Modeling And Optimization For The Overall Process Of Hydrometallurgy

As is well known, mineral resources are not only the basis but also the main constraint factors of economic and social development. With the development of industrialization process and continuous consumption of mineral resources, high grade ores of great economic value are rapidly decreasing, and the metal resource exhaustion situation is becoming severe; on the other hand, our country contains a lot of low grade non-ferrous metal resources, therefore, it is important to effectively and economically use the low grade non-ferrous metal mineral resources for the sustainable development of our country.Hydrometallurgy can deal with miscellaneous and low grade mineral, and it has less environmental pollution, so it is widely used. Although our country is not behind other countries in hydrometallurgical processes, there is a big gap between the automatic control technology in our country and the abroad, which makes it difficult for us to use mineral resources effectively with low energy consumption. This thesis aims to solve such a problem on the background of one gold smelting factory. The prediction model of the overall process of hydrometallurgy is established and then optimized by the two-order oscillating PSO algorithm. The optimization results show that, it can effectively improve the production efficiency, reduce the production cost and obtain the biggest economic benefits. The main content of this thesis is as following:(1) After analyzing the typical processes of hydrometallurgy, such as leaching, press filtration washing and replacement, their mechanism models are built according to the material conservation and reaction kinetic equations. Then the mechanism model of the overall process of hydrometallurgy is built based on the three models.(2) Based on the research of overall process, the overall process optimization is studied with conventional optimization method firstly. The optimization model with the goal of economic benefit which is built based on the mechanism model of the overall process is solved directly with two-order oscillating PSO algorithm.(3) Because the conventional optimization problem is complex, time-consuming and difficult to find the optimal solution, we put forward a hierarchic optimization strategy, and the overall process is divided into two layers:the first layer takes the economic benefit as the goal, and the process indices (leaching rate, washing rate and replacement rate) as the decision variables; the second layer determines the minimum consumption on the premise that the indices which are determined by the first layer should be reached, and takes the operation variables (the added amount of sodium cyaniding and zinc) as decision variables. Hierarchic optimization is shown having more advantages compared with the conventional optimization.(4) Finally, the prediction and optimization platform system is designed and realized based on the mixed programming of C#, Matlab and SQL Server database for the overall process of hydrometallurgical in details, which is used for guiding the field operation.