PID Gain Tuning Method for Oil Refining Controller based on Neural Networks

In these years, plant control systems are being highly automated. But the control performances change with the passage of time, so it is necessary to tune them. This is why human experts tune the control system to improve the total plant performances. In this study, PID control system for the oil refining chemical plant process is treated. In the oil refining controller, there are thousands of the control loops in the plant to keep the product quality desired value and to secure the safety of the plant operation. According to the ambiguity of the interference between the control loops, it is difficult to estimate the plant dynamic model accurately. Neuro emulator is employed to model the plant characteristics. Combining neuro emulator and RNN model, auto tuning system of PID control gains has been constructed. Through numerical experiments using actual plant data, the effect of the proposed method was ascertained.