Prediction of Specific Volume of Polypropylene at High Cooling Rates by Artificial Neural Networks-Reference-Cited by-同舟云学术

Prediction of Specific Volume of Polypropylene at High Cooling Rates by Artificial Neural Networks

Published:2021-10-04 Issue:40 Volume:60 Page:14434-14446
ISSN:0888-5885
Container-title:Industrial & Engineering Chemistry Research
language:en
Short-container-title:Ind. Eng. Chem. Res.

Author:

Wang Jian¹²^ORCID,Hopmann Christian²,Liu Ben²,Lockner Yannik²

Affiliation:

1. State Key Laboratory of Organic−Inorganic Composites, College of Mechanical and Electrical Engineering, Beijing University of Chemical Technology, 15 Beisanhuan East Road, Chaoyang, Beijing 100029, China

2. Institute for Plastics Processing (IKV) in Industry and Craft at RWTH Aachen University, 52062 Aachen, Germany

Funder

Deutsche Forschungsgemeinschaft

Central University Basic Research Fund of China

Alexander von Humboldt-Stiftung

National Natural Science Foundation of China

Publisher

American Chemical Society (ACS)

Subject

Industrial and Manufacturing Engineering,General Chemical Engineering,General Chemistry

Link

https://pubs.acs.org/doi/pdf/10.1021/acs.iecr.1c02622

Reference48 articles.

1. Wang, J. PVT Properties of Polymers for Injection Molding; InTech, 2012; pp 3–30.

2. Analysis of equations of state for polymers

3. Volumetric profile of polymer melts from the ISM equation of state

4. Continuous Two-Domain Equations of State for the Description of the Pressure-Specific Volume-Temperature Behavior of Polymers

5. Modeling of pvT behavior of semi-crystalline polymer based on the two-domain Tait equation of state for injection molding

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