Multiple response optimization of styrene–butadiene rubber emulsion polymerization
A multiple response optimization of styrene-butadiene rubber (SBR) emulsion batch polymerization is proposed. Several properties of latex and rubber were optimized to obtain a particular grade of SBR, namely 1712. Artificial neural networks (ANNs) were employed for the modelling of the following pro...
| Autores: | , , |
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| Tipo de recurso: | artículo |
| Estado: | Versión publicada |
| Fecha de publicación: | 2009 |
| País: | Argentina |
| Institución: | Consejo Nacional de Investigaciones Científicas y Técnicas |
| Repositorio: | CONICET Digital (CONICET) |
| Idioma: | inglés |
| OAI Identifier: | oai:ri.conicet.gov.ar:11336/105022 |
| Acceso en línea: | http://hdl.handle.net/11336/105022 |
| Access Level: | acceso abierto |
| Palabra clave: | SBR Emulsion polymerization Artificial neural networks Multiple response optimization Desirability function https://purl.org/becyt/ford/1.4 https://purl.org/becyt/ford/1 |
| Sumario: | A multiple response optimization of styrene-butadiene rubber (SBR) emulsion batch polymerization is proposed. Several properties of latex and rubber were optimized to obtain a particular grade of SBR, namely 1712. Artificial neural networks (ANNs) were employed for the modelling of the following properties: solid content of latex, Mooney viscosity and polydispersity. The training was done by feeding the ANNs with experimental data obtained from a central composite design in which the concentration of some of the polymerization reagents (initiator, activator and chain transfer agent) was varied. The onedimensional desirability functionwas used for optimization, in order to obtain a single set of reaction conditions for the multiple responses. With optimum conditions, polymerization experiments were carried out and good agreementwas found between predicted and experimental values of the required properties. |
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