Study on Increasing Conversion of Styrene/Butadiene Co-Polymerization by Adjusting Basic Recipe

The effects of initiator, modifier, electrolyte, and emulsifier on emulsion polymerization of styrene butadiene rubber were investigated. The results showed that the conversion could reach up to 70% within 12 hour with the same properties as conversion 62% without changing the kinds and specification by using the adjusted recipe: the amount of initiator was 10%, the mass ratio of initial amount to addition amount of molecular weight regulator was 100/20, addition time was at polymerization beginning 2 hour, the initial amount of electrolyte was 110%-115%, the total amount of emulsifier was 110%, the addition amount of emulsifier was 10% and adding time was at polymerization beginning 2 hour. the raw and vulcanizate properties of rubber crumb high monomer conversion of 70% by adjusting recipe were satisfied to meet the requirements of the superior product standard of styrene butadiene rubber.

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

Journal of Engineering ◽

10.1155/2013/515704 ◽

2013 ◽

Vol 2013 ◽

pp. 1-6 ◽

Cited By ~ 1

Author(s):

Yan-jiang Jin ◽

Ben-xian Shen ◽

Ruo-fan Ren ◽

Lei Yang ◽

Jun Sui ◽

...

Keyword(s):

Neural Network ◽

Molecular Weight ◽

National Standard ◽

Styrene Butadiene Rubber ◽

Butadiene Rubber ◽

Backpropagation Neural Network ◽

Marquardt Algorithm ◽

Molecular Weight Regulator ◽

Styrene Butadiene ◽

Original Formula

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.

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