Costs of copper production

2022 ◽  
pp. 525-537
Author(s):  
Mark E. Schlesinger ◽  
Kathryn C. Sole ◽  
William G. Davenport ◽  
Gerardo R.F. Alvear Flores
Keyword(s):  
Author(s):  
Kholikulov Doniyor Bakhtiyorovich ◽  
Yakubov Nodirbek Maxmud Janovich

2021 ◽  
Author(s):  
Oliver Dixon ◽  
William McCarthy ◽  
Nasser Madani ◽  
Michael Petronis ◽  
Steve McRobbie ◽  
...  

<p>Copper is one of the most important critical metal resources needed to achieve carbon neutrality with a projected increase in demand of >300% over the next half century from electronics and renewables.  Porphyry deposits account for most of the global copper production, but the discovery of new reserves is ever more challenging. Machine learning presents an opportunity to cross reference new and traditionally under-utilised data sets with a view to developing quantitative predictive models of hydrothermal alteration zones to guide new, ambitious exploration programs.</p><p>The aim of this study is to demonstrate a new alteration classification scheme driven by quantitative magnetic and spectral data to feed a machine learning algorithm. The benefits of an alteration model based on quantitative data rather than subjective observations by geologists, are that there is no bias in the data collected, the arising model is quantifiable and therefore easy to model and the process be fully automated. Ultimately, this approach aids more detailed exploration and mine modelling, in turn, reducing the extraction process carbon footprint and more effectively identifying new deposits.</p><p>Presented here are magnetic susceptibility and shortwave infrared (SWIR) data collected from the KazMinerals plc. owned Aktogay Cu-Mo giant porphyry deposit, eastern Kazakhstan, which has a throughput of 30Mtpa of ore. These data are cross referenced using a newly developed machine learning algorithm. Generated autonomously, our results reveal twelve statistically and geologically significant clusters that define a new alteration classification for porphyry style mineralisation. Results are entirely non-subjective, reproducible, quantitative and modellable.</p><p>Importantly, magnetic susceptibility measurements improve the algorithm’s ability to identify clusters by between 29-36%; enhancing the sophistication of the included magnetic data promises to yield substantially better statistical results. Magnetic remanence data are therefore being complied on representative samples from each of the twelve identified clusters, including hysteresis, isothermal remanent magnetisation (IRM) acquisition, FORC measurements, natural remanent magnetisation (NRM) and anhysteretic remanent magnetisation (ARM). Through collaboration with industry partners, we aim to develop an automated means of collecting these magnetic remanence data to accompany the machine learning algorithm.</p>


2018 ◽  
Author(s):  
Simón Moreno-Leiva ◽  
Felipe Valencia ◽  
Jannik Haas ◽  
Dimitrij Chudinzow ◽  
Ludger Eltrop

2020 ◽  
Vol 8 (4) ◽  
pp. 351-360
Author(s):  
Matthew D. Howland ◽  
Brady Liss ◽  
Thomas E. Levy ◽  
Mohammad Najjar

AbstractArchaeologists have a responsibility to use their research to engage people and provide opportunities for the public to interact with cultural heritage and interpret it on their own terms. This can be done through hypermedia and deep mapping as approaches to public archaeology. In twenty-first-century archaeology, scholars can rely on vastly improved technologies to aid them in these efforts toward public engagement, including digital photography, geographic information systems, and three-dimensional models. These technologies, even when collected for analysis or documentation, can be valuable tools for educating and involving the public with archaeological methods and how these methods help archaeologists learn about the past. Ultimately, academic storytelling can benefit from making archaeological results and methods accessible and engaging for stakeholders and the general public. ArcGIS StoryMaps is an effective tool for integrating digital datasets into an accessible framework that is suitable for interactive public engagement. This article describes the benefits of using ArcGIS StoryMaps for hypermedia and deep mapping–based public engagement using the story of copper production in Iron Age Faynan, Jordan, as a case study.


JOM ◽  
1982 ◽  
Vol 34 (4) ◽  
pp. 60-63
Author(s):  
John G. Peacey

2021 ◽  
Author(s):  
Alexandra Escobar ◽  
Jorge Relvas ◽  
Alvaro Pinto ◽  
Mafalda Oliveira

<p>Neves Corvo is an underground high-grade Cu-(Sn)-Zn mine, currently producing copper, zinc and lead concentrates. Copper production started in 1989, followed by tin production, between 1990 and 2001, and zinc / lead production started in 2006. The operation is owned by SOMINCOR, a subsidiary of Lundin Mining, with a maximum capacity of 2.6Mtpy for the copper processing plant and 1.0Mtpy (ongoing expansion to 5.6Mtpy) for the zinc processing plant.</p><p>The Neves Corvo VMS deposit is located in the Portuguese part of the world-class Iberian Pyrite Belt (IPB) and is composed of seven orebodies. The Neves, Corvo, Zambujal and Lombador orebodies are currently in production, whereas the Semblana and Monte Branco orebodies are relatively recent discoveries still under development and evaluation, and the Graça orebody has been already fully mined.</p><p>From 2010 till end of 2019, the mine has accumulated 7.3Mt of waste rock and 17Mt of thickened tailings. These mining residues are stored in Cerro do Lobo Tailings Management Facility (Cerro do Lobo TMF), which completes a volume of 47Mt since the beginning of the operation in 1989 (30Mt are slurry tailings).</p><p>The deposition method changed in 2010 from slurry subaquatic deposition to sub-aerial thickened tailings stack (vertical expansion) in co-deposition with potentially acid-generating (PAG) waste rock. The thickened tailings have an average of 63% solids. X-ray fluorescence analysis have shown copper and zinc grades variation in the waste rock between 0.3 and 0.9%, and 0.4% and 1.1%, respectively, and concentrations up to 0.3% and 0.4% of copper and zinc, respectively, in the tailings.</p><p>Mineralogically, the tailings consist mainly in pyrite, sphalerite, chalcopyrite, +/- arsenopyrite, +/- tetrahedrite-tennantite, gangue minerals such as quartz, phyllosilicates, carbonates and some oxides, and have a non-uniform particle size distribution ranging between 1 and 100 µm. The waste rock fraction is millimetric to centimetric in size, and is formed by the local host rocks, which include acid volcanic rocks, schists and graywackes, all of them containing variably significant disseminated sulfides, largely dominated by pyrite.</p><p>On-going research is being undertaken aiming to build a geometallurgical model for the Neves Corvo mine, ground on a huge database on the chemical and mineralogical composition, and particle size distribution of the mine tailings, coupled with (and calibrated by) new analytical and automated data acquired in a large set of carefully selected representative samples, in order to assess the potential recovery of base metals and their by-products out of these potentially valuable mine residues. The model construction and consequent resource estimation will be based on the daily monitoring of the tailings deposition at the disposal units, over the past 10 years (i.e., since the subaerial deposition has started at Neves Corvo), in terms of volume/tonnage, chemical and mineralogical compositions and physical characterization of the material.</p><p>This study is part of the work package 1 (WP1) of ETN–SULTAN project (H2020) - European Training Network for the remediation and reprocessing of sulfidic mining waste sites. Publication supported by FCT- Project UID/GEO/50019/2019 - Instituto Dom Luiz.</p>


2021 ◽  
Vol 22 (1) ◽  
pp. 21-29
Author(s):  
K. A. Linnik ◽  
◽  
A. S. Sharipova ◽  
A. N. Zagorodnyaya ◽  
S. T. Akchulakova

The results of experiments for the study of behavior of lead and selenium during the leaching process of slurry by a solution of trylon B depending on parameters typical for hydrometallurgical processes and their ranges are presented. It has been found out that trylon B practically completely extracts lead into the solution, selenium is concentrated in cake. However, the process is accompanied by precipitation of ethylenediaminetetraacetic acid (C10H16N2O8). Selenium-containing substances were determined in slurry and cakes.


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