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Mathematical Problems in Engineering
Volume 2014, Article ID 681259, 13 pages
Research Article

D-FNN Based Modeling and BP Neural Network Decoupling Control of PVC Stripping Process

1College of Information and Engineering, Shenyang University of Chemical Technology, Shenyang 110142, China
2Multi-Functional Design and Research Academy, Zhengzhou University, Zhengzhou 450001, China
3School of Electronic and Information Engineering, University of Science and Technology Liaoning, Anshan 114044, China

Received 23 December 2013; Accepted 18 March 2014; Published 15 April 2014

Academic Editor: Qintao Gan

Copyright © 2014 Shu-zhi Gao et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


PVC stripping process is a kind of complicated industrial process with characteristics of highly nonlinear and time varying. Aiming at the problem of establishing the accurate mathematics model due to the multivariable coupling and big time delay, the dynamic fuzzy neural network (D-FNN) is adopted to establish the PVC stripping process model based on the actual process operation datum. Then, the PVC stripping process is decoupled by the distributed neural network decoupling module to obtain two single-input-single-output (SISO) subsystems (slurry flow to top tower temperature and steam flow to bottom tower temperature). Finally, the PID controller based on BP neural networks is used to control the decoupled PVC stripper system. Simulation results show the effectiveness of the proposed integrated intelligent control method.