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

dc.creatorNogueira, AL
dc.creatorLona, LMF
dc.creatorMachado, RAF
dc.date2004
dc.date42005
dc.date2014-11-16T20:03:10Z
dc.date2015-11-26T16:25:06Z
dc.date2014-11-16T20:03:10Z
dc.date2015-11-26T16:25:06Z
dc.date.accessioned2018-03-28T23:05:55Z
dc.date.available2018-03-28T23:05:55Z
dc.identifierJournal Of Applied Polymer Science. John Wiley & Sons Inc, v. 91, n. 2, n. 871, n. 882, 2004.
dc.identifier0021-8995
dc.identifierWOS:000186915200024
dc.identifier10.1002/app.13258
dc.identifierhttp://www.repositorio.unicamp.br/jspui/handle/REPOSIP/56968
dc.identifierhttp://www.repositorio.unicamp.br/handle/REPOSIP/56968
dc.identifierhttp://repositorio.unicamp.br/jspui/handle/REPOSIP/56968
dc.identifier.urihttp://repositorioslatinoamericanos.uchile.cl/handle/2250/1268560
dc.descriptionContinuous 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. (C) 2003 Wiley Periodicals, Inc.
dc.description91
dc.description2
dc.description871
dc.description882
dc.languageen
dc.publisherJohn Wiley & Sons Inc
dc.publisherHoboken
dc.publisherEUA
dc.relationJournal Of Applied Polymer Science
dc.relationJ. Appl. Polym. Sci.
dc.rightsfechado
dc.rightshttp://olabout.wiley.com/WileyCDA/Section/id-406071.html
dc.sourceWeb of Science
dc.subjectpolymerization
dc.subjecttubular reactor
dc.subjectneural networks
dc.subjectmodeling
dc.subjectsimulations
dc.subjectEmulsion Polymerization
dc.subjectThermal Polymerization
dc.subjectStyrene
dc.subjectBatch
dc.subjectQuality
dc.titleContinuous polymerization in tubular reactors with prepolymerization: analysis using two-dimensional phenomenological model and hybrid model with neural networks
dc.typeArtículos de revistas


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