Study On Intrrval Control Method Of Rare Earth Extraction Process Based On Optimization Setting

China is a big country in rare earth resources,with the largest production and storage of rare earth in the world.Rare earth elements are widely used in various fields because of their excellent physical and chemical properties.The extraction and separation process of rare earth is characterized by nonlinear,multi-variable and large lag.The extraction site of rare earth in China mainly relies on operators to manually control each flow value based on their own experience,which is prone to unstable quality of export products and waste of raw materials.Therefore,it is necessary to study effective automatic control methods of rare earth extraction process.On the premise of ensuring the quality of rare earth extraction products up to standard,this paper introduces the idea of optimization setting in order to reduce the economic cost.Firstly,the wavelet neural network model of rare earth extraction process was established,and the optimal value of component content was obtained by using particle swarm optimization algorithm.Finally,the generalized predictive controller of rare earth extraction process based on optimization setting was designed.In order to improve the robustness and freedom of rare earth extraction control system,a generalized predictive interval control(GPIC)method was proposed by combining the interval control strategy with generalized predictive control(GPIC).The research contents of this paper are as follows:1.Due to the complex internal mechanism of rare earth extraction process,it is difficult to establish an accurate mechanism model,so a data-driven modeling method based on wavelet neural network is proposed.Combined with rare earth extraction process parameters and field data,the WNN model of rare earth extraction process was established,which laid a foundation for the study of rare earth extraction process2.According to the field requirements of rare earth extraction process,particle swarm optimization algorithm was used to obtain the accurate optimal value of component content in order to reduce the economic cost.Then,a generalized predictive controller for rare earth extraction process was designed based on optimal setting,and the effectiveness of the proposed method was verified by experimental simulation.3.In order to improve the robustness and freedom of the system,the generalized predictive interval controller for rare earth extraction process was designed by combining the interval control strategy with the generalized predictive control,and the feasibility of the algorithm was verified by comparison with the conventional generalized predictive controller.To sum up,in order to reduce the economic cost of rare earth extraction process and improve the control performance of rare earth extraction process,on the basis of establishing the WNN model of rare earth extraction process,the optimal value of component content was obtained by using particle swarm optimization algorithm,and then a generalized predictive controller of rare earth extraction process based on optimization setting was designed.In order to improve the freedom and robustness of the system,a generalized predictive interval control method was proposed,and its effectiveness was verified by experimental simulation and comparison with conventional predictive controllers,which has certain guiding significance for the field operation of rare earth extraction.