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Journal of Sensors
Volume 2017, Article ID 7872030, 13 pages
Research Article

Soft-Sensor Modeling of PVC Polymerizing Process Based on F-GMDH-Type Neural Network Algorithm

1School of Electronic and Information Engineering, University of Science and Technology Liaoning, Anshan City, Liaoning Province, China
2National Financial Security and System Equipment Engineering Research Center, University of Science and Technology Liaoning, Anshan, Liaoning Province, China
3College of Information and Engineering, Shenyang University of Chemical Technology, Shenyang, Liaoning Province, China

Correspondence should be addressed to Jie-sheng Wang; moc.621@gnehseij_gnaw

Received 14 December 2016; Accepted 30 January 2017; Published 19 February 2017

Academic Editor: Fanli Meng

Copyright © 2017 Wei-zhen Sun 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.


For predicting the conversion velocity of the vinyl chloride monomer (VCM) in the polymerization process of polyvinylchloride (PVC), an improved Group Method of Data Handling- (GMDH-) type neural network soft-sensor model is proposed. After analyzing the technique of PVC manufacturing process, the auxiliary variables for setting up the soft-sensor model are selected and the experimental data are normalized. Because the internal standard of the original GMDH-type neural cannot solve the problem of multiple-collinearity problem and the useful variables tend to be prematurely eliminated in the modeling process, a hybrid method combining the regression analysis method and the least squares method is proposed to solve the multiple-collinearity problem. On the same time, by adopting some optimization experiences in genetic algorithm (GA), the generational crossover combination variables method is proposed to solve the shortcoming of useful variable being eliminated prematurely. The simulation results show that the proposed soft-sensor model can significantly improve the prediction accuracy of economic and technical indicators in the PVC polymerization process and can meet the real time control requirements of polymerization reactor production process.