Intelligent control of hydrometallurgy leaching process based on RBF neural network

Hydrometallurgy is an important part of nonferrous metal production. The leaching is an important process of chemical dissolution reaction between leaching agent and specific components in solid materials. The leaching rate is a key output control variable in leaching process. The leaching rate is a nonlinear function of sodium cyanide and dissolved oxygen. There are some assumptions and simplifications in the establishment of mechanism model of leaching process, which mainly includes gold conservation equations, cyanide ion conservation equations and kinetic reaction velocity equations. The mismatching of mechanism model parameters may cause prediction error. The existing control algorithms based on model are difficult to achieve accurate control and have great application limitations. The intelligent control algorithm based on radial basis function (RBF) neural network will be ultilized to control the nonlinear leaching process due to the less limitation. The consistent stability and merit of the presented adaptive control algorithm are also analyzed and verified via practical test. The proposed intelligent control algorithm has good promotion performance, and can be easily extended to other industrial systems, hoping to obtain satisfactory control effect.