Prediction of the gas solubility in polymers by a radial basis function neural network based on chaotic self-adaptive particle swarm optimization and a clustering method-Reference-Cited by-同舟云学术

Prediction of the gas solubility in polymers by a radial basis function neural network based on chaotic self-adaptive particle swarm optimization and a clustering method

Published:2013-07-08 Issue:5 Volume:130 Page:3825-3832
ISSN:0021-8995
Container-title:Journal of Applied Polymer Science
language:en
Short-container-title:J. Appl. Polym. Sci.

Author:

Li Mengshan,Huang Xingyuan¹,Liu Hesheng¹,Liu Bingxiang²,Wu Yan²

Affiliation:

1. College of Mechanical and Electric Engineering; Nanchang University; Nanchang; China

2. School of Information Engineering; Jingdezhen Ceramic Institute; Jingdezhen; China

Publisher

Wiley

Subject

Materials Chemistry,Polymers and Plastics,Surfaces, Coatings and Films,General Chemistry

Link

http://onlinelibrary.wiley.com/wol1/doi/10.1002/app.39525/fullpdf

Reference42 articles.

1. Artificial Neural Network modeling of solubility of supercritical carbon dioxide in 24 commonly used ionic liquids

2. Solubility and diffusivity of CO2 in carboxylated polyesters

3. Supercritical carbon dioxide as a green solvent for processing polymer melts: Processing aspects and applications

4. Solubilities and diffusion coefficients of carbon dioxide and nitrogen in polypropylene, high-density polyethylene, and polystyrene under high pressures and temperatures

5. Solubilities of carbon dioxide and nitrogen in polystyrene under high temperature and pressure

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