heap leaching is a rate-dependent process sensitive to copper mineralogy (copper oxides > secondary sulfides > hypogene sulfides), driven by the pH of the leach solution, the activity of ferric iron (Fe3+ (aq)) dissolved in the leach solution, and temperature. Acid consumption, a principal operating cost item, depends on the pH of the leach solution; the presence of reactive gangue minerals, notably carbonates, Ca plagioclase, pyroxene, Fe-rich amphibole, and olivine; and the cumulative surface area of material in the heap. There are three basic approaches to commercial copper heap leaching—run-of-mine, dedicated pad, and on-off pad leaching, with variables that include crushing, acid/ferric agglomeration, solution application rate, and leach solution pH. These approaches affect copper leach kinetics, overall copper recovery, acid consumption, and capital and operating costs. A successful copper heap leach evaluation program requires a systematic approach, beginning with geologic mapping, then drilling and hydraulic and metallurgical testing, and concluding with financial analysis, engineering, and permitting. As geologists are the unique party in the process, with a thorough understanding of the overall deposit geology, including ore and gangue mineralogy, the domains that comprise the deposit, and the geochemistry of leaching, they must remain fully involved in the project throughout the evaluation. At the outset, geologists must manage the drilling program and define the grade-mineral domains. Later, they must participate in the metallurgical and hydraulic testing programs, including the evaluation of test results; then, during financial modeling, they must collaborate with all of the other specialists.

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.