| 工业PTA氧化过程4-CBA浓度的模糊神经网络模型 |
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| 引用本文： | 刘瑞兰,苏宏业,牟盛静,贾涛,陈渭泉,褚健.工业PTA氧化过程4-CBA浓度的模糊神经网络模型[J].中国化学工程学报,2004,12(2):234-239. |
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| 作者姓名： | 刘瑞兰 苏宏业 牟盛静 贾涛 陈渭泉 褚健 |
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| 作者单位： | NationalLaboratoryofIndustrialControlTechnology,InstituteofAdvancedProcessControl,ZhejiangUniversity,Hangzhou310027,China |
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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.
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| 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 |
| 修稿时间： | |
Fuzzy Neural Network Model of 4-CBA Concentration for Industrial Purified Terephthalic Acid Oxidation Process |
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| LIU Ruilan,SU Hongye,MU Shengjing,JIA Tao,CHEN Weiquan,CHU Jian.Fuzzy Neural Network Model of 4-CBA Concentration for Industrial Purified Terephthalic Acid Oxidation Process[J].Chinese Journal of Chemical Engineering,2004,12(2):234-239. |
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| Authors: | LIU Ruilan SU Hongye MU Shengjing JIA Tao CHEN Weiquan CHU Jian |
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| Affiliation: | National Laboratory of Industrial Control Technology, Institute of Advanced Process Control, Zhejiang University, Hangzhou 310027, China |
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| 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. |
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| Keywords: | purified terephthalic acid 4-carboxybenzaldchydc fuzzy neural network soft sensor input variables selection fuzzy curve dead time detection |
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