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Language: English

Conflict of Interest: In relation to this article, we declare that there is no conflict of interest.

Publication history: Received September 13, 2010
Accepted December 28, 2010

This is an Open-Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/bync/3.0) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

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Modeling and simulation of hollow fiber CO2 separation modules

Hyun Ju Jung Sang Hoon Han¹ Young Moo Lee¹ Yeong-Koo Yeo^†

Department of Chemical Engineering, Hanyang University, Seoul 133-791, Korea ¹WCU Department of Energy, Hanyang University, Seoul 133-791, Korea

Korean Journal of Chemical Engineering, July 2011, 28(7), 1497-1504(8), 10.1007/s11814-010-0530-y

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Abstract

We developed two models for the CO2 separation process by hollow-fiber membrane modules. The explicit model, which is based on mass balances for the separation modules, is compared with the multilayer perceptrons (MLP) back-propagation neural networks model. Experimental data obtained from single-stage module with recycle are used to validate the explicit model as well as to train the MLP neural model. The effectiveness of the model is demonstrated by little discrepancy between experimental data and computational results. The explicit model for the single-stage module can easily be extended to the multi(three)-stage module. Because of the lack of experimental data for multi-stage modules, computational data from the explicit model with and without recycle are used as training data set for the MLP neural model. We examined the effects of recycle on the recovery based on the results of numerical simulations, and could_x000D_ see that the predicting performance is improved by recycle for multi-stage module. From the results of numerical simulations, the proposed models can be effectively used in the analysis and operation of gas separation processes by hollowfiber membrane modules.

Keywords

Multi-stage Module Multilayer Perceptrons Back-propagation Networks Modeling of Separation Process Carbon Dioxide Hollow Fiber Membrane

References

Choi SH, Theories and Applications of Chem. Eng., 14, 298 (2008)
Park HB, Jung CH, Lee YM, Hill AJ, Pas SJ, Mudie ST, Wagner EV, Freeman BD, Cookson DJ, Science., 318, 254 (2007)
Lee YT, Euromembrane., 200(1), 169 (2006)
Kim NS, Membr. J., 17(4), 318 (2007)
Noureddine B, Amltava S, Kamalesh S, Ind. Eng. Chem. Fundam., 25, 217 (1986)
Thundyil MJ, Koros WJ, J. Membr. Sci., 125(2), 275 (1997)
Choi SH, Kim JH, Kim BS, Lee SB, Membr. J., 15(4), 310 (2005)
Song IH, Ahn HS, Lee YJ, Jeon HS, Lee YT, Kim JH, Lee SB, Membr. J., 16(3), 204 (2006)
Song IH, Ahn HS, Lee YJ, Jeon HS, Lee YT, Kim JH, Lee SB, Membr. J., 16(4), 252 (2006)
Shahsavand A, Chenar AP, J. Membr. Sci., 297(1-2), 59 (2007)
Noble RD, Stern SA, Elsevier Science B.V., 519 (1995)
Zhao L, Riensche E, Menzer R, Blum L, Stolten D, J. Membr. Sci., 325(1), 284 (2008)
Rumelhar D, Clelland M, Parallel distributed processing, Vol.1&2, MIT Press, Boston (1986)
Kohonen T, Self-Organizing and Associative Memory, 3rd Ed., Springer-Verlag, Berlin (1989)
Hecht-Nielsen R, Neurocomputing, Addison-Wesley, New York (1990)
Hopfield JJ, Proc. of the National Academy of Sciences., 79, 2554 (1982)
Elman JL, Cognitive Science., 14, 179 (1986)
Jordan MI, Proc. of the 8th Annual Conf. of the Cognitive Science Society, 531 (1986)
Widrow B, Hoff ME, Proc. 1960 IRE WESCON Convention Record, Part 4:IRE, 96, New York (1960)
Carpenter GA, Grossberg S, Computer, March 77 (1988)
Hollan JH, Adaptation in natural and artificial systems, University of Michigan Press, Ann Arbor (1975)
Goldberg D, Generic algorithms in search, optimization and machine learning, Addison-Wesley, New York (1989)