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Journal of Engineering
Volume 2013 (2013), Article ID 515704, 6 pages
Research Article

Prediction of the Styrene Butadiene Rubber Performance by Emulsion Polymerization Using Backpropagation Neural Network

1State Key Laboratory of Chemical Engineering, East China University of Science and Technology, Shanghai 200237, China
2Jilin Petrochemical Company, Petro China, Jilin 132021, China

Received 16 August 2012; Revised 29 November 2012; Accepted 1 December 2012

Academic Editor: Alireza Khataee

Copyright © 2013 Yan-jiang Jin 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.


The effect of the amounts of initiator, emulsifier, and molecular weight regulator on the styrene butadiene rubber performance was investigated, based on the industrial original formula. It was found that the polymerization rate was increased with the increased dosage of initiator and emulsifier, and together with replenishing molecular weight regulator will make the Mooney viscosity of rubber meet the national standard when the conversion rate reaches 70%. The backpropagation neural network was trained by the original formula and ameliorated formula on the basis of Levenberg-Marquardt algorithm, and the relative error between the simulation results and experimental data is less than 1%. The good consistency shows that the BP neural network could predict the product performances in different formula conditions. It would pave the way for adjustment of the SBR formulation and prediction of the product performances.