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

journal contribution
posted on 2015-01-01, 00:00 authored by Hamid Khayyam, Minoo NaebeMinoo Naebe, Omid ZabihiOmid Zabihi, R Zamani, Stephen Atkiss, Bronwyn Fox
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 ramp
and temperature

History

Journal

IEEE Transactions on Industrial Informatics

Volume

11

Issue

4

Pagination

887 - 896

Publisher

IEEE

Location

Piscataway, N.J.

ISSN

1551-3203

Language

eng

Publication classification

C Journal article; C1 Refereed article in a scholarly journal

Copyright notice

2015, IEEE