Development of a predictive model for study of skin-core phenomenon in stabilization process of PAN precursor

Gelayol Golkarnarenji; Minoo Naebe; Jeffrey S. Church; Khashayar Badii; Alireza Bab-Hadiashar; Stephen Atkiss; Hamid Khayyam

Journal of Industrial and Engineering Chemistry, Vol.49, 46-60, May, 2017

DOI10.1016/j.jiec.2016.12.027 Export Citation

Development of a predictive model for study of skin-core phenomenon in stabilization process of PAN precursor

Gelayol Golkarnarenji¹, Minoo Naebe^{1, †}, Jeffrey S. Church², Khashayar Badii¹, Alireza Bab-Hadiashar³, Stephen Atkiss¹, and Hamid Khayyam^{1, 3, †}

¹Institute for Frontier Materials, Carbon Nexus, Deakin University, VIC 3216, Australia
²Commonwealth Scientific and Industrial Research Organization (CSIRO), Manufacturing, Waurn Ponds, VIC 3216, Australia
³School of Engineering, RMIT University, Melbourne, Australia

E-mail:,

Studying the presence and progress of fiber defects, such as skin-core structure, is an important tool for analysis of a chemical process. In this article, the skin core morphology has been analyzed by optical microscopic (OM) images and Fourier transform infrared attenuated total reflectance mapping (FTIR-ATR mapping). The results of FTIR-ATR mapping showed that the fiber is almost uniform in the core area while OM images are accurate enough to be used for skin-core analysis. Using OM images, the core ratio of samples were measured to quantify the skin-core structure. Non-parametric kernel density estimation methods have then been compared with conventional parametric distribution models using these data. The results reveal that the parametric methods cannot adequately describe the skin-core phenomenon and that the non-parametric distributions are more appropriate for the quantification of skin-core morphology. By applying the non-parametric distributions, a model has been developed, which describes the relationship between the skin-core defect and the operation parameters of the fiber production. This approach can be used to predict the probability of skin-core occurrence and can be used to decrease the presence of this phenomenon in the carbon fibers production industry. Our results show that temperature is one of the most significant operational parameter at a typical oxygen concentration (in air at atmospheric pressure) governing the skin-core formation.

Keywords:Thermal stabilization;Skin-core morphology;Non-parametric density estimation;Probability density function;Non-linear predictive modelling;Stochastic model

[References]

Chand S, J. Mater. Sci., 35(6), 1303 (2000)
Karacan I, Erdogan G, Fibers Polym., 13, 855 (2012)
Hou XM, Chou KC, Corros. Sci., 52, 1093 (2010)
Wiles KB, MSc thesis, Virginia Polytechnic Institute and State University, Blacksburh, Virginia, USA, 2002.
Edie DD, Carbon, 36, 345 (1998)
Sha JJ, Dai JX, Li J, Wei ZQ, Hausherr JM, Krenkel W, Appl. Surf. Sci., 274, 89 (2013)
Zhang W, Liu J, Liang J, J. Mater. Sci. Technol., 20, 369 (2004)
Dunham MG, Edie DD, Carbon, 30, 435 (1992)
Bashir Z, Carbon, 29, 1081 (1991)
Khayyam H, Naebe M, Zabihi O, Zamani R, Atkiss S, Fox B, IEEE Trans. Ind. Inform., 11, 887 (2015)
Yu MJ, Wang CG, Bai YJ, Ji MX, Xu Y, Polym. Bull., 58(5-6), 933 (2007)
Lv MY, Ge HY, Chen J, J. Polym. Res., 16, 513 (2009)
Badii M, Naebe G, Golkarnarenji N, Dhami S, Atkiss D, Buckmaster B, Energy Saving in Electric Heater of Carbon Fiber Stabilization Oven, (2014) p. 109.
Liu X, Zhu C, Guo J, Liu Q, Dong H, Gu Y, Liu R, Zhao N, Zhang Z, Xu J, Mater. Lett., 128, 417 (2014)
Kong L, Liu H, Cao W, Xu L, Fibers Polym., 15, 2480 (2014)
Morgan P, Carbon Fibers and their Composites, CRC Press, USA, 2005.
Yu MJ, Wang CG, Bai YJ, Xu Y, Zhu B, J. Appl. Polym. Sci., 107(3), 1939 (2008)
Nunna S, Naebe M, Hameed N, Creighton C, Naghashian S, Jennings MJ, Atkiss S, Setty M, Fox BL, Polym. Degrad. Stab., 125, 105 (2016)
Morris EA, Weisenberger MC, Abdallah MG, Vautard F, Grappe H, Ozcan S, Paulauskas FL, Eberle C, Jackson D, Mecham SJ, Carbon, 101, 245 (2016)
Ge HY, Liu HS, Chen J, Wang CG, J. Appl. Polym. Sci., 113(4), 2413 (2009)
Fitzer E, Frohs W, Heine M, Carbon, 24, 387 (1986)
Khayyam H, Naebe M, Bab-Hadiashar A, Jamshidi F, Li QX, Atkiss S, Buckmaster D, Fox B, Appl. Energy, 158, 643 (2015)
Qin Z, Li W, Xiong X, Electr Pow Syst. Res., 81, 2139 (2011)
Elgammal A, Duraiswami R, Harwood D, Davis LS, Proc. IEEE, 90, 1151 (2002)
Pitt D, Guillen M, Bolance C, Estimation of Parametric and Nonparametric Models for Univariate Claim Severity Distributions.An Approach Using R, Xarxa de Referencia en Economia Aplicada (XREAP), 2011 Spain.
Montes-Moran MA, Martı’nez-Alonso A, Tascon JMD, Paiva MC, Bernardo CA, Carbon, 39, 1057 (2001)
Xu H, Sun Y, Chen X, Chin. Sci. Bull., 51, 1255 (2006)
Hocker H, Pure Appl. Chem., 74, 423 (2002)
Dai XJ, Kviz L, Study of Atmospheric and Low Pressure Plasma Modification on the Surface Properties of Synthetic and Natural Fibres, CSIRO, Melbourne, Australia, 2001 p. 1.
Mukhopadhyay SK, Zhu Y, Text. Res. J., 65, 25 (1995)
Zhu D, Koganemaru A, Xu CY, Shen QD, Li SL, Matsuo M, J. Appl. Polym. Sci., 87(13), 2063 (2003)
Belyaev SS, Arkhangelsky IV, Makarenko IV, Thermochim. Acta, 507-508, 9 (2010)
Cho D, Carbon, 34, 1151 (1996)
Min BG, Sreekumar TV, Uchida T, Kumar S, Carbon, 43, 599 (2005)
Shiedlin A, Marom G, Zilkha A, Polymer, 26, 447 (1985)
Wang J, Hu L, Yang C, Zhao W, Lu Y, RSC Adv., 6, 73404 (2016)
Research Services Branch, ImageJ, Image Processing and Analysis in Java, National Institute of Mental Health Bethesda, Maryland, USA, 2016.
Alarifi I, Alharbi A, Khan W, Swindle A, Asmatulu R, Materials, 8, 7017 (2015)
Xue Y, Liu J, Lian F, Liang J, Polym. Degrad. Stab., 98, 2259 (2013)
Badii K, Church JS, Golkarnarenji G, Naebe M, Khayyam H, Polym. Degrad. Stabil., 131, 53 (2016)
Martinez WL, Martinez AR, Computational Statistics Handbook with MATLAB, CRC Press, USA, 2007.
Silverman BW, Density Estimation for Statistics and Data Analysis, Chapman & Hall, UK, 1986.
Wand MP, Jones MC, Kernel Smoothing, CRC Press, USA, 1994.
Murphy MP, Machine Learning: A Probabilistic Perspective, MIT Press, USA, 2012.
Khayyam H, IEEE Trans. Veh. Technol., 62, 61 (2013)
Marron J, Nolan D, Stat. Probab. Lett., 7, 195 (1988)
Huang S, Meng SX, Yang Y, Can. J. For. Res.-Rev. Can. Rech. For, 39, 418 (2009)
Barbeau EJ, Polynomials, Springer-Verlag, New York, 1989.
Liao G, Ming J, Wei B, Xiang H, Ai NJP, Dai C, Xie X, Li M, Wind Power Prediction Errors Model and Algorithm Based on Non-parametric Kernel Density Estimation, 5th International Conference on Electric Utility Deregulation and Restructuring and Power Technologies (DRPT) (2015) Changsha, China, p. 1864.
Rasmussen CE, Gaussian Processes in Machine Learning, Springer, Berlin, Germany, 2004 p. 63.
Shirvanimoghaddam K, Khayyam H, Abdizadeh H, Karbalaei Akbari M, Pakseresht AH, Ghasali E, Naebe M, Mater. Sci. Eng. A-Struct. Mater. Prop. Microstruct. Process., 658, 135 (2016)

[1] Chand S, J. Mater. Sci., 35(6), 1303 (2000)

[2] Karacan I, Erdogan G, Fibers Polym., 13, 855 (2012)

[3] Hou XM, Chou KC, Corros. Sci., 52, 1093 (2010)

[4] Wiles KB, MSc thesis, Virginia Polytechnic Institute and State University, Blacksburh, Virginia, USA, 2002.

[5] Edie DD, Carbon, 36, 345 (1998)

[6] Sha JJ, Dai JX, Li J, Wei ZQ, Hausherr JM, Krenkel W, Appl. Surf. Sci., 274, 89 (2013)

[7] Zhang W, Liu J, Liang J, J. Mater. Sci. Technol., 20, 369 (2004)

[8] Dunham MG, Edie DD, Carbon, 30, 435 (1992)

[9] Bashir Z, Carbon, 29, 1081 (1991)

[10] Khayyam H, Naebe M, Zabihi O, Zamani R, Atkiss S, Fox B, IEEE Trans. Ind. Inform., 11, 887 (2015)

[11] Yu MJ, Wang CG, Bai YJ, Ji MX, Xu Y, Polym. Bull., 58(5-6), 933 (2007)

[12] Lv MY, Ge HY, Chen J, J. Polym. Res., 16, 513 (2009)

[13] Badii M, Naebe G, Golkarnarenji N, Dhami S, Atkiss D, Buckmaster B, Energy Saving in Electric Heater of Carbon Fiber Stabilization Oven, (2014) p. 109.

[14] Liu X, Zhu C, Guo J, Liu Q, Dong H, Gu Y, Liu R, Zhao N, Zhang Z, Xu J, Mater. Lett., 128, 417 (2014)

[15] Kong L, Liu H, Cao W, Xu L, Fibers Polym., 15, 2480 (2014)

[16] Morgan P, Carbon Fibers and their Composites, CRC Press, USA, 2005.

[17] Yu MJ, Wang CG, Bai YJ, Xu Y, Zhu B, J. Appl. Polym. Sci., 107(3), 1939 (2008)

[18] Nunna S, Naebe M, Hameed N, Creighton C, Naghashian S, Jennings MJ, Atkiss S, Setty M, Fox BL, Polym. Degrad. Stab., 125, 105 (2016)

[19] Morris EA, Weisenberger MC, Abdallah MG, Vautard F, Grappe H, Ozcan S, Paulauskas FL, Eberle C, Jackson D, Mecham SJ, Carbon, 101, 245 (2016)

[20] Ge HY, Liu HS, Chen J, Wang CG, J. Appl. Polym. Sci., 113(4), 2413 (2009)

[21] Fitzer E, Frohs W, Heine M, Carbon, 24, 387 (1986)

[22] Khayyam H, Naebe M, Bab-Hadiashar A, Jamshidi F, Li QX, Atkiss S, Buckmaster D, Fox B, Appl. Energy, 158, 643 (2015)

[23] Qin Z, Li W, Xiong X, Electr Pow Syst. Res., 81, 2139 (2011)

[24] Elgammal A, Duraiswami R, Harwood D, Davis LS, Proc. IEEE, 90, 1151 (2002)

[25] Pitt D, Guillen M, Bolance C, Estimation of Parametric and Nonparametric Models for Univariate Claim Severity Distributions.An Approach Using R, Xarxa de Referencia en Economia Aplicada (XREAP), 2011 Spain.

[26] Montes-Moran MA, Martı’nez-Alonso A, Tascon JMD, Paiva MC, Bernardo CA, Carbon, 39, 1057 (2001)

[27] Xu H, Sun Y, Chen X, Chin. Sci. Bull., 51, 1255 (2006)

[28] Hocker H, Pure Appl. Chem., 74, 423 (2002)

[29] Dai XJ, Kviz L, Study of Atmospheric and Low Pressure Plasma Modification on the Surface Properties of Synthetic and Natural Fibres, CSIRO, Melbourne, Australia, 2001 p. 1.

[30] Mukhopadhyay SK, Zhu Y, Text. Res. J., 65, 25 (1995)

[31] Zhu D, Koganemaru A, Xu CY, Shen QD, Li SL, Matsuo M, J. Appl. Polym. Sci., 87(13), 2063 (2003)

[32] Belyaev SS, Arkhangelsky IV, Makarenko IV, Thermochim. Acta, 507-508, 9 (2010)

[33] Cho D, Carbon, 34, 1151 (1996)

[34] Min BG, Sreekumar TV, Uchida T, Kumar S, Carbon, 43, 599 (2005)

[35] Shiedlin A, Marom G, Zilkha A, Polymer, 26, 447 (1985)

[36] Wang J, Hu L, Yang C, Zhao W, Lu Y, RSC Adv., 6, 73404 (2016)

[37] Research Services Branch, ImageJ, Image Processing and Analysis in Java, National Institute of Mental Health Bethesda, Maryland, USA, 2016.

[38] Alarifi I, Alharbi A, Khan W, Swindle A, Asmatulu R, Materials, 8, 7017 (2015)

[39] Xue Y, Liu J, Lian F, Liang J, Polym. Degrad. Stab., 98, 2259 (2013)

[40] Badii K, Church JS, Golkarnarenji G, Naebe M, Khayyam H, Polym. Degrad. Stabil., 131, 53 (2016)

[41] Martinez WL, Martinez AR, Computational Statistics Handbook with MATLAB, CRC Press, USA, 2007.

[42] Silverman BW, Density Estimation for Statistics and Data Analysis, Chapman & Hall, UK, 1986.

[43] Wand MP, Jones MC, Kernel Smoothing, CRC Press, USA, 1994.

[44] Murphy MP, Machine Learning: A Probabilistic Perspective, MIT Press, USA, 2012.

[45] Khayyam H, IEEE Trans. Veh. Technol., 62, 61 (2013)

[46] Marron J, Nolan D, Stat. Probab. Lett., 7, 195 (1988)

[47] Huang S, Meng SX, Yang Y, Can. J. For. Res.-Rev. Can. Rech. For, 39, 418 (2009)

[48] Barbeau EJ, Polynomials, Springer-Verlag, New York, 1989.

[49] Liao G, Ming J, Wei B, Xiang H, Ai NJP, Dai C, Xie X, Li M, Wind Power Prediction Errors Model and Algorithm Based on Non-parametric Kernel Density Estimation, 5th International Conference on Electric Utility Deregulation and Restructuring and Power Technologies (DRPT) (2015) Changsha, China, p. 1864.

[50] Rasmussen CE, Gaussian Processes in Machine Learning, Springer, Berlin, Germany, 2004 p. 63.

[51] Shirvanimoghaddam K, Khayyam H, Abdizadeh H, Karbalaei Akbari M, Pakseresht AH, Ghasali E, Naebe M, Mater. Sci. Eng. A-Struct. Mater. Prop. Microstruct. Process., 658, 135 (2016)