Dynamic prediction models and optimization of polyacrylonitrile (PAN) stabilization processes for production of carbon fiber

Khayyam, Hamid, Naebe, Minoo, Zabihi, Omid, Zamani, Reza, Atkiss,Stephen and Fox, Brownwyn 2015, Dynamic prediction models and optimization of polyacrylonitrile (PAN) stabilization processes for production of carbon fiber, IEEE Transactions on Industrial Informatics, vol. 11, no. 4, pp. 887-896, doi: 10.1109/TII.2015.2434329.

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Title Dynamic prediction models and optimization of polyacrylonitrile (PAN) stabilization processes for production of carbon fiber
Author(s) Khayyam, Hamid
Naebe, MinooORCID iD for Naebe, Minoo orcid.org/0000-0002-0607-6327
Zabihi, OmidORCID iD for Zabihi, Omid orcid.org/0000-0001-8065-2671
Zamani, Reza
Fox, Brownwyn
Journal name IEEE Transactions on Industrial Informatics
Volume number 11
Issue number 4
Start page 887
End page 896
Total pages 10
Publisher IEEE
Place of publication Piscataway, N.J.
Publication date 2015
ISSN 1551-3203
Keyword(s) Genetic algorithms (GAs)
LevenbergMarquardt algorithm (LMA)-neural networks (LMA-NNs)
polyacrylonitrile (PAN)
prediction models
process control
thermal stabilization
Science & Technology
Automation & Control Systems
Computer Science, Interdisciplinary Applications
Engineering, Industrial
Computer Science
Summary Thermal stabilization process of polyacrylonitrile (PAN) is the slowest and the most energy-consuming step in carbon fiber production. As such, in industrial production of carbonfiber, this step is considered as amajor bottleneck in the whole process. Stabilization process parameters are usually many in number and highly constrained, leading to high uncertainty. The goal of this paper is to study and analyze the carbon fiber thermal stabilization process through presenting several effective dynamic models for the prediction of the process. The key point with using dynamic models is that using an evolutionary search technique, the heat of reaction can be optimized. The employed components of the study are Levenberg–Marquardt algorithm (LMA)-neural network (LMA-NN), Gauss–Newton (GN)-curve fitting, Taylor polynomial method, and a genetic algorithm. The results show that the procedure can effectively optimize a given PAN fiber heat of reaction based on determining the proper values of heating rampand temperature
Language eng
DOI 10.1109/TII.2015.2434329
Field of Research 091399 Mechanical Engineering not elsewhere classified
08 Information And Computing Sciences
09 Engineering
10 Technology
Socio Economic Objective 850799 Energy Conservation and Efficiency not elsewhere classified
HERDC Research category C1 Refereed article in a scholarly journal
ERA Research output type C Journal article
Copyright notice ©2015, IEEE
Persistent URL http://hdl.handle.net/10536/DRO/DU:30076644

Document type: Journal Article
Collections: Institute for Frontier Materials
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