Integration of CFD and condensation polymerization chemistry for a commercial multi-jet tubular reactor

Kolhapure, Nitin H.; Tilton, J.N.; Pereira, Candido

doi:10.1016/j.ces.2004.09.017

Cited by 11 publications

(5 citation statements)

References 14 publications

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“…Condensation polymerization [92] Several industrially significant resins -polyamides (for example, nylon), polyester, polyurethanes, polyacetals, phenol-aldehydes, polyurea, and silicones -are produced by condensation polymerization. In this example, a multi-jet tubular reactor is used for the polycondensation between monomers A and B, each having two reactive groups.…”

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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“…54,55 These methods generally focus less on the reactive aspect due to too high computational demands for the mesh size so that extensive reaction schemes are rarely incorporated. 54,[56][57][58][59] A more recent development is the coupled matrix-based stochastic kinetic Monte Carlo (CMMC) method, 60,61 which allows to track individual chains and structural elements/ defects for polymer synthesis, modification and recycling. This method samples elementary reaction events in a discrete manner per reactive phase according to micro-scale reaction probabilities originating from kinetic rate laws that are possibly adapted to correct for diffusional limitations thus local viscosity effects.…”

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Kinetic Monte Carlo residence time distributions and kinetics in view of extrusion-based polymer modification and recycling

et al. 2023

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“…In the last 20 years modeling approaches using CFD simulations to describe polymer reactors have overcome this challenge by using either simplified kinetics, as refs. 15–18 and Fox et al,19–21 and/or “user defined function”, as the group of Luo22–24 to take polymerization reaction into account, which is still numerical demanding. On the contrary, axial dispersion models describe hydrodynamics on macro scale with only one or two parameters, but allow for the implementation of complex polymerization kinetics on micro scale.…”

Section: Introductionmentioning

confidence: 99%

Simulation of Polymer Reactors Using the Compartment Modeling Approach

Cremer-Bujara

Biessey

Grünewald

2019

Macro Reaction Engineering

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A compartment modeling approach based on computational fluid dynamics (CFD) simulations is applied to a simplified static mixer geometry. Compartments are derived from velocity fields obtained from cold CFD simulations. This methodology is based on the definition of periodic flow zones (PFZ) derived from the recurrent flow profile within the static mixer. In general, PFZ can be characterized by two different compartments: flow zones with hydrodynamic behavior of a tubular reactor and dead zones exhibiting a more continuous stirred tank reactor‐like characteristic. In CFD studies the influence of changing fluid properties, for example viscosity, on flow profile due to polymerization progress is considered. In the deterministic compartment model, the continuous flow profile within the static mixer is transformed to basic reactor models interconnected via an exchange stream. To reduce model complexity and the number of model parameters, constant volumes of compartments are assumed. Changes in hydrodynamics are considered by a variable exchange flow rate as a function of Re manipulating residence time in compartments. Simulation studies show the influence of decreasing exchange flow rates with polymerization progress, as Re decreases, resulting in a greater increase of viscosity in dead zones. The reactor performance is qualitatively represented by the simulation results.

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Integration of CFD and condensation polymerization chemistry for a commercial multi-jet tubular reactor

Cited by 11 publications

References 14 publications

Mathematical Methods

Mathematical Methods

Kinetic Monte Carlo residence time distributions and kinetics in view of extrusion-based polymer modification and recycling

Simulation of Polymer Reactors Using the Compartment Modeling Approach

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