Optimization Of Zinc Dust Addition For Copper Removal Process Based On QPSO Algorithm

The copper removal process from zinc solution was important steps in direct leaching of zinc hydrometallurgy. It was by adding zinc to the solution to remove impurities copper ions, and the copper ions concentration was reduced to within the range of process requirement. Since the important parameter of the copper removal process couldn’t be tested on line, to obtain the export of copper ions concentration was by artificial tests in actual production process, and the operators used the experience to add zinc powder. It will not only caused a waste of zinc, but also resulted in the concentration of copper ions in the solution can’t reach the norm, and affected the subsequent production process seriously. Therefore, to study the optimization methods to reduce zinc consumption in copper removal process in depth, to ensure the result of copper removal had positive significance.Firstly, the mechanism of the copper removal process and the main factors affecting purification process were analyzed in detail. In this case, we used principal component analysis method to deal with the historical data, and established a copper ion concentration prediction model based on least squares support vector machine. Thereby the online predictive values of export copper concentration were obtained, laying a solid foundation for the realization of optimal control zinc dosage.In order to ensure the export of copper ions concentration of the copper removal process to meet production requirements, and as stable as possible, while reducing the amount of zinc added. This article established a multi-objective optimization model for the copper removal process, and put the addition of zinc and the export of copper ions concentration as the optimized target. To solve the multi-objective optimization problem and to obtain the optimal solution, so quantum particle swarm optimization (QPSO) algorithm was proposed, and then QPSO algorithm was improved, which used the chaos initialization and group alternative search to improve QPSO algorithm. Some test functions were applied, verified the improved QPSO algorithm’s converges were fast and the capability of searching was strong. Finally, the improved QPSO algorithm was applied to solve the multi-objective optimization model, and obtained the optimal amount of zinc added. The use of field data to verify the effectiveness of this method showed that the consumption of zinc was greatly decreased, and fluctuation of the outlet copper ion concentration was also reduced. The optimization model established in this paper could provide guidance for the actual production.