CIESC Journal

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工业PTA氧化过程4-CBA浓度的模糊神经网络模型

刘瑞兰; 苏宏业; 牟盛静; 贾涛; 陈渭泉; 褚健   

  1. National Laboratory of Industrial Control Technology, Institute of Advanced Process
    Control, Zhejiang University, Hangzhou 310027, China
  • 收稿日期:1900-01-01 修回日期:1900-01-01 出版日期:2004-04-28 发布日期:2004-04-28
  • 通讯作者: 刘瑞兰

Fuzzy Neural Network Model of 4-CBA Concentration for Industrial Purified Terephthalic Acid
Oxidation Process

LIU Ruilan; SU Hongye; MU Shengjing; JIA Tao; CHEN Weiquan; CHU Jian   

  1. National Laboratory of Industrial Control Technology, Institute of Advanced Process
    Control, Zhejiang University, Hangzhou 310027, China
  • Received:1900-01-01 Revised:1900-01-01 Online:2004-04-28 Published:2004-04-28
  • Contact: LIU Ruilan

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摘要： A fuzzy neural network (FNN) model is developed to predict the 4-CBA concentration of the
oxidation unit in purified terephthalic acid process. Several technologies are used to deal
with the process data before modeling.First,a set of preliminary input variables is
selected according to prior knowledge and experience. Secondly,a method based on the
maximum correlation coefficient is proposed to detect the dead time between the process
variables and response variables. Finally, the fuzzy curve method is used to reduce the
unimportant input variables.The simulation results based on industrial data show that the
relative error range of the FNN model is narrower than that of the American Oil Company
(AMOCO) model. Furthermore, the FNN model can predict the trend of the 4-CBA concentration
more accurately.

关键词: 模糊神经网络系统;对苯二酸;氧化过程;传感器;变量;模糊曲线

Abstract: A fuzzy neural network (FNN) model is developed to predict the 4-CBA concentration of the
oxidation unit in purified terephthalic acid process. Several technologies are used to deal
with the process data before modeling.First,a set of preliminary input variables is
selected according to prior knowledge and experience. Secondly,a method based on the
maximum correlation coefficient is proposed to detect the dead time between the process
variables and response variables. Finally, the fuzzy curve method is used to reduce the
unimportant input variables.The simulation results based on industrial data show that the
relative error range of the FNN model is narrower than that of the American Oil Company
(AMOCO) model. Furthermore, the FNN model can predict the trend of the 4-CBA concentration
more accurately.

Key words: purified terephthalic acid, 4-carboxybenzaldchydc, fuzzy neural network, soft sensor, input variables selection, fuzzy curve

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