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Predicting Polyethylene Molecular Weight and Composition Distributions Obtained Using a Multi‐Site Catalyst in a Gas‐Phase Lab‐Scale Reactor
Author(s) -
Aiello Jennifer P.,
Jiang Yan,
Moebus Joseph A.,
Greenhalgh Brian R.,
McAuley Kimberley B.
Publication year - 2021
Publication title -
macromolecular theory and simulations
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.37
H-Index - 56
eISSN - 1521-3919
pISSN - 1022-1344
DOI - 10.1002/mats.202000079
Subject(s) - comonomer , copolymer , molar mass distribution , gel permeation chromatography , gas chromatography , polyethylene , chemistry , phase (matter) , elution , biological system , materials science , chromatography , chemical engineering , analytical chemistry (journal) , polymer , organic chemistry , engineering , biology
A dynamic model is developed to predict detailed chain‐length and comonomer incorporation behavior during gas‐phase ethylene/hexene copolymerization using a supported hafnocene catalyst. The multi‐site catalyst results in a copolymer with a broad orthogonal composition distribution (BOCD) where the high molecular‐weight tail has high hexene incorporation. The model relies on gel permeation chromatography measurements obtained using multiple detectors (GPC‐4D), so that the composition of the copolymer is determined for different chain‐length fractions. Chain‐length distributions are discretized into bins so that comparisons can be made between GPC‐4D data and model predictions. Parameter estimation is aided by an estimability‐ranking procedure and a mean‐squared‐error selection criterion to determine that 22 of 36 model parameters can be estimated using product characterization and reactor operating data from 10 semi‐batch reactor runs. An additional 4 runs are used for model validation, confirming the predictive power of the model. The proposed model can aid the selection of reactor operating conditions to achieve targeted copolymer properties.