工业PTA氧化过程4-CBA浓度的模糊神经网络模型

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.     

基于贝叶斯方法的4-CBA含量的软测量研究

利用贝叶斯方法和多项式基函数进行辅助变量及模型结构的选择问题，结合最小二乘法建立精对苯二甲酸(PTA)氧化过程的软测量模型，针对实际对象进行仿真研究，结果表明该模型简单、实用、可操作性强。