Diffusion coefficient of cupric ion in a copper electrorefining electrolyte containing nickel and arsenic. (April 2019)

• Record Type: Journal Article
• Authors: Kalliomäki, Taina; Wilson, Benjamin P.; Aromaa, Jari; Lundström, Mari
• Is Part Of: Minerals engineering. Volume 134(2019)
• Page Start: 381
• Page End: 389
• Publication Date: 2019-04
• Subjects: Copper electrorefining electrolyte -- Diffusion coefficient -- Cupric ion -- Arsenic impurities; Mines and mineral resources -- Periodicals; Ressources minérales -- Périodiques; Mines and mineral resources; Periodicals; Electronic journals; 622
• DOI: 10.1016/j.mineng.2019.02.027
• Languages: English
• ISSNs: 0892-6875

Abstract

Highlights: The effects of electrolyte composition and temperature on DCu(II) were modeled. An increase in arsenic concentration decreased the DCu(II) . DCu(II) is highest at the highest temperature and the lowest [H2 SO4 ] and [metals]. The built model can be used for predicting the DCu(II) of industrial electrolytes. Abstract: Diffusion and convection are the main modes of mass transport that occur during copper electrorefining. Diffusion determines the rate of copper transfer across the diffusion layer, which in turn, affects the dissolution of the anode and deposition on the cathode. The diffusion coefficient of cupric ion (DCu(II) ) is a typical property that can be defined from the limiting current density ( j lim ) values. In this work, the limiting current densities were measured for 24 different synthetic copper electrolytes over a temperature range of 50–70 °C using a rotating disc electrode (RDE). From this data, a model for j lim and the corresponding models for DCu(II) were constructed using Levich (Model L), Koutecký-Levich (Model K) and mixed-control Newman equations (Model M). The models for j lim and DCu(II) were designed, refined and analyzed using the modeling and design tool MODDE, with the temperature, copper, nickel, arsenic and sulfuric acid concentrations as variables. Results from this research show for the first time that an increase in arsenic concentration has a reciprocal effect on the DCu(II) under copper electrorefining conditions. Furthermore, the models were validated with 11 industrial electrorefining electrolytes with known compositions. Model L (DCu(II) based on Levich equation) was shown to provide the highest correlation with the industrial solutions when compared to the other models (Model K and M) considered and previously published diffusion coefficient models. Overall, this work provides an explanation for the previously observed data variances in the literature, investigates for the first time the combined effect of parameters on DCu(II) value in industrial copper electrolysis and clarifies the effect of arsenic on the DCu(II) of copper electrorefining electrolytes.