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Estimation of Average Comonomer Content of Ethylene/1‐Olefin Copolymers Using Crystallization Analysis Fractionation (Crystaf) and Artificial Neural Network (ANN)
Author(s) -
Anantawaraskul Siripon,
Chokputtanawuttilerd Nuttawat
Publication year - 2009
Publication title -
macromolecular symposia
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.257
H-Index - 76
eISSN - 1521-3900
pISSN - 1022-1360
DOI - 10.1002/masy.200950815
Subject(s) - comonomer , artificial neural network , fractionation , crystallization , copolymer , olefin fiber , materials science , biological system , calibration , chromatography , chemical engineering , computer science , mathematics , chemistry , artificial intelligence , engineering , statistics , composite material , biology , polymer
Summary: An artificial neural network (ANN) with a 4‐3‐3‐1 architecture was developed to estimate average comonomer content of ethylene/1‐olefin copolymers from crystallization analysis fractionation (Crystaf) results. The ANN was trained with a back propagation algorithm. It was found that average comonomer contents predicted from ANN agree well with experimental results for both training and testing data sets. The developed ANN was also used to systematically investigate the effects of chain microstructures and Crystaf operating conditions on Crystaf calibration curves.