You are not logged in.

Stochastic optimization models for energy management in carbonization process of carbon fiber production

Khayyam, Hamid, Naebe, Minoo, Bab-Hadiashar, Alireza, Jamshidi, Farshid, Li, Quanxiang, Atkiss, Stephen, Buckmaster, Derek and Fox, Bronwyn 2015, Stochastic optimization models for energy management in carbonization process of carbon fiber production, Applied energy, vol. 158, pp. 643-655, doi: 10.1016/j.apenergy.2015.08.008.

Attached Files
Name Description MIMEType Size Downloads

Title Stochastic optimization models for energy management in carbonization process of carbon fiber production
Author(s) Khayyam, Hamid
Naebe, Minoo
Bab-Hadiashar, Alireza
Jamshidi, Farshid
Li, QuanxiangORCID iD for Li, Quanxiang orcid.org/0000-0002-0190-1930
Atkiss, Stephen
Buckmaster, Derek
Fox, Bronwyn
Journal name Applied energy
Volume number 158
Start page 643
End page 655
Total pages 13
Publisher Elsevier
Place of publication Amsterdam, The Netherlands
Publication date 2015-11-15
ISSN 0306-2619
Keyword(s) Science & Technology
Technology
Energy & Fuels
Engineering, Chemical
Engineering
HT furnace energy management
Stochastic optimization models
Genetic algorithm
Convex Hull
Mixed integer linear programming
Carbonization process
WEIBULL DISTRIBUTION
Summary Industrial producers face the task of optimizing production process in an attempt to achieve the desired quality such as mechanical properties with the lowest energy consumption. In industrial carbon fiber production, the fibers are processed in bundles containing (batches) several thousand filaments and consequently the energy optimization will be a stochastic process as it involves uncertainty, imprecision or randomness. This paper presents a stochastic optimization model to reduce energy consumption a given range of desired mechanical properties. Several processing condition sets are developed and for each set of conditions, 50 samples of fiber are analyzed for their tensile strength and modulus. The energy consumption during production of the samples is carefully monitored on the processing equipment. Then, five standard distribution functions are examined to determine those which can best describe the distribution of mechanical properties of filaments. To verify the distribution goodness of fit and correlation statistics, the Kolmogorov-Smirnov test is used. In order to estimate the selected distribution (Weibull) parameters, the maximum likelihood, least square and genetic algorithm methods are compared. An array of factors including the sample size, the confidence level, and relative error of estimated parameters are used for evaluating the tensile strength and modulus properties. The energy consumption and N2 gas cost are modeled by Convex Hull method. Finally, in order to optimize the carbon fiber production quality and its energy consumption and total cost, mixed integer linear programming is utilized. The results show that using the stochastic optimization models, we are able to predict the production quality in a given range and minimize the energy consumption of its industrial process.
Language eng
DOI 10.1016/j.apenergy.2015.08.008
Field of Research 091399 Mechanical Engineering not elsewhere classified
14 Economics
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, Elsevier
Persistent URL http://hdl.handle.net/10536/DRO/DU:30078736

Document type: Journal Article
Collection: Institute for Frontier Materials
Connect to link resolver
 
Unless expressly stated otherwise, the copyright for items in DRO is owned by the author, with all rights reserved.

Versions
Version Filter Type
Citation counts: TR Web of Science Citation Count  Cited 11 times in TR Web of Science
Scopus Citation Count Cited 12 times in Scopus
Google Scholar Search Google Scholar
Access Statistics: 114 Abstract Views, 1 File Downloads  -  Detailed Statistics
Created: Wed, 10 Feb 2016, 13:47:32 EST

Every reasonable effort has been made to ensure that permission has been obtained for items included in DRO. If you believe that your rights have been infringed by this repository, please contact drosupport@deakin.edu.au.