Soft-Sensing Modeling Method of Vinyl Acetate Polymerization Rate Based on BP Neural Network

Author

Huang Jiangping ; Tao Huihui ; Zhu Zhigao

Author_Institution

East China Jiaotong Univ., Nanchang, China

Volume

3

fYear

2010

fDate

13-14 March 2010

Firstpage

410

Lastpage

413

Abstract

Providing a soft-sensing modeling method of vinyl acetate (VAC) polymerization rate based on BP neural network. Solving the current problem that the VAC polymerization rate in the polyvinyl alcohol (PVA) producing process is hard to real-time measuring. Using the data samples collected from the scene to train the network. In the network learning process, using the Levenberg-Marquardt optimization algorithm. Finally, testing the network which has completed training. Test result shows that soft-sensing model of VAC polymerization rate based on BP neural network is accurate and effective.

Keywords

backpropagation; neural nets; optimisation; polymerisation; real-time systems; resins; BP neural network; Levenberg-Marquardt optimization algorithm; PVA; VAC; network learning process; polyvinyl alcohol; real-time measurement; soft sensing modeling method; vinyl acetate polymerization rate; Layout; Methanol; Multi-layer neural network; Neural networks; Neurons; Polymers; Production; Software measurement; Temperature; Testing; BP network; Modeling; Soft-sensing; VAC polymerization rate;

fLanguage

English

Publisher

ieee

Conference_Titel

Measuring Technology and Mechatronics Automation (ICMTMA), 2010 International Conference on

Conference_Location

Changsha City

Print_ISBN

978-1-4244-5001-5

Electronic_ISBN

978-1-4244-5739-7

Type

conf

DOI

10.1109/ICMTMA.2010.326

Filename

5458831