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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 FoxThermal 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
and temperature
History
Journal
IEEE Transactions on Industrial InformaticsVolume
11Issue
4Pagination
887 - 896Publisher
IEEELocation
Piscataway, N.J.Publisher DOI
ISSN
1551-3203Language
engPublication classification
C Journal article; C1 Refereed article in a scholarly journalCopyright notice
2015, IEEEUsage metrics
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No categories selectedKeywords
Genetic algorithms (GAs)LevenbergMarquardt algorithm (LMA)-neural networks (LMA-NNs)polyacrylonitrile (PAN)prediction modelsprocess controlthermal stabilizationScience & TechnologyTechnologyAutomation & Control SystemsComputer Science, Interdisciplinary ApplicationsEngineering, IndustrialComputer ScienceEngineeringKERNEL METHODSNETWORKSKINETICS
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