Open AccessDeconvolution of Microstructural Distributions of Ethylene/1- Butene Copolymer Blends using Artifi cial Neural Network
Author(s) -
Piriyakorn Piriyakulkit,
Siripon Anantawaraskul
Publication year - 2022
Publication title -
warasan khana witthayasat maha witthayalai chiang mai
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.164
H-Index - 20
ISSN - 0125-2526
DOI - 10.12982/cmjs.2022.019
Subject(s) - artificial neural network , biological system , materials science , deconvolution , polymer , backpropagation , copolymer , multilayer perceptron , computer science , process engineering , algorithm , artificial intelligence , composite material , engineering , biology
Polymer blending is a useful approach to tailor-make microstructural distributions (e.g., molecular weight distribution (MWD), chemical composition distribution (CCD)) and product properties. A technique to help identify polymer components and their weight fractions in the unknown blends is desirable for the product development. In this work, artifi cial neural network (ANN) models were developed to help identify this information from microstructural distributions and validated with simulated datasets of various binary blends of polyolefi n with different characteristics. The proposed models are multilayer perceptron network with 2 hidden layers; the backpropagation algorithm is used for the network training. Three types of input data were compared: (1) MWD, (2) CCD, and (3) MWD+CCD. Optimum topologies for each types of input data were also determined.