Continuous polymerization in tubular reactors with prepolymerization: Analysis using two-dimensional phenomenological model and hybrid model with neural networks

Authors

  • André L. Nogueira,

    1. Departamento de Processos Químicos, Faculdade de Engenharia Química, Universidade Estadual de Campinas–Unicamp, CEP 13081-970, Campinas, São Paulo, Brazil
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  • Liliane M. F. Lona,

    Corresponding author
    1. Departamento de Processos Químicos, Faculdade de Engenharia Química, Universidade Estadual de Campinas–Unicamp, CEP 13081-970, Campinas, São Paulo, Brazil
    • Departamento de Processos Químicos, Faculdade de Engenharia Química, Universidade Estadual de Campinas–Unicamp, CEP 13081-970, Campinas, São Paulo, Brazil
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  • Ricardo A. F. Machado

    1. Departamento de Engenharia Química, Universidade Federal de Santa Catarina, P.B. 476, CEP: 88010-970, Florianópolis, Santa Catarina, Brazil
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Abstract

Continuous polymerization processes have advantages when large amounts of product are required; moreover, higher quality can be obtained because of the elimination of variability between batches. Tubular reactors are economically attractive because of their simple geometry and high heat exchange area; however, they are not commonly used for commercial purposes, mainly because of the large radial profiles. This study elucidates the operation of this kind of reactors in three different ways: first a detailed two-dimensional mathematical model was developed, in which a complete visualization of all axial and radial profiles is possible, allowing a safe analysis at different operating conditions. In a second step a system composed of a continuously stirred tank reactor in series with a tubular reactor was used. A reduction in radial profiles can be clearly observed when prepolymerization is taken into account, improving both the homogeneity and the end properties of the polymer. In a third approach neural networks (NNs) were used in parallel with a one-dimensional model. The objective of this study was to illustrate how NNs can improve the prediction capability when it is not possible to build a reliable model because of uncertainties in parameters and incomplete knowledge of the system. The NNs generated good results, showing that the hybrid model was able to accurately simulate the reactor, even when uncertainty in kinetic and diffusional parameters was imposed to the model. © 2003 Wiley Periodicals, Inc. J Appl Polym Sci 91: 871–882, 2004

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