Production of low cost carbon-fiber through energy optimization of stabilization process

Golkarnarenji, G, Naebe, M, Badii, K, Milani, A, Nakhaie Jazar, R and Khayyam, H 2018, 'Production of low cost carbon-fiber through energy optimization of stabilization process', Materials, vol. 11, no. 3, pp. 1-13.


Document type: Journal Article
Collection: Journal Articles

Title Production of low cost carbon-fiber through energy optimization of stabilization process
Author(s) Golkarnarenji, G
Naebe, M
Badii, K
Milani, A
Nakhaie Jazar, R
Khayyam, H
Year 2018
Journal name Materials
Volume number 11
Issue number 3
Start page 1
End page 13
Total pages 13
Publisher MDPI AG
Abstract To produce high quality and low cost carbon fiber-based composites, the optimization of the production process of carbon fiber and its properties is one of the main keys. The stabilization process is the most important step in carbon fiber production that consumes a large amount of energy and its optimization can reduce the cost to a large extent. In this study, two intelligent optimization techniques, namely Support Vector Regression (SVR) and Artificial Neural Network (ANN), were studied and compared, with a limited dataset obtained to predict physical property (density) of oxidative stabilized PAN fiber (OPF) in the second zone of a stabilization oven within a carbon fiber production line. The results were then used to optimize the energy consumption in the process. The case study can be beneficial to chemical industries involving carbon fiber manufacturing, for assessing and optimizing different stabilization process conditions at large. © 2018 by the authors.
Subject Chemical Sciences not elsewhere classified
Keyword(s) Artificial Neural Network
Complex manufacturing systems
Intelligent optimization techniques
Limited data
Support vector machines
System identification
DOI - identifier 10.3390/ma11030385
Copyright notice © 2018 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
ISSN 1996-1944
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