摘要
针对流浆箱的总压和浆位之间存在严重耦合的问题,提出一种基于BP网络的静态解耦方法,用进浆量去控制总压,用进气量去控制浆位,成功地实现了总压、浆位之间的解耦。实际运行结果表明该系统具有良好的控制效果,具有一定的推广价值。
Aimed at the strong coupling between the pressure of head box and level, proposed a static decoupling method based on BP neural network. Applied pulp flux and air flux to control pressure and level respectively,which realized decoupling control between the two variables successfully. The results of working process shows that the system meets good control effect.
出处
《化工自动化及仪表》
CAS
2007年第6期24-27,共4页
Control and Instruments in Chemical Industry
关键词
解耦控制
神经网络
流浆箱
decoupling control
neural network
head box
