Research On Temperature Control Method Of Continuous Annealing Furnace Heating Section For Stainless Steel

Modeling control and optimization of complex industrial process is one of the basic problems of control theory and control engineering.As the major equipments of continuous annealing pickling line in stainless steel industry, continuous annealing furnace could directly affect the quality and quantity of stainless steel as well as the products. The research on continuous annealing furnace temperature control system makes sense in increasing the efficiency of productivity, improving product quality and economizing energy. Considering the continuous annealing furnace temperature control as a complex and difficult controlled industry process, the classical PID control strategy could hardly reach desired effect.Then,it is no doubt that studying a control strategy with better performance has a practical significance.Based on the real project background of the continuous annealing furnace of stainless steel continuous annealing pickling line, the thesis mainly focuses on the stainless steel process and temperature control system of continuous annealing furnace.The thesis analyzes the gas flow control loop, the air flow control loop, the furnace temperature control loop and the strip temperature control loop in detail. At the same time, it gives the framework of cascade control system with the furnace temperature correction and takes profound research on double cross limit control strategy used in the system.What’s more, it introduces the double cross limit control strategy based on the residual oxygen compensation.In view of continuous annealing furnace temperature control system being a complicated nonlinear system with the characteristic of large inertia, pure time-delay, strong-coupling, time-varying and so on, thus it is difficult to build the exact mathematic model via the mechanism modeling method.After analyzing the basic automation programs and PID control strategy in stainless steel continuous annealing furnace, we can get the principle factors which influence the furnace temperature.The paper identifies dynamic model of continuous annealing furnace temperature system using RBF neural network based on a large number of field data.Then the system is simulated and the result shows that the identification effect of annealing furnace temperature model using RBF neural network is quite good. Based on the mathematical model of continuous annealing furnace heating section in stainless steel, the paper designs a continuous annealing furnace temperature control system based on the PSO-ANFIS controller in which the furnace tempreture model is viewed as the main control object. At last, the system adopting the control strategy which combines fuzzy control,neural network and PSO is simulated with MATLAB tools. The results show that compared with the PID and ANFIS controller, the PSO-ANFIS controller in the furnace temperature control system has a better performance in the following aspects:less overshoot,less adjusting time and better robustness.