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Deconvolution of Microstructural Distributions of Ethylene/1- Butene Copolymer Blends using Artificial Neural Network


Paper Type 
 Contributed Paper
Title 
 Deconvolution of Microstructural Distributions of Ethylene/1- Butene Copolymer Blends using Artificial Neural Network
Author 
 Piriyakorn Piriyakulkit and Siripon Anantawaraskul
Email 
 fengsia@ku.ac.th
Abstract:

     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, artificial 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.
Start & End Page 
 217 - 222
Received Date 
 2021-03-20
Revised Date 
 2021-08-18
Accepted Date 
 2021-10-06
Full Text 
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Keyword 
 artificial neural network (ANN), blends, deconvolution, microstructures, polyethylene
Volume 
 Vol.49 No.1 (Special Issue I : Jan 2022)
DOI 
 https://doi.org/10.12982/CMJS.2022.019
Citation 
 Piriyakulkit P. and Anantawaraskul S., Deconvolution of Microstructural Distributions of Ethylene/1- Butene Copolymer Blends using Artificial Neural Network, Chiang Mai Journal of Science, 2022; 49(1): e2022019. DOI 10.12982/CMJS.2022.019.
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