Heating Furnace Temperature Predictive Control Based On Siemens PCS7

Step heating furnace is one of the main equipments in hot rolling production process of ironand steel enterprise, its main task is to provide the qualified billets in heating quality for the rollingprocess, which meet the requirements of rolling technology. Step heating furnace as the keyequipment for rolling process, and its control level directly affect the billet heating quality, yield, andthe cost of production. Step heating furnace is a complex industrial controlled object with manyfactors, such as large inertia, pure time-delay, nonlinear, external disturbances characteristics. Theheating furnace adopts the traditional PID control, Wincc configuration software monitoring all thetime, although this control method has been using in control, but for the complex characteristics ofthe heating furnace traditional PID control is difficult to achieve good control effect, furnacetemperature fluctuations is very big, temperature control can’t reach a plateau. So in order to aim atbig inertia and pure lagging characteristics of heating furnace, this paper used model predictivecontrol module based on model predictive control algorithm as furnace temperature predictivecontroller to control the step heating furnace.Firstly, learned the model predictive control principle according for the large inertia and thepure time-delay characteristics existed in controlling step heating furnace, apply Matlab systemidentification toolbox to identify the model parameters of heating furnace, and use the Matlabsoftware simulation to research model predictive control theory.Secondly, Used the traditional PID control and MPC predictive control to the heating furnace,compared the anti-jamming and robustness of the MPC and PID through simulation，and provedthat these performance of MPC are stronger than PID control.Thirdly, learned the SIMATIC PCS7V7.1software, learned PCS7advanced process controlfunction modules and method in the library, and have the deep level to digest absorb. Also learnedhow to create project procedures in the PCS7system.And the result shows that using model predictive control can make the furnace temperaturefollow the given value and reach to the stable state quickly. This lay a solid foundation forresearching heating furnace temperature optimization work in the future.