Study On Hybrid Modeling And Intelligent Control Of Volatile Kiln Of Zinc Oxide

Volatile kilns occupy an important position in many industrial fields,and their main function is to process and process kiln materials.Only when the temperature of the reaction zone is between 1100?and1300?can product quality and production continuity be guaranteed.Because the operation process of the volatile kiln is a cumbersome thermal process,and its multi-variable,strong coupling,large inertia and nonlinear characteristics,and the kiln body that has been in a rotating state,some key parameters are not easy to obtain in real time Models that accurately characterize the production process of volatile kilns have become a major problem,which hinders the realization of volatile kiln production automation.In this thesis,based on the zinc oxide volatilization kiln,through the study of the structure,production process,internal physical and chemical reaction process and heat and mass transfer mechanism of the zinc oxide volatilization kiln,a mechanism model of zinc oxide volatilization kiln based on conservation of mass and energy is established.Aiming at the problem that the chemical reaction rate in this mechanism model is difficult to measure accurately and effectively,through the study of the thermal conditions and thermodynamics of each chemical reaction in the kiln,a chemical reaction rate model based on the Arrhenius equation was established,and a finite difference was used The method performs discrete processing and numerical solution.The simulation results show that the model has good stability and can characterize the temperature change in the kiln.Aiming at the problem that the reaction rate in the zinc oxide volatilization kiln is difficult to obtain accurately,the support vector regression method is used to fit the experimental data to obtain the reaction rate prediction model,and the design and simulation of the hybrid model are realized by calling the S function.In contrast,the hybrid model can more accurately characterize the production of the kiln than the mechanism model.Aiming at the problem that it is difficult to establish accurate mathematical model and realize precise tracking control for zinc oxidevolatilization kiln,an intelligent predictive control method for zinc oxide volatilization kiln based on support vector machine is proposed.This method utilizes the extreme learning machine to have the advantages of fast learning speed,high fitting accuracy,strong generalization ability and global optimal solution,to identify the input and output data of the production process,and to establish a prediction model for the production process of the zinc oxide volatilization kiln.The output of the kiln system is predicted.On this basis,Simulink is used to build a volatile kiln intelligent predictive control system module.The ELM-based predictive control subroutine and volatile kiln mixed model subroutine are called,and the intelligent predictive control system is simulated using the sim function.Simulation results verify that the intelligent predictive control method has good stability and robustness.