Modeling And Optimizing For Leaching Process Of Hydrometallurgy

As the rapid development of metallurgical industry and great consumption increasing in high-quality resources, the problem of exhausting in rich resources of non-ferrous metal are becoming more and more severely in recent years. The social development and economic construction is also seriously hampered by that. At the same time, in China, there are fairly rich in low-taste of non-ferrous metals or complex mineral resources. Therefore, it is a great significance in the sustainable development of our country that how to make use of these low-taste of non-ferrous mineral resources furthermore effectively.Hydrometallurgy is widely used in Metallurgical Industry with the advantage in dealing with these complexity and low quality minerals. The hydrometallurgical leaching process is the basis of following processes which is the first process in hydrometallurgy. At present, as the low level of automation, Chinese hydrometallurgical leaching process still remains in the open-loop controlling technology conditions by manual operation. In order to achieve automatic controlling, the solution of online-measuring for leaching rate is quite imperative. In order to solve this problem, the thesis establishes the leaching rate of prediction model about the leaching process based on hybrid modeling. Considering the situation that leaching process is rough, and there is also considerable room for improvement, the thesis uses the propagation of particle swarm optimization(PPSO) algorithm to optimize the leaching process. The optimization results show it can effectively improve the productivity, conserve resources and improve the production level of the leaching process. The main contents of this thesis include:1. Based on an analysis in leaching process, the thesis builds the mechanism model of leaching process according to material balance and energy balance equation.2. Due to the complexity of the leaching process, the mechanism model is based on several assumptions and simplifications. Therefore, its prediction accuracy is low. According to the mechanism model, this thesis builds the hybrid model of leaching process by the hybrid modeling theory. It consists of two parts:the simplification of the mechanism model and data-driven model. The simplified mechanism model describes the basic dynamic characteristics of the leaching process. The data-driven model compensates the model error by support vector machine (SVM) method. The application of this model to a production line of hydrometallurgical leaching process achieves satisfactory results.3. According to the research on the leaching process, the thesis establishes the optimization model for operation variables of leaching process with the aim of economic benefits. And then it uses the propagation of particle swarm optimization method to optimize the process. The validity of the method is shown by the comparison of the optimization results with the production records.4. Finally, this thesis introduces in details how to design and build the prediction and optimization platform system for the hydrometallurgical leaching process. And it also introduces every part's structure and function of the platform system.