Modeling And Optimization For Copper Extraction Process Of Hydrometallurgy

Hydrometallurgy is a process that the metallic mineral in acidulous liquid or alkaline solution is chemically treated, extracted, refined, and gains the metal and chemical compound. The advantage of hydrometallurgy is the effective utilization of materials, environmental protection, prevention of environment pollution, and the continuous and automation of productive process can be easily achieving, thus the productive capacity is expanded, the productivity of labor and the recovery rate of useful components are increased, and the product quality is greatly improved. The solvent extraction, as an important step of hydrometallurgical, achieves the separation of components by the liquid extraction agent.Component content on-line continuous measurement in hydrometallurgy extraction process is impossible, and it is difficult to model and optimally control the component content of extraction process, because it has the features of strong coupling, nonlinearity and large delay. Until now, most of the controlling the component content extraction process are still manual operation, and therefore which can not guarantee the quality and output. Aim at these problems, the primary works in this paper is presented as follows:Firstly, the principle and process of the copper extraction process is briefly described. Through analyzing the mechanism of mixer-settler, material balance is used to establish component content dynamic model for the extraction process.Secondly, the parameter distribution ratio can not be directly measured on-line. Thus a modeling method combining independent component analysis with least squares support vector machine (ICA-LSSVM) is proposed, which is utilized to establish the model of unknown parameter distribution ratio. The proposed ICA-LSSVM model and first principle model compose the serial hybrid model, which implements the on-line estimating the component content of extraction process.Finally, based on the hybrid model, the economic benefit is chosen as optimization objective, the optimization model of the extraction process is built. The improved particle swarm optimization (PSO) is applied to optimize the extraction process, and the result shows that the proposed method not only effectively improves the productivity of labor efficiency, but also saves raw stuff and prompts the productivity benefit of copper extraction process.