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Modeling of Functional Group Distribution in Copolymerization: A Comparison of Deterministic and Stochastic Approaches
Author(s) -
Ali Parsa M.,
Kozhan Iurii,
Wulkow Michael,
Hutchinson Robin A.
Publication year - 2014
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.201300156
Subject(s) - copolymer , monte carlo method , polymer , computer science , distribution (mathematics) , biological system , fraction (chemistry) , composition (language) , materials science , statistical physics , algorithm , polymer chemistry , chemistry , mathematics , physics , organic chemistry , statistics , mathematical analysis , linguistics , philosophy , biology
The distribution of functional groups in polymer chains produced in radical copolymerization by starved‐feed semibatch operation is simulated using three different methodologies. Even under perfect control of the overall copolymer composition, a significant fraction of the polymer chains produced contain no functionality. A deterministic model is formulated to separately track the homopolymer chains that are produced without the desired functionality, a Monte Carlo (MC) model is written to represent the system, and a hybrid deterministic/MC approach is taken using new capabilities within the software package P REDICI . The advantages and disadvantages of each approach are discussed.