Continuous electrolytic refining process of cathode copper with non-dissolving anode

Ding, Lifeng; Cheng, Jinrong; Wang, Tong; Zhao, Junliang; Chen, Chongyan; Niu, Yulan

doi:10.1016/j.mineng.2019.02.032

Cited by 24 publications

(6 citation statements)

References 24 publications

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“…In general, the addition of gelatin as an additive to the copper electrodeposition process has a significant impact on the surface morphology of the copper deposition layer. 50,51 When the gelatin concentration is lower than 0.02 g/L, increasing the gelatin concentration is beneficial to grain refinement. The effect of further increasing the concentration of gelatin and inhibiting the growth of crystal grains is not obvious.…”

Section: Kinetic Parameter Studymentioning

confidence: 99%

Gelatin Effects on the Kinetic of an Electrodeposition Process in Copper Foil Preparation

Li,

Chen,

Zhou

et al. 2024

J. Phys. Chem. C

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In the electrolysis of copper foil, the addition of trace additives can adjust the kinetic parameters of the copper electrochemical process to control the crystal orientation. This is of great significance for the production of ultrathin copper foil and its application in lithium-ion batteries. The reaction rate constant and diffusion coefficient determine the rate of the electrochemical and mass transfer processes around the electrode−electrolyte interface. In this study, the influences of gelatin on the kinetic parameters of copper electrodeposition were fully investigated. An electrochemical study found that gelatin could make the copper deposit more difficult to dissolve and that an increase in gelatin concentration would reduce the charge transfer rate of the Cu 2+ → Cu reaction. An RDE study found that the addition of gelatin reduced the reaction rate constant of the Cu 2+ → Cu reaction and increased the diffusion coefficient of Cu 2+ in the electrolyte. Increasing the gelatin concentration from 0.002 to 0.02 g/L was beneficial to the grain refinement of the deposited layer. After the addition of gelatin, the copper deposit layer grew preferentially on the (111) crystal plane.

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Section: Kinetic Parameter Studymentioning

confidence: 99%

Gelatin Effects on the Kinetic of an Electrodeposition Process in Copper Foil Preparation

Li,

Chen,

Zhou

et al. 2024

J. Phys. Chem. C

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“…Copper is an important non-ferrous metal in the manufacturing and construction industries, with regards to which electrolytic refining is one of the main methods of processing crude copper into refined copper [1]. As shown in figure 1, in the electrolytic refining process, copper is dissolved into copper ions from a fire-treated anode plate and passes through an electrolyte * Author to whom any correspondence should be addressed.…”

Section: Introductionmentioning

confidence: 99%

An attention-based long short-term memory prediction model for working conditions of copper electrolytic plates

Zhu

Pang

Zhou

et al. 2023

Meas. Sci. Technol.

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Copper is an important resource of non-ferrous metals. Electrolytic refining is one of the main methods to produce fine copper. In electrolytic process, plate conditions seriously affects the output and quality of copper. Timely and accurate prediction of the working condition of the plate is of great significance to the copper electrolytic refining process. Aiming at the problems of the traditional plate conditions detection algorithm with large lag, poor anti-interference ability and low accuracy, this paper proposes a plate condition prediction model based on LSTM with attention mechanism. The average gray value of the plate in the infrared image is used to characterize the plate working condition. In hidden layer, double layer LSTM network is used to improve the effect of model training. A unique attention mechanism is added to learn the correlation between input and output better and improve the accuracy of model prediction. Experimental results show that the accuracy of the proposed model for plate condition prediction reaches 95.11%. Compared with the commonly used LSTM algorithm, the plate condition prediction model proposed in this paper has stronger prediction ability.

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“…Nowadays, non-ferrous metals have become necessary for developing a country's economy, technology, and national defense industry. The electrolysis process is an essential step in refining many non-ferrous metals, which directly affects the productivity and quality of finished metals [1].…”

Section: Introductionmentioning

confidence: 99%

Fault detection of electrolyzer plate based on improved Mask R-CNN and infrared images

Zhu¹,

Peng²,

Dai³

et al. 2022

Meas. Sci. Technol.

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Non-ferrous metals are very important strategic resources, electrolysis is an essential step in refining non-ferrous metals. In the electrolysis process, plate short circuit is the most common fault, which seriously affects output and energy consumption. The rapid and accurate detection of faulty plates is of great significance to the metal refining process. Given the weak generalization ability and complex feature rule design of traditional object detection algorithms, and the poor detection effect of existing deep learning models in infrared images with many interference factors, an improved Mask R-CNN based fault detection algorithm is--proposed. Improve the generation strategy and the NMS algorithm of proposals to reduce the missed detection; propose the Globally Generalized IoU loss function to characterize better the position and scale relationship between the predicted box and the target box, which is beneficial to the bounding box regression. The experimental results show that the improved model has an accuracy rate of 10.4% higher than the original model, reaching 86.8%. Compared with the common one-stage and two-stage object detection models, the improved model has a stronger detection ability. This algorithm has some reference value for the accurate detection and location of electrolytic cell faults.

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Continuous electrolytic refining process of cathode copper with non-dissolving anode

Cited by 24 publications

References 24 publications

Gelatin Effects on the Kinetic of an Electrodeposition Process in Copper Foil Preparation

Gelatin Effects on the Kinetic of an Electrodeposition Process in Copper Foil Preparation

An attention-based long short-term memory prediction model for working conditions of copper electrolytic plates

Fault detection of electrolyzer plate based on improved Mask R-CNN and infrared images

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