Optimization For Zinc Dust Addition In Copper Removal Process Based On CBR

Abstract:The copper removal process of zinc sulphate solution is one of the most important step in Direct leaching of zinc smelting. It removes impurities copper ions by adding zinc to the solution. Since the key parameters of the copper removal process can not be detected online, there is lots of blindness the zinc dosage which controlled by human experience. It will not only cause a waste of zinc can not only make the zinc dust, but also result in the concentration of copper ions in the solution after copper removal process cannot reach the norm, and affect the subsequent zinc electrolytic process seriously. This article studies the optimization for zinc dust addition in copper removal process, and provides guidance for copper removal process, can make a great significance in guarantee the result of copper removal process and reducing zinc consumption.Firstly, the article detailed description of the mechanism of the copper removal process, and analysis of the main factors which impact the process. Considering the concentration of copper ions of the reactor inlet can not be detected online, wavelet denoising was applied, after the processing of the historical data of the copper ions concentration which collect from the reactor inlet, the valid data which used for modeling was gained; On this basis, an SVM prediction model of Entrance concentration of copper ions was built, and using the PSO algorithm to optimize the parameters of the model, the copper ion concentration online estimation of Inlet was obtained, so the optimal control of the removing copper process was laid the foundation.Considering the problem of the addition of zinc dust control in copper removal process, an optimization for zinc dust addition in copper removal process based on CBR has been proposed. First, the historical data of the process was selected and preprocessed reasonably, the case base of copper removal process has been built. A case classification search strategy has been proposed, namely, start with the search in case library for the cases which match the current process conditions and make the copper ion concentration meets the production requirements; then the cases would been search again for a case which makes zinc consumes the least, providing guidance for the actual production. So the target of guarantee in the effect of copper removal process and reduce zinc consumption has been achieved. Finally, an optimization control system has been developed, to achieve automated monitoring in copper removal process.