Electrocoagulation Process for Recovery of Precious Metals from Cyanide Leachates Using a Low Voltage

IF 4.3 Q2 ENGINEERING, CHEMICAL ACS Engineering Au Pub Date : 2023-12-05 DOI:10.1021/acsengineeringau.3c00041
Juan Carlos Soto-Uribe*, Jesus Leobardo Valenzuela-Garcia*, Maria Mercedes Salazar-Campoy*, José Refugio Parga-Torres, Víctor Manuel Vazquez-Vazquez, Martin Antonio Encinas-Romero and Guadalupe Martinez-Ballesteros, 
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Abstract

The cyanidation of gold ores with copper content is frequent in gold mines. Copper affects the performance and profits of mineral processing. The current technology for gold recovery from cyanide solutions usually involves the adsorption of the gold-cyanide complex ion on activated carbon; however, the copper affects this process. The process of electrocoagulation (EC) is a promising technique for gold and silver recovery with copper, where all of the metals can be recovered. This work used the electrocoagulation process (EC) to evaluate the metal recovery from a pregnant leach solution (PLS), where EC is a promising technique. This study aimed to determine the optimal parameter to recover the gold and silver and to see the effect of copper concentration in the PLS obtained by simultaneous pressure leaching/oxidation of a gold-bearing pyritic concentrate. EC tests were run to recover gold and silver over copper from PLS using aluminum electrodes and variables like distances between electrodes, pH, potential applied, and feeding flow for continuous EC. The chemical assay of cyanide leachates shows a concentration of 7.15 mg/L of gold, 305 mg/L of silver, and 351.5 mg/L of copper with 1810 mg/L of free cyanide. The results showed that the EC process recovered 99% of gold and copper and 92% of silver at a pH of 11, 8 mm of dE, and a potential applied 3 V in 10 min. However, under this condition in continuous EC with a flow rate of 40 mL/min, the recovery is 66.3% of gold, 85.8% of silver, and 45.3% of copper; compared with the batch process, the gold and silver decrease.

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低电压电絮凝法回收氰化浸出液中的贵金属
在金矿中,含铜金矿氰化反应较为常见。铜影响矿物加工的性能和利润。目前从氰化物溶液中回收金的技术通常是将金-氰化物络合物离子吸附在活性炭上;然而,铜会影响这一过程。电絮凝法是一种很有前途的从铜中回收金银的技术,它可以回收所有的金属。本研究采用电凝法(EC)对浸出液(PLS)中金属的回收进行了评价,电凝法是一种很有前途的技术。本研究旨在确定回收金银的最佳参数,并考察铜浓度对含金黄铁矿精矿同时加压浸出/氧化所得PLS的影响。使用铝电极和诸如电极之间的距离、pH值、施加的电位和连续EC的进料流量等变量,运行EC测试以从PLS中回收金和银。氰化物渗滤液的化学分析表明,金的浓度为7.15毫克/升,银的浓度为305毫克/升,铜的浓度为351.5毫克/升,游离氰化物为1810毫克/升。结果表明,在pH = 11.8 mm dE、电压为3 V的条件下,电催化工艺在10 min内可回收99%的金、铜和92%的银。在此条件下,在流量为40 mL/min的连续电催化条件下,金、银、铜的回收率分别为66.3%、85.8%和45.3%;与间歇法相比,金和银的含量有所降低。
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ACS Engineering Au
ACS Engineering Au 化学工程技术-
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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)
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