PDF simulations of ethylene decomposition in tubular LDPE reactors

Kolhapure, Nitin H.; Fox, Rodney O.; Daiβ, Andreas; Mähling, Frank‐Olaf

doi:10.1002/aic.10378

Cited by 39 publications

(24 citation statements)

References 35 publications

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“…More details of the kinetic scheme, the rate expressions, and the rate constants as well as the customized ISAT algorithm can be found in Ref. 81. The poorly mixed region is modeled using a two-dimensional axisymmetric computational domain of length 10 m and radius 1.9 cm.…”

Section: Illustrative Case Studiesmentioning

confidence: 99%

Mathematical Methods

Iedema¹,

Kolhapure²

2005

Handbook of Polymer Reaction Engineering

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IntroductionIn this chapter some mathematical methods to solve kinetic modeling problems are explained. A very sound basis for this was already laid many years ago by Flory [1]. Here, we want to present modern mathematical tools that have recently been developed through the use of computers. The focus is on the link between kinetic rate data and reactor type on one hand, and distributive properties -in one or more dimensions -on the other. These distributive or microstructural properties are concerned not only with countable quantities, such as the number of monomer units in a polymer molecule, but also with structure in the case of branched polymer molecules. With structure, we discuss the connectivity of branch points and the lengths of the segments between them. In the greater part of the text, reactors are treated in a simplified manner. We consider continuous and batch reactors, but all of them ideally mixed. The effect of incomplete mixing (segregation, macroand micromixing) is addressed in classical textbooks such as Biesenberger [2] and Dotson et al. [3]. Some recent attempts to include the impact of micromixing on distributions are available in the literature [57][58][59], but this field is still in its infancy. Nevertheless, to still provide a sound basis for issues of mixing, we devote one section to the use of computational fluid dynamics in polymer reaction engineering problems.The microstructural properties that we address are chain length, number of monomer units of one kind (copolymer), number of branch points, number of unsaturated bonds, and number of reactive monomer units (end groups in polycondensation). Problems may require solution of one or more of these properties simultaneously. Here, we will denote this as the dimensionality of the problem at hand. For instance, growth in addition polymerization can be described by a simple 1D (chain length) reaction equation and population balance.In contrast, growth in polycondensation of a trifunctional monomer A with a bifunctional monomer B requires a 3D description. The 3D distribution R n; i; k , where subscripts denote chain length, number of A end groups, number of B end groups, respectively, obeys:Note that this describes a reaction between single end groups of two different molecules; end group combinations within one (longer) molecule require a similar approach. These examples illustrate the importance of the dimensionality of the problem at hand. In general, low dimensionality can be dealt with using analytical or differential methods, while higher dimensionality soon requires a Monte Carlo sampling approach. Note that all of these methods, except MC, start with a population balance. Here we will mainly discuss ways to solve such balances of lower or higher dimensionality. This chapter will start with the description of lower-dimensional problems and show to what extent these can be successful. This often involves the reduction of the problem to lower dimensionality, inevitably leading to averaging over one or more dimensions. The most well...

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Section: Illustrative Case Studiesmentioning

confidence: 99%

Mathematical Methods

Iedema¹,

Kolhapure²

2005

Handbook of Polymer Reaction Engineering

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“…[7][8][9]); and the algorithm has been incorporated into the ANSYS/FLUENT software [3,10]. ISAT has also been used in chemical engineering [11][12][13], in solid mechanics [14], control [15] and the study of thin films and surface reaction [16,17]. Several variants of ISAT have been proposed and investigated [18][19][20][21][22][23][24].…”

Section: Introductionmentioning

confidence: 99%

An improved algorithm for in situ adaptive tabulation

Pope

2009

Journal of Computational Physics

129

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“…Here, numerical simulation methods such as computational fluid dynamics (CFD) can provide a more detailed insight into the flow conditions. In earlier publications it could be shown that the LDPE polymerization process can be modeled effectively with CFD. By adding simplified decomposition kinetics to the polymerization model , it is even possible to account for the probability of decomposition events.…”

Section: Introductionmentioning

confidence: 99%

Experimental Investigation of Ethene Decomposition by Video – Determination of Physical Data for Simulation Model

Hilfer

Degenkolb

Busch

2019

Chemie Ingenieur Technik

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In this work, the decomposition reaction of ethylene is investigated in a high‐pressure view cell. Thus, for the first time, it is possible to precisely determine the velocity and structure of the flame front under high pressure. On one hand, the experiments should provide a better understanding of the decomposition process in a time and spatially resolved manner but also about the chemistry‐turbulence interaction. On the other hand, the experiments serve as a basis and validation for the development of a numerical simulation model.

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PDF simulations of ethylene decomposition in tubular LDPE reactors

Abstract: The present study deals with turbulent reacting flow simulation inside low-density polyethylene (LDPE) tubular reactors, based on a detailed computational fluid dynamics (CFD

Cited by 39 publications

References 35 publications

Mathematical Methods

Mathematical Methods

An improved algorithm for in situ adaptive tabulation

Experimental Investigation of Ethene Decomposition by Video – Determination of Physical Data for Simulation Model

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