| 光谱学与光谱分析 |
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| Applications of On-Line Near Infrared Spectroscopy Monitoring Technology in Polymer Processing |
| CHEN Ru-huang, WANG Xiao-lin, LIN Xiao-kai, HU Xin, JIN Gang* |
| National Engineering Research Center of Novel Equipment for Polymer Processing, The Key Laboratory of Polymer Processing Engineering of Ministry of Education, South China University of Technology, Guangzhou 510641, China |
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Abstract Due to the significant impact of processing on the performance of polymer products, it is crucial to develop in-line monitoring methods on processing. Based on the feedback data from in-line monitoring the processing parameters can be adjusted, which will contribute to the stability of production, thereby ensuring product quality, reducing energy waste and improving production efficiency. Near infrared spectroscopy (NIR), a low-cost, real-time and accurately quantitative analysis technology, has been widely used in many areas but still under study in polymer processing. The applications of in-line NIR monitoring technology in measuring the content of component, melt index, melt density and dispersion of filler of polymer during processing were reviewed. The existing problems about in-line NIR monitoring technology were pointed out, as well as the suggestions for the corresponding problems. The future trends of in-line NIR monitoring technology were discussed. With the development of fiber optic spectrometer, computer science and chemometrics, it is foreseen that the in-line NIR monitoring technology will make considerable progress in the stability of raw data, methods of pretreatment and modeling, the robustness and accuracy of model. Therefore, in-line NIR monitoring technology will be applied to more areas generating the great economic and environmental value.
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Received: 2014-05-11
Accepted: 2014-08-29
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Corresponding Authors:
JIN Gang
E-mail: pmrdd@scut.edu.cn
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[1] HONG Ding-yi(洪定一). Chemical Industry and Engineering Progress(化工进展), 2013, 32(3): 481.
[2] Dumitrescu O R, Baker D C, Foster G M, et al. Polymer Testing, 2005, 24(3): 367.
[3] Workman J, Jr., Lavine B, Chrisman R, et al. Analytical Chemistry, 2011, 83(12): 4557.
[4] Vaidyanathan S, Macaloney G, Harvey L M, et al. Applied Spectroscopy, 2001, 55(4): 444.
[5] Wood J, Turner P H. Applied Spectroscopy, 2003, 57(3): 293.
[6] De Beer T, Burggraeve A, Fonteyne M, et al. International Journal of Pharmaceutics, 2011, 417(1-2): 32.
[7] McGlone V A, Martinsen P J, Clark C J, et al. Postharvest Biology and Technology, 2005, 37(2): 142.
[8] Alig I, Steinhoff B, Lellinger D. Measurement Science and Technology, 2010, 21(6): 062001.
[9] Coates P D, Barnes S E, Sibley M G, et al. Polymer, 2003, 44(19): 5937.
[10] Inberg J, Gaymans R. Polymer, 2002, 43(8): 2425.
[11] Inberg J, Gaymans R. Polymer, 2002, 43(15): 4197.
[12] Inberg J, Gaymans R. Polymer, 2002, 43(13): 3767.
[13] Miller R G J, Stace B. Laboratory Methods in Infrared Spectroscopy, 2nd ed. London: Heyden & Son, 1972. 48.
[14] Hansen M G, Khettry A. Polymer Engineering & Science, 1994, 34(23): 1758.
[15] Batra J, Khettry A, Hansen M G. Polymer Engineering & Science, 1994, 34(23): 1767.
[16] Khettry A, Hansen M G. Polymer Engineering & Science, 1996, 36(9): 1232.
[17] Barnes S E, Sibley M G, Edwards H G M, et al. Transactions of the Institute of Measurement and Control, 2007, 29(5): 453.
[18] Ghita O R, Baker D C, Evans K E. Polymer Testing, 2008, 27(4): 459.
[19] Bledzki A K, Faruk O. Journal of Applied Polymer Science, 2005, 97(3): 1090.
[20] Balasuriya P, Ye L, Mai Y-W. Composites Part A: Applied Science and Manufacturing, 2001, 32(5): 619.
[21] Hansen M, Vedula S. Journal of Applied Polymer Science, 1998, 68(6): 859.
[22] Nagata T, Ohshima M, Tanigaki M. Polymer Engineering & Science, 2000, 40(5): 1107.
[23] Dong Y, Bhattacharyya D. Composites Part A: Applied Science and Manufacturing, 2008, 39(7): 1177.
[24] Lee Y, Park C B, Sain M, et al. Journal of Applied Polymer Science, 2007, 105(4): 1993.
[25] Barbas J M, Machado A V, Covas J A. Polymer Testing, 2012, 31(4): 527.
[26] Barbas J M, Machado A V, Covas J A. Journal of Applied Polymer Science, 2013, 127(6): 4899. |
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