Abstract
In copper electrorefining and electrowinning, the conductivity and viscosity of the electrolyte affect the energy consumption, and for electrorefining the purity of cathode copper. Consequently, accurate models for predicting these properties are highly important. Although the modeling of conductivity and viscosity of synthetic copper electrolytes has been previously studied, only a few models have been validated with actual industrial electrolytes. The conductivity and viscosity models outlined in this study were developed using conductivity and viscosity measurements from both synthetic and industrial solutions. The synthetic electrolytes were investigated over a temperature range between 50–70 °C and typical concentrations of Cu (40–90 g/dm3), Ni (0–30 g/dm3), Fe (0–10 g/dm3), Co (0–5 g/dm3), As (0–63.8 g/dm3), H2SO4 (50–223 g/dm3) as well as other solution impurities like Sb in some cases. Validation of the synthetic electrolyte models was performed through industrial measurements at three copper plants across Europe. Generally, the developed models predicted the conductivities and viscosities of industrial solutions with high accuracy. The viscosity models covered extended ranges of both [H2SO4] and [Cu] with percentage errors of only (2.08 ± 0.59) - (2.48 ± 0.61). For conductivity, two different models for low (<142 g/dm3) and high (>142 g/dm3) [H2SO4] electrolytes were utilized. Their error margins were (−1.96 ± 0.84) - (−1.44 ± 0.35) and (1.17 ± 0.27) - (2.52 ± 0.28), respectively. In the case of high [H2SO4] electrolytes, the validations focused on conductivity, and the highest level of accuracy was obtained when the effects of Sb and other minor impurities were considered.
Highlights
Copper is one of the most important industrial metals and is third, by consumption, after iron and aluminum (U.S Geological Survey, 2018)
The regression models for conductivity in electrorefining and electrowinning electrolytes are displayed in Table 4 as the generic form (Eq (5))
The model κER4, includes the effect of [Minor impurities] ([Sb] + [Bi] + [Pb] + [Te] + [Se]) and this sum term was utilized because Sb and Bi were found to have insignificant individual effects on conductivity at the concentrations found in industry (Fig. S1 and Table S1, Supplementary Material)
Summary
Copper is one of the most important industrial metals and is third, by consumption, after iron and aluminum (U.S Geological Survey, 2018). In Cu-EW, the value of electrolyte resistance is of similar magnitude, its proportion of the total energy consumption is lower (12–24%). This is due to difference in anodic and cathodic re actions, increasing the cell voltage as well as the overpotential for anodic reaction (26–33%) (Beukes and Badenhorst, 2009; Schlesinger et al, 2011; Schmachtel, 2017). Energy consumption in electrorefining and electrowinning processes can be minimized by optimization of the electrolyte conductivity and viscosity. Conductivity directly affects the energy consumption in terms of cell voltage, whilst viscosity influences the mass transport of Cu – increased viscosity results in increased diffusion overpotential, which impacts on the energy efficiency (Jar joura et al, 2003)
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