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Predicting Polymer Properties via a Coupled Kinetic, Stochastic and Rheological Modeling Approach from Reaction Conditions

Zentel, Kristina M. ; Busch, Markus (2022):
Predicting Polymer Properties via a Coupled Kinetic, Stochastic and Rheological Modeling Approach from Reaction Conditions. (Publisher's Version)
In: Macromolecular Reaction Engineering, 16 (1), Wiley-VCH, e-ISSN 1862-8338,
DOI: 10.26083/tuprints-00021542,
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Item Type: Article
Origin: Secondary publication DeepGreen
Status: Publisher's Version
Title: Predicting Polymer Properties via a Coupled Kinetic, Stochastic and Rheological Modeling Approach from Reaction Conditions
Language: English
Abstract:

A three‐step multiscale modeling approach to predict and thus control polymer properties, such as flow behavior and linear as well as non‐linear rheology, based on polymerization conditions, is developed and applied for long‐chain branched low‐density polyethylene (LDPE). The approach consists of i) a deterministic kinetic model for the description of conversion and average polymer characteristics, ii) a hybrid stochastic Monte Carlo model for the description of the polymeric microstructure, and iii) a rheology model for the evaluation of polymer melt flow properties. The modeling approach is validated via high‐pressure miniplant LDPE samples with a special focus on long‐chain branching. In the next step, the modeling approach can be successfully transferred to a tubular reactor of industrial scale. Due to its universality the approach opens up possible applications for other polymer and also copolymer systems.

Journal or Publication Title: Macromolecular Reaction Engineering
Volume of the journal: 16
Issue Number: 1
Place of Publication: Darmstadt
Publisher: Wiley-VCH
Collation: 16 Seiten
Uncontrolled Keywords: low‐density polyethylene, multiscale modeling, rheology, structure–property relationships
Classification DDC: 500 Naturwissenschaften und Mathematik > 540 Chemie
Divisions: 07 Department of Chemistry > Fachgebiet Technische Chemie
Date Deposited: 24 Jun 2022 13:10
Last Modified: 24 Jun 2022 13:10
DOI: 10.26083/tuprints-00021542
Corresponding Links:
URN: urn:nbn:de:tuda-tuprints-215429
SWORD Depositor: Deep Green
URI: https://tuprints.ulb.tu-darmstadt.de/id/eprint/21542
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