Targeted Coordination-Controlled Iron Precipitation in the Zinc Hydrometallurgy Process: A Green Strategy for Fe–Zn Separation

Effect of in-situ oxidation on the phase composition and magnetic properties of Fe3O4: Implications for zinc hydrometallurgy

Determination of trace ions of cobalt and copper by UV–vis spectrometry in purification process of zinc hydrometallurgy

ZnSO4∙7H2O was prepared using a filter cake enriched in calcium and magnesium that was generated during the process of zinc hydrometallurgy. The study was optimized to obtain process parameters. The results show that the optimal acid leaching parameters are a solid-to-liquid ratio of 1:3.5, a sulfuric acid concentration of 16%, an acid leaching time of 20 min, a final pH of 4.1–4.4, a cooling and settling time of 120 min, an oxidation time of 20 min, a stirring speed of 300 r/min, a H2O2 dosage of 25 mL/L, a crystallization temperature of 20 °C, and a crystallization time of 60 min. The ZnSO4∙7H2O content in the product is 98.6%, and the zinc recovery efficiency is 97.5%. This process is characterized by simple flow and low cost, while the circulation and accumulation problems with calcium and magnesium ions in the zinc hydrometallurgy process are also solved.

An integrated predictive model with an on-line updating strategy for iron precipitation in zinc hydrometallurgy

Iron precipitation is a key process in zinc hydrometallurgy. The process consists of a series of continuous reactors arranged in descending order, overflowing zinc leach solution from one reactor to the next. In this paper, according to the law of mass conservation and the reaction kinetics, a continuously stirred tank reactor model of a single reactor is first established. Then, a distributed model of cascade reactors is built with coupled control based on the single reactor model, considering the unreacted oxygen in leaching solution. Secondly, four reactors in the iron precipitation process are considered as four subsystems, the optimization control problem of the process is solved by a distributed model predictive control strategy. Moreover, the control information feedback between successive subsystems is used to solve the optimization problem of each subsystem, because of the existing control coupling in their optimization objective function of pre and post subsystems. Next, considering the intractability of the optimization problem for subsystems with various constraints, a distributed dual iterative algorithm is proposed to simplify the calculation. With the consideration of its cascade structure and control couplings, the proposed algorithm iteratively solves the primal problem and the dual problem of each subsystem. The application case shows that distributed model predictive control based on dual iteration algorithm can handle coupled control effectively and reduce the oxygen consumption.

The application of zinc calcine as a neutralizing agent for the goethite process in zinc hydrometallurgy

Fractional order fuzzy PID optimal control in copper removal process of zinc hydrometallurgy

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.

As part of the zinc hydrometallurgy plant, the iron removal process is a complex system with four cascaded reactors. Tighter process-index control is difficult to achieve due to the complicated, long, and time-varying removal process. The control performance is also affected by the quality of the ore source and external disturbances. Little research is documented in the literature to address these difficulties and manual control is widely used. An innovative hybrid control strategy is developed to control the iron removal process’ indices within narrow ranges with minimum cost of additive regents, including oxygen and zinc oxide. This strategy is composed of an optimal setting model, a model-based optimal controller, an integrated prediction model, a fuzzy-logic-based feedforward compensator, and a model feedback adjustor. The optimal setting model automatically optimizes the set-points of the process indices under different production conditions. To achieve the process requirements with minimal cost, the model-based optimal controller is designed. The integrated prediction model is established to provide a more accurate on-line prediction of the process indices by integrating the mechanism prediction model and an error compensation model based on the least-square support vector machine. Based on the predicted process indices, the compensator is developed for the optimal controller. The adjustor provides a parameter adjustment mechanism. Four-week-long industrial experiments in the largest zinc hydrometallurgy plant in China show that the control strategy can not only improve the process-indexes control performance, but also save 6.55% oxygen and 4.61% zinc oxide consumptions, which translates to 222 858 m3 oxygen and 1236 t zinc oxide per year (a saving of about $570 000). The hybrid control strategy can be extended to cover other similar processes in the zinc hydrometallurgy and other industries.

Comparison between purification processes for zinc leach solutions with arsenic and antimony trioxides

In the zinc hydrometallurgy process, iron is usually present in leach solutions and its elimination is a major operational problem. The first step of iron removal is iron oxidation by MnO2, but this process have some disadvantages such as the cost is relatively high and decreased the current efficiency in the electro-winning stage. In this paper, a new iron oxidation process in zinc leaching solution by ozone oxidation was conducted and evaluated. The results showed that Fe2+ concentration decreased from 7.45 g/L to 0.51 g/L in 80min at pH 3. Oxidation of Fe2+ by O3 is controlled by chemical reaction. O3 dissolve rate, pH and initial iron concentration are key parameters for this process. Our finding indicated the potential application of O3 oxidation in iron removal process of zinc hydrometallurgy.

This paper aims at the problem of the influence of the gas evolved from the electrode on the performance of the electrolytic cell during the electrowinning process of zinc hydrometallurgy. Based on the COMSOL Multiphysics software, a visually intuitive coupling model of the distribution of the tertiary current and the flow of bubbles in the zinc electrolysis cell is established. The effects of the gas released from the electrode on the concentration distribution of zinc ions, current efficiency and power consumption in the electrolytic cell were studied. These works provide an important basis for further improvement of the structure and operation of zinc electrolytic cells and subsequent optimization of electrolytic cell performance.

Iron precipitation occupies a vital position in the metallurgy industry, especially for treating iron-bearing sphalerite in hydrometallurgical processes. This paper emphasizes techniques for removing iron from high-iron sphalerite via hematite precipitation, and describes the results of research performed to examine ferrous oxydrolysis and precipitation. The behavior and mechanism of conversion between the iron phase and hematite residue at elevated temperature under pressure is ascertained by investigating the dissolving characteristic and thermodynamics stable area of hematite at sulfate system. The results show hematite precipitation went through the process of crystallization, dissolve, oxidation and precipitation of ferrous sulfate, and the overall iron precipitation was determined to be controlled by the rate of ferric sulphate hydrolysis rather than the oxidation of ferrous sulphate or the re-dissolution of ferrous sulphate crystals, or the transformation of basic ferric sulphate.

An integrated hydrometallurgical process was used for the zinc leaching and purification from a zinc ore containing 9.75 wt% zinc. The zinc minerals in the ore were hemimorphite, willemite, and calcophanite. Main gangue minerals were quartz, goethite, hematite, and calcite. Central composite design (CCD) method was used to design leaching experiments and the optimum conditions were found as follows: 30% of solid fraction, 22.05% sulphuric acid concentration, and the leaching temperature of 45 °C. The PLS containing 35.07 g/L zinc, 3.16 g/L iron, and 4.58 g/L manganese impurities was produced. A special purification process including Fe precipitation and Zn solvent extraction was implemented. The results showed that after precipitation of iron, Zn extraction of 88.5% was obtained with the 2 stages extraction system composed of 30 vol% D2EHPA as extractant. The overall Zn recovery from the ore was 71.44%. Therefore, an appropriate solution containing 16.6 g/L Zn, 0.05 g/L Fe, and 0.11 g/L Mn was prepared for the electro-winning unit without using the roasting and calcination steps (conventional method), which result in environmental pollution.

During the process of zinc hydrometallurgy, the control of pH value during leaching not only decides the quantity and rate of leaching but also restricts the quantity and quality in follow-up working procedure. Moreover, to keep high nonlinearity during the neutralization of pH value has always been very difficult in controlling fields. Aiming at this problem, this paper raised the idea of using nonlinear PID control based on Neural-network adaptive-PID in controlling pH of zinc hydrometallurgy so as to raise the controlling precision during leaching. It also made experimental compare with traditional PID controlling.