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

• SYSTEM ENGINEERING • Previous Articles Next Articles

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

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

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

National Laboratory of Industrial Control Technology, Institute of Advanced Process
Control, Zhejiang University, Hangzhou 310027, China

通讯作者: 刘瑞兰

Abstract

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

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

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]. , DOI: .

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]. , DOI: .

[1]	Kaixun He, Kai Wang, Yayun Yan. Active training sample selection and updating strategy for near-infrared model with an industrial application [J]. Chinese Journal of Chemical Engineering, 2019, 27(11): 2749-2758.
[2]	Honggui Han, Shuguang Zhu, Junfei Qiao, Min Guo. Data-driven intelligent monitoring system for key variables in wastewater treatment process [J]. Chin.J.Chem.Eng., 2018, 26(10): 2093-2101.
[3]	Pengfei Cao, Xionglin Luo, Xiaohong Song. Modeling and identification for soft sensor systems based on the separation of multi-dynamic and static characteristics [J]. Chin.J.Chem.Eng., 2018, 26(1): 137-143.
[4]	Pengfei Cao, Xionglin Luo, Xiaohong Song. Feasibility analysis and online adjustment of constraints in model predictive control integrated with soft sensor [J]. , 2017, 25(9): 1230-1237.
[5]	Huan Min, Xionglin Luo. Calibration of soft sensor by using Just-in-time modeling and AdaBoost learning method [J]. , 2016, 24(8): 1038-1046.
[6]	Wentao Cang, Huizhong Yang. Estimation of catalytic activity using an unscented Kalman filtering in condensation reaction [J]. Chin.J.Chem.Eng., 2015, 23(12): 1965-1969.
[7]	Weiming Shao, Xuemin Tian, PingWang. Supervised local and non-local structure preserving projections with application to just-in-time learning for adaptive soft sensor [J]. Chin.J.Chem.Eng., 2015, 23(12): 1925-1934.
[8]	Yanlin He, Yuan Xu, Zhiqiang Geng, Qunxiong Zhu . Soft sensor of chemical processes with large numbers of input parameters using auto-associative hierarchical neural network [J]. Chin.J.Chem.Eng., 2015, 23(1): 138-145.
[9]	Ouguan Xu, Yongfeng Fu, Hongye Su, Lijuan Li . A Selective Moving Window Partial Least Squares Method and Its Application in Process Modeling [J]. , 2014, 22(7): 799-804.
[10]	CAO Pengfei, LUO Xionglin. Soft Sensor Model Derived from Wiener Model Structure：Modeling and Identification [J]. Chin.J.Chem.Eng., 2014, 22(5): 538-548.
[11]	LÜ Ye, YANG Huizhong. A Multi-model Approach for Soft Sensor Development Based on Feature Extraction Using Weighted Kernel Fisher Criterion [J]. Chin.J.Chem.Eng., 2014, 22(2): 146-152.
[12]	Junfei Qiao, Wei Li, Honggui Han. Soft Computing of Biochemical Oxygen Demand Using an Improved T-S Fuzzy Neural Network [J]. , 2014, 22(11/12): 1254-1259.
[13]	XU Feng, WANG Yeye, LUO Xionglin. Soft Sensor for Inputs and Parameters Using Nonlinear Singular State Observer in Chemical Processes [J]. Chin.J.Chem.Eng., 2013, 21(9): 1038-1047.
[14]	YAN Xingdi, YANG Wen, MA Hehe, SHI Hongbo. Soft Sensor for Ammonia Concentration at the Ammonia Converter Outlet Based on an Improved Group Search Optimization and BP Neural Network^* [J]. Chin.J.Chem.Eng., 2012, 20(6): 1184-1190.
[15]	LI Chengfei. Dynamic Simulation and Analysis of Industrial Purified Terephthalic Acid Solvent Dehydration Process [J]. , 2011, 19(1): 89-96.