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New Approach in Modeling of Metallocene-Catalyzed Olefin Polymerization Using Artificial Neural Networks

✍ Scribed by Mostafa Ahmadi; Mehdi Nekoomanesh; Hassan Arabi

Publisher: John Wiley and Sons
Year: 2009
Tongue: English
Weight: 266 KB
Volume: 18
Category: Article
ISSN: 1022-1344
DOI: 10.1002/mats.200800088

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✦ Synopsis

Abstract

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.

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