The application of neural network PID controller to control the light gasoline etherification

3rd International Conference on Advances in Energy, Environment and Chemical Engineering

能源学;生态环境科学;化学工业

Cheng, Huanxin^1 ; Zhang, Yimin^1 ; Kong, Lingling^2 ; Meng, Xiangyong^1

College of Automation and Electrical Engineering, Qingdao University of Science and Technology, Qingdao

266042, China^1

Research Institute of Physical and Chemical Engineering of Nuclear Industry, Tianjin

300180, China^2

关键词: BP neural networks;    Environmental-friendly;    Matlab simulations;    Neural network PID controller;    Oil concentration;    On-line parameter;    PID controllers;    Self-learning ability;   

Others  :  https://iopscience.iop.org/article/10.1088/1755-1315/69/1/012045/pdf
DOI  :  10.1088/1755-1315/69/1/012045

学科分类：环境科学（综合）

来源: IOP

【 摘 要 】

Light gasoline etherification technology can effectively improve the quality of gasoline, which is environmental- friendly and economical. By combining BP neural network and PID control and using BP neural network self-learning ability for online parameter tuning, this method optimizes the parameters of PID controller and applies this to the Fcc gas flow control to achieve the control of the final product- heavy oil concentration. Finally, through MATLAB simulation, it is found that the PID control based on BP neural network has better controlling effect than traditional PID control.