Solubility prediction of supercritical carbon dioxide in 10 polymers using radial basis function artificial neural network based on chaotic self-adaptive particle swarm optimization and K-harmonic means-Reference-Cited by-同舟云学术

Solubility prediction of supercritical carbon dioxide in 10 polymers using radial basis function artificial neural network based on chaotic self-adaptive particle swarm optimization and K-harmonic means

Published:2015 Issue:56 Volume:5 Page:45520-45527
ISSN:2046-2069
Container-title:RSC Advances
language:en
Short-container-title:RSC Adv.

Author:

Li Mengshan¹²³⁴⁵,Huang Xingyuan⁵⁶⁷⁴,Liu Hesheng⁵⁶⁷⁴,Liu Bingxiang⁸⁹¹⁰⁴,Wu Yan¹²³⁴,Wang Lijiao⁵⁶⁷⁴

Affiliation:

1. College of Physics and Electronic Information

2. Gannan Normal University

3. Ganzhou

4. China

5. College of Mechanical & Electric Engineering

6. Nanchang University

7. Nanchang

8. School of Information Engineering

9. JingDeZhen Ceramic Institute

10. JingDeZhen

Abstract

Excellent prediction modeling of CO₂ solubility in polymers using hybrid computation algorithm.

Publisher

Royal Society of Chemistry (RSC)

Subject

General Chemical Engineering,General Chemistry

Link

http://pubs.rsc.org/en/content/articlepdf/2015/RA/C5RA07129A

Reference63 articles.

1. Continuous reactions in supercritical carbon dioxide: problems, solutions and possible ways forward

2. Recent progress in the design of advanced PEO-containing membranes for CO2 removal

3. Dual pH- and temperature-responsive RAFT-based block co-polymer micelles and polymer–protein conjugates with transient solubility

4. Solution Properties of a CO2-Soluble Fluoropolymer via Small Angle Neutron Scattering

5. An efficient cleavage of the aryl ether C–O bond in supercritical carbon dioxide–water

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