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New Approach in Modeling of Metallocene‐Catalyzed Olefin Polymerization Using Artificial Neural Networks
Author(s) -
Ahmadi Mostafa,
Nekoomanesh Mehdi,
Arabi Hassan
Publication year - 2009
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.200800088
Subject(s) - metallocene , polymerization , post metallocene catalyst , olefin polymerization , materials science , artificial neural network , catalysis , reaction rate , work (physics) , reaction rate constant , olefin fiber , polymer chemistry , thermodynamics , chemistry , computer science , organic chemistry , kinetics , composite material , polymer , physics , artificial intelligence , classical mechanics
A new approach for the estimation of kinetic rate constants in olefin polymerization using metallocene catalysts is presented. The polymerization rate has been modeled using the method of moments. An ANN has been used and trained to behave like the mathematical model developed before, so that it gets polymerization rate at different reaction times and predicts reaction rate constants. The network was trained using modeling results in desired operational window. The polymerization rates were normalized to make the network work independent of operational conditions. The model has also been applied to real polymerization rate data and the predictions were satisfactory. This model is specially useful in comparing different new metallocene catalysts.