{"title":"Hydrometallurgical Processing of Chalcopyrite by Attrition-Aided Leaching","authors":"Amine Dakkoune, Florent Bourgeois, Adeline Po, Catherine Joulian, Agathe Hubau, Solène Touzé, Carine Julcour*, Anne-Gwénaëlle Guezennec and Laurent Cassayre, ","doi":"10.1021/acsengineeringau.2c00051","DOIUrl":null,"url":null,"abstract":"<p >We report the investigation of a chalcopyrite leaching process that implements millimeter-sized glass beads that are stirred in the leach reactor to combine particle grinding, mechanical activation, and surface removal of reaction products. The paper focuses on demonstrating the impact of the so-called attrition-leaching phenomenon on the leaching rate of a chalcopyrite concentrate and provides a first understanding of the underlying mechanisms. For this purpose, we have compared the copper leaching yield for different configurations under controlled chemical conditions (1 kg of glass beads and 84 g of chalcopyrite concentrate in 2.5 L of H<sub>2</sub>SO<sub>4</sub>-H<sub>2</sub>O solution, pH = 1.3, <i>E</i><sub>h</sub> = 700 mV vs SHE, and <i>T</i> = 42 °C). On top of elemental analysis of the leach solution with time, we provide a full characterization of the solid residue based on X-ray diffraction, elemental analysis, and sulfur speciation. We demonstrate that glass beads led to a remarkable enhancement of the leaching rate in conditions where particles were already passivated by simple leaching and even when large amounts of solid products (elemental sulfur and jarosite) were present. An in-depth evaluation of particle size distribution showed that particle breakage occurred during a rather short time (a few hours) at the beginning of the runs, transforming the initial particles with <i>d</i><sub>4/3</sub> = 30 μm to finer particles with <i>d</i><sub>4/3</sub> = 15 μm. Then, particle breakage almost stopped, while an attrition phenomenon was evidenced, inducing the formation of very fine particles (<1 μm) and aggregates concomitantly with copper leaching.</p>","PeriodicalId":29804,"journal":{"name":"ACS Engineering Au","volume":null,"pages":null},"PeriodicalIF":4.3000,"publicationDate":"2023-03-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://pubs.acs.org/doi/epdf/10.1021/acsengineeringau.2c00051","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"ACS Engineering Au","FirstCategoryId":"1085","ListUrlMain":"https://pubs.acs.org/doi/10.1021/acsengineeringau.2c00051","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"ENGINEERING, CHEMICAL","Score":null,"Total":0}
引用次数: 0
Abstract
We report the investigation of a chalcopyrite leaching process that implements millimeter-sized glass beads that are stirred in the leach reactor to combine particle grinding, mechanical activation, and surface removal of reaction products. The paper focuses on demonstrating the impact of the so-called attrition-leaching phenomenon on the leaching rate of a chalcopyrite concentrate and provides a first understanding of the underlying mechanisms. For this purpose, we have compared the copper leaching yield for different configurations under controlled chemical conditions (1 kg of glass beads and 84 g of chalcopyrite concentrate in 2.5 L of H2SO4-H2O solution, pH = 1.3, Eh = 700 mV vs SHE, and T = 42 °C). On top of elemental analysis of the leach solution with time, we provide a full characterization of the solid residue based on X-ray diffraction, elemental analysis, and sulfur speciation. We demonstrate that glass beads led to a remarkable enhancement of the leaching rate in conditions where particles were already passivated by simple leaching and even when large amounts of solid products (elemental sulfur and jarosite) were present. An in-depth evaluation of particle size distribution showed that particle breakage occurred during a rather short time (a few hours) at the beginning of the runs, transforming the initial particles with d4/3 = 30 μm to finer particles with d4/3 = 15 μm. Then, particle breakage almost stopped, while an attrition phenomenon was evidenced, inducing the formation of very fine particles (<1 μm) and aggregates concomitantly with copper leaching.
期刊介绍:
)ACS Engineering Au is an open access journal that reports significant advances in chemical engineering applied chemistry and energy covering fundamentals processes and products. The journal's broad scope includes experimental theoretical mathematical computational chemical and physical research from academic and industrial settings. Short letters comprehensive articles reviews and perspectives are welcome on topics that include:Fundamental research in such areas as thermodynamics transport phenomena (flow mixing mass & heat transfer) chemical reaction kinetics and engineering catalysis separations interfacial phenomena and materialsProcess design development and intensification (e.g. process technologies for chemicals and materials synthesis and design methods process intensification multiphase reactors scale-up systems analysis process control data correlation schemes modeling machine learning Artificial Intelligence)Product research and development involving chemical and engineering aspects (e.g. catalysts plastics elastomers fibers adhesives coatings paper membranes lubricants ceramics aerosols fluidic devices intensified process equipment)Energy and fuels (e.g. pre-treatment processing and utilization of renewable energy resources; processing and utilization of fuels; properties and structure or molecular composition of both raw fuels and refined products; fuel cells hydrogen batteries; photochemical fuel and energy production; decarbonization; electrification; microwave; cavitation)Measurement techniques computational models and data on thermo-physical thermodynamic and transport properties of materials and phase equilibrium behaviorNew methods models and tools (e.g. real-time data analytics multi-scale models physics informed machine learning models machine learning enhanced physics-based models soft sensors high-performance computing)