An Effective Harmonic Suppression Method Based on Linear Active Disturbance Rejection Control for Copper Electrowinning Rectifier

Because the harmonics in the production process of copper electrowinning have an important impact on the electrical energy consumption, it is necessary to suppress the harmonics effectively. In this paper, a copper electrowinning rectifier with double inverse star circuit is selected as a study object in which a large number of harmonics mainly including the 5th, 7th, 11th, and 13th harmonics are generated and injected back into the power grid. The total harmonic distortion rate of the power grid is up to 29.19% before filtering. Therefore, a method combining the induction filtering method and the active filtering method is proposed to carry out comprehensive filtering. Simulation results demonstrate that the total harmonic distortion rate of the system decreases to 4.20%, which indicates that the proposed method can track the corresponding changes of harmonics when the load changes in real time and filter them out. In order to ensure and improve the effect of active filter, a current harmonic tracking control method based on linear active disturbance rejection control is proposed. Simulation results show that the total harmonic distortion rate decreases to 3.34%, which is also lower than that of hysteresis control. Compared with the conventional single filtering method, the new filtering method combining induction filtering with active filtering based on linear active disturbance rejection control in the copper electrowinning rectifier has obvious advantages.

Behaviour of selenium and tellurium in copper electrowinning process

The revamping project to be implemented at Norilsk Nickel’s refining facilities includes commissioning of a nickel plant and increasing the output of copper obtained by electrowinning from liquors produced in matte leach processes. At the revamped facility, a major portion of selenium and a considerable share of tellurium will be transferred with calciner gases to a sulphuric acid facility. The microimpurities that remain after calcination get distributed between the leachates. Considering that semiproducts keep circulating between the nickel and copper plants, selenium and tellurium can accumulate in the product solutions and thus affect the quality of finished metals. This paper examines the behaviour of selenium and tellurium in copper electrowinning process and formulates certain requirements for copper electrolyte that will ensure production of high-purity copper cathodes for commercial application. It is shown that, at low concentrations of selenium (IV) and tellurium (IV) in the copper electrolyte, their discharge follows the laws of diffusion kinetics, i.e. occurs at maximum current, together with cathode reduction of copper, and the concentrations of selenium and tellurium in copper change in correlation with the concentration of chalcogenides in the solution. Simultaneously with cathode reduction on a lead anode, selenium (IV) and tellurium (IV) get oxidized to selenium (VI) and tellurium (VI) that are not precipitable with copper and get accumulated in circulating solutions. It is shown that the concentration of chalcogenides has a negative first order from the removal of copper per unit volume of electrolyte. That’s why the contamination of cathode copper with selenium and tellurium rapidly decreases with an increase in copper removal. The paper provides data that can be used to estimate the allowable concentration of toxic microimpurities in the feed electrolyte that would not affect the desired copper purity level.

A BP Neural Network Based on GA for Optimizing Energy Consumption of Copper Electrowinning

In this paper, achieving minimum energy consumption in the copper electrowinning process is taken as the research objective. In the traditional production process, sulfate ion concentration, copper ion concentration, and current density are carried out according to the empirical value, which cannot ensure the energy consumption reaching the optimal level. Therefore, this paper proposes a BP neural network model to optimize energy consumption according to the relationship between current density, sulfate ion concentration, copper ion concentration, electrolytic tank voltage, and current efficiency, and the established BP neural network model is trained by using real data from the enterprise. The simulation results show that there is a definite error between the predicted electrolytic tank voltage and current efficiency and corresponding to predict electrolytic tank voltage and current efficiency measured at the production site. The BP neural network improved by GA is proposed to further improve the prediction accuracy of the BP neural network. Simulation results indicate that the prediction error of electrolytic tank voltage and current efficiency is greatly reduced that meets the accuracy requirements, and then the minimum energy consumption can be calculated. On the premise of guaranteeing the quality of copper electrowinning, the current density, sulfate ion concentration, and copper ion concentration corresponding to the minimum energy consumption accurately predicted by this method can be respectively adjusted in real time, which realizes the optimization of energy consumption in the process of copper electrowinning under the background of low carbon and environmental protection.