Investigation of the kinetics and the morphology of cementation products formed during purification of a synthetic zinc sulfate electrolyte

Karlsson, Toni; Cao, Yu; Colombus, Yuda Chryspian; Steenari, Britt‐Marie

doi:10.1016/j.hydromet.2018.09.007

Cited by 26 publications

(8 citation statements)

References 23 publications

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“…As shown in Fig. 1, it is found that the cementation Cu precipitate consists of fine particles, which shows a good agreement with conventional studies, 19,20) and it contains Cu as main component and Cu 2 O, which resulted from partly oxidized surface.…”

Section: Resultssupporting

confidence: 84%

“…It is almost impossible to detect Cu + ion during the leaching test because Cu + ions are extremely unstable in sulfuric acid solution. 11,16,18,19) Therefore, the effects of Cu 2+ and O 2 addition on the leaching of Cu were investigated experimentally as follows: Three conditions such as addition of O 2 with 10000 mg/L Cu 2+ , addition of N 2 with 10000 mg/L Cu 2+ , and addition of O 2 without 10000 mg/L Cu 2+ were tested for comparison. Figure 3 shows the leaching behavior of Cu in 1 mol/L sulfuric acid solution at 90°C and 600 rpm with 2% pulp density.…”

Section: Resultsmentioning

confidence: 99%

“…Therefore, a new leaching process using both Cu 2+ and O 2 in sulfuric acid solution was proposed to leach Cu metal, 19) and the leaching of Cu metal is accomplished as follows. 16)…”

Section: Introductionmentioning

confidence: 99%

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Leaching of Copper from Cementation Precipitate in Sulfuric Acid Solution with Cupric Ion and Oxygen

Yoo

Jeon

et al. 2022

Mater. Trans.

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In zinc hydrometallurgical processes, copper was removed as copper precipitate powder by a cementation process, and, in the present study, leaching medium using Cu 2+ and O 2 in sulfuric acid solution was proposed to enhance the leaching rate of Cu from the cementation Cu precipitate powder. The leaching efficiency of Cu increased with increasing agitation speed, temperature, and initial Cu 2+ concentration and decreasing pulp density. When the effect of gas type on the leaching was examined, the leaching efficiency was faster in the order of O 2 , air, and N 2 . The leaching efficiency of Cu increased to 100% in 1 mol/L sulfuric acid solution at 70°C and 600 rpm with 2% pulp density, 10000 mg/L Cu 2+ and 1000 mL/min O 2 . In the kinetic study, the leaching behavior of Cu followed the reaction-controlled model, and the activation energy was determined to be 19.2 kJ/mol. Therefore, these results suggest that the addition of Cu 2+ and O 2 could enhance the leaching rate of Cu from the cementation Cu precipitate.

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Section: Resultssupporting

confidence: 84%

Section: Resultsmentioning

confidence: 99%

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Leaching of Copper from Cementation Precipitate in Sulfuric Acid Solution with Cupric Ion and Oxygen

Yoo

Jeon

et al. 2022

Mater. Trans.

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“…Almost all the data published [ 2 , 3 , 4 , 5 , 6 ] showed that the activation energy of cobalt removal reaction was higher than 40 kJ/mol, so it must be a chemically controlled process rather than a diffusion-controlled one. The electrochemical reaction for cobalt removal occurs on the microcell surface and is driven by the potential difference between the microcell surface and the solution.…”

Section: Cobalt–hydrogen Electrochemical Competition Model For the Cobalt Removal Processmentioning

confidence: 99%

“…Exposed zinc on the microcell surface undergoes oxidation to become zinc ions while cobalt ions obtain electrons and are reduced to elemental cobalt accompanied by reduction of hydrogen ions to hydrogen molecules. Low pH conditions (pH = 3–5) are usually adopted for cobalt removal to avoid zinc ions from forming basic zinc sulfate, which would deactivate the zinc dust by absorbing it onto its surface [ 5 ]. However, hydrogen ions have greater reactivity than cobalt ions on the zinc surface thereby making high-concentration hydrogen ions compete with cobalt ions for electrons result in a significant drop in the removal rate of cobalt [ 6 ].…”

mentioning

confidence: 99%

Constrained Parameter Estimation for a Mechanistic Kinetic Model of Cobalt–Hydrogen Electrochemical Competition during a Cobalt Removal Process

Liang

Zhang

2021

Entropy

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A mechanistic kinetic model of cobalt–hydrogen electrochemical competition for the cobalt removal process in zinc hydrometallurgical was proposed. In addition, to overcome the parameter estimation difficulties arising from the model nonlinearities and the lack of information on the possible value ranges of parameters to be estimated, a constrained guided parameter estimation scheme was derived based on model equations and experimental data. The proposed model and the parameter estimation scheme have two advantages: (i) The model reflected for the first time the mechanism of the electrochemical competition between cobalt and hydrogen ions in the process of cobalt removal in zinc hydrometallurgy; (ii) The proposed constrained parameter estimation scheme did not depend on the information of the possible value ranges of parameters to be estimated; (iii) the constraint conditions provided in that scheme directly linked the experimental phenomenon metrics to the model parameters thereby providing deeper insights into the model parameters for model users. Numerical experiments showed that the proposed constrained parameter estimation algorithm significantly improved the estimation efficiency. Meanwhile, the proposed cobalt–hydrogen electrochemical competition model allowed for accurate simulation of the impact of hydrogen ions on cobalt removal rate as well as simulation of the trend of hydrogen ion concentration, which would be helpful for the actual cobalt removal process in zinc hydrometallurgy.

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Distributed nonlinear model predictive control for cobalt removal process in zinc hydrometallurgy considering error compensation modelling

Wang

Tang

et al. 2023

Can J Chem Eng

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To address the strong nonlinearity, uncertainty, and mutual coupling in the cobalt removal process of zinc hydrometallurgy, an algorithm based on an improved genetic algorithm (GA) backpropagation (BP) neural network combined with distributed nonlinear model predictive control (NMPC) is proposed. This method was applied to improve the quality of the purification solution and reduce the consumption of zinc powder, overcoming the challenges faced by the current cobalt removal process. First, a synergistic continuously stirred tank reactor (SCSTR) model was constructed for the dynamic cobalt removal process. Second, aiming at the problem that a single SCSTR model has difficulty describing the process accurately, based on the highly nonlinear mapping ability of data‐driven models, a method that organically integrates the SCSTR model and an error compensation model based on the GA‐BP neural network was proposed (GA‐BP‐SCSTR) to provide a more accurate online prediction of the process indicators. Then, a distributed NMPC architecture was developed using the GA‐BP‐SCSTR model, control vector parameterization (CVP) technique, and sequential quadratic programming (SQP) algorithm to achieve the coordinated control of the cobalt removal process. Finally, simulation results of an actual site showed that the prediction accuracy of the GA‐BP‐SCSTR model was higher than those of other models. The proposed predictive control method can maintain the outlet cobalt ion concentrations at the set values while achieving accurate control of the zinc powder addition. This approach can provide guidance for on‐site production and eliminate the blindness of manual experience control.

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Investigation of the kinetics and the morphology of cementation products formed during purification of a synthetic zinc sulfate electrolyte

Cited by 26 publications

References 23 publications

Leaching of Copper from Cementation Precipitate in Sulfuric Acid Solution with Cupric Ion and Oxygen

Leaching of Copper from Cementation Precipitate in Sulfuric Acid Solution with Cupric Ion and Oxygen

Constrained Parameter Estimation for a Mechanistic Kinetic Model of Cobalt–Hydrogen Electrochemical Competition during a Cobalt Removal Process

Distributed nonlinear model predictive control for cobalt removal process in zinc hydrometallurgy considering error compensation modelling

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