Modeling Of Iron Precipitation By Goethite Process In Zinc Leaching And Its Application

In the directly leaching process under atmospheric pressure with rich oxygen, zinc sulfide concentrate often contains significant iron irons (5%～15%), which decreases efficiency of impurity removing in the next stage (purification), and could even lead a unstable production station. In iron precipitation process, iron ions are deposited as goethite in five continuous stirred tank reactors (CSTR) in series using zinc calcine and oxygen. In this process, multi-phase chemical reactions, including oxidation, hydrolysis and neutralization, display at the same time with serious coupling and nonlinearity. For the complexity of multi-phase reactions in the process, it is not easy to control iron precipitation process, and the oxidation rate of ferrous ion and the pH value fluctuate seriously, leading low stability of production process, high energy consuming and low valuable metal recovery. Hence, the modeling of iron precipitation process and prediction of iron concentration are significant to the optimization control of the process, and benefit to energy saving, high production of the company.To solve the above problems, the optimization calculation in iron precipitation by goethite process, series weighted coupling CSTR model, hybrid prediction model for iron ion concentration and integrated prediction model for iron content have been built, in the basis of pre-processing and relevance analysis of industrial data. The major work and the innovative achievements in this paper are reflected in the following areas:(1) Data preprocessing and relevance analysis of iron precipitation by goethite processTo solve the problem of low modeling accuracy caused by gross error in detected value of pH meter, the gross error correction method of outlet pH value based on a multiple regression and first order kinetic was presented. And aimed at the undetection of iron concentration of the central reactor in CSTR, the interpolation method for iron concentration combined first order kinetics with average interpolation algorithm was proposed. On the basis of mechanisms study of iron precipitation as goethite, the relevance among the operating variables, state variables and production targets were analyzed. The experimental results showed that the proposed data pre-processing and analysis method promoted the efficiency of industrial data in modeling and increased the accuracy of the models(2) Effective dissolved oxygen concentration model under different working conditionsIn the view of the modeling problems caused by lack of effective dissolved oxygen concentration, the iron precipitation reactors classification method according to oxygen dissolving differences was proposed. In the method, iron precipitation reactors were classified into three groups after analysis of iron ion concentration distribution and changes in each reactor. Then according to the influences of oxygen dissolving in the solution, the effective dissolved oxygen concentration model under different working conditions on the basis of the two-film theory, penetration theory and surface renewal theory, has been analyzed and built. The proposed model could describe the dissolved oxygen concentrations objectively in each iron precipitation reactors.(3) Series Weighted coupled CSTR modeling in iron precipitation by goethite processFor the complexity of reaction mechanism in iron precipitation as goethite, kinetic models of oxidation, hydrolysis and neutralization were established, respectively, according to the principles of chemical dynamics. Combined with the kinetic models, the regular series of continuous stirred tank reactor model (n-CSTR) was built based on mass conservation. On the basis of those work, the weighted coupling CSTR model was proposed to compensate calculation errors of regular mechanism model caused by the coupling effect among the three reactions.(4) An iron precipitation mechanism model parameters identifica-tion method is put forwardAccording to the strong nonlinear of mechanism model and the coupling between the identified parameters, this paper proposes a model parameter identification algorithm based on the piecewise evolution PSO algorithm. Aiming at the weight coefficient of weighted coupling mechanism model, a method to determine the weight coefficient has been proposed in this paper, and the change tendency of the weight coefficient along with the reactor has been analyzed. The data simulation experiment of typical working conditions show that the parameter identification method can effectively improve the accuracy of the mechanism model.(5) Integrated prediction model for iron ion concentration in production targetFor the mechanism model prediction accuracy and real-time difference, as well as the time lag of low iron and ferric ion detection, an intelligent forecasting model based on improved support vector machine and process neural network ensemble has been built to efficiently solve the problem that the sample point was too little and input data dynamically changed during the iron precipitation by goethite.According to the prediction accuracy of the forecast model in the production process time-varying situation, this paper has proposed a Euclidean distance sample selection method based on the relevance analysis and weighing, in addition, a correction method for prediction model based on the capability of prediction model and local regression.In addition, aiming at raising the precision of prediction model under different work conditions, a integrated model including mechanism and intelligent model is presented based on the mulstitgae process multivariate quality process capability index size. The scene data simulation results verified the validity of the integrated prediction model.(6) Developing the optimization control system of iron precipita-tion by goethite processThrough the OPC client this system can collect scene data and realize the function of the key process parameters coordination optimization setting in the iron precipitation by goethite process, ion concentration online estimate after precipitation process and operating variable optimization control. In addition, it also can achieving process monitoring, data query and analysis function in the iron precipitation process. The actual industrial operation results confirmed that this system efficiently improved the enterprise’s production efficiency and the resources utilization.