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Received March 14, 2014
Accepted July 30, 2014
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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
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Experimental and computational investigation of polyacrylonitrile ultrafiltration membrane for industrial oily wastewater treatment
Hooman Adib1 2
Shadi Hassanajili1†
Mohammad Reza Sheikhi-Kouhsar1
Abdolhamid Salahi3
Toraj Mohammadi3
1Department of Chemical Engineering, School of Chemical and Petroleum Engineering, Shiraz University, Shiraz, Iran 2National Iranian Gas Company (NIGC), South Pars Gas Complex (SPGC), Asaluyeh, Iran 3Research Centre for Membrane Separation Processes (RCMSP), Faculty of Chemical Engineering, Iran University of Science and Technology (IUST), Narmak, Tehran 16846, Iran
ajili@shirazu.ac.ir
Korean Journal of Chemical Engineering, January 2015, 32(1), 159-167(9), 10.1007/s11814-014-0218-9
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Abstract
An experimental study on separation of industrial oil from oily wastewater has been done. A polyacrylonitrile membrane with a molecular weight cut-off (MWCO) of 20 kDa was used and an outlet wastewater of API unit of Tehran refinery was employed. The main purpose of this study was to develop a support vector machine model for permeation flux decline and fouling resistance in a cross-flow hydrophilic polyacrylonitrile membrane during ultrafiltration. The operating conditions which have been applied to develop a support vector machine model were transmembrane pressure (TMP), operating temperature, cross flow velocity (CFV), pH values of oily wastewater, permeation flux decline and fouling resistance. The testing results obtained by the support vector machine models are in very good agreement with experimental data. The calculated squared correlation coefficients for permeation flux decline and fouling resistance were both 0.99. Based on the results, the support vector machine proved to be a reliable accurate_x000D_
estimation method.
Keywords
References
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Srijaroonrat P, Julien E, Aurelle Y, J. Membr. Sci., 159(1-2), 11 (1999)
Bai H, Wang X, Zhou Y, Zhang L, J. Prog. Nat. Sci., 250, 3 (2012)
Mohammadi T, Kazemimoghadam M, Saadabadi M, Desalination, 157(1-3), 369 (2003)
Salahi A, Abbasi M, Mohammadi T, Desalination, 251(1-3), 153 (2010)
Yu B, Cong H, Zhao X, J. Prog. Nat. Sci., 22, 662 (2012)
Cheng C, Uhe J, Yang X, Wu Y, Li D, J. Prog. Nat. Sci., 22, 670 (2012)
Dave YG, Reddy AVR, Desalination, 282, 9 (2011)
Yang D, Zhang X, Yuan L, Hu J, J. Prog. Nat. Sci., 19, 1305 (2009)
Xu J, Feng XS, Chen PP, Gao CJ, J. Membr. Sci., 413, 62 (2012)
Hoek EMV, Allred J, Knoell T, Jeong BH, J. Membr. Sci., 314(1-2), 33 (2008)
Tu SC, Ravindran V, Pirbazari M, J. Membr. Sci., 265(1-2), 29 (2005)
Van der Bruggen B, Manttari M, Nystrom M, Sep. Purif. Technol., 63(2), 251 (2008)
Boerlage SFE, Kennedy MD, Bonne PAC, Galjaard G, Schippers JC, Desalination, 113(2-3), 231 (1997)
Yin N, Chen S, Ouyang Y, Tang L, Yang J, Wang H, J. Prog. Nat. Sci., 21, 472 (2011)
Ballo S, Liu M, Hou L, Chang J, J. Prog. Nat. Sci., 19, 873 (2009)
Gunalan S, Sivaraj R, Rajendran V, J. Prog. Nat. Sci., 22, 695 (2012)
Shokrkar H, Salahi A, Kasiri N, Mohammadi T, Chem. Eng. Res. Des., 90(6), 846 (2012)
Hwang TM, Oh H, Choung YK, Oh S, Jeon M, Kim JH, Nam SH, Lee S, Desalination, 247(1-3), 285 (2009)
Liu QF, Kim SH, Lee S, Sep. Purif. Technol., 70(1), 96 (2009)
Madaeni SS, Kurdian AR, Chem. Eng. Res. Des., 89(4A), 456 (2011)
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Mohammadi T, Esmaeelifar A, J. Membr. Sci., 254(1-2), 129 (2005)
Hearst MA, Dumais ST, Osman E, Platt J, Scholkopf B, IEEE Intell. Syst. Appl., 13, 18 (1998)
Schmidt M, Identifying speaker with support vector networks, In Interface 96 Proceedings, Sydney (1996)
Cristianini N, Taylor JS, An introduction to support vector machine (and other kernel-based learning methods), Cambridge Univ. Press, Cambridge (2000)
Vapnik VN, Statistical learning theory, Wiley, New York (1998)
Pontil M, Verri A, Neural Comput., 10, 955 (1998)
Eslamimanesh A, Gharagheizi F, Illbeigi M, Mohammadi AH, Fazlali A, Richon D, Fluid Phase Equilib., 316, 34 (2012)
Balabin RM, Lomakina EI, Phys. Chem. Chem. Phys., 13, 11710 (2011)
Suykens JAK, Van Gestel T, De Brabanter J, De Moor B, Vandewalle J, Least Squares Support Vector Machines, World Scientific, Singapore (2002)
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Vapnik VN, The Nature of Statistical Learning Theory, 2nd Ed. Springer, New York (1995)
Zhao CY, Zhang HX, Zhang XY, Liu MC, Hu ZD, Fan BT, Toxicol., 217, 105 (2006)
Peng X, Pattern Recog. Lett., 44, 2678 (2011)
Zanghirati G, Zanni L, Parallel Comput., 29, 535 (2003)
Terzica J, Nagarajahb CR, Alamgira M, Sens. Actuators, 161, 278 (2010)
Agarwal S, Saradhi VV, Karnick H, Neurocomputing., 71, 1230 (2008)
Strack R, Kecman V, Strack B, Li Q, Neurocomputing, 59, 101 (2013)
Li DC, Fang YH, Expert. Syst. Appl., 34, 2013 (2008)
Comak E, Arslan A, Expert. Syst. Appl., 35, 564 (2008)
Hwang JP, Park S, Kim E, Expert. Syst. Appl., 38, 8580 (2011)
Salooki MK, Abedini R, Adib H, Koolivand H, Sep. Purif. Technol., 1, 82 (2011)
Adib H, Haghbakhsh R, Saidi M, Takassi MA, Sharifi F, Koolivand M, Rahimpour MR, Keshtkari S, J. Nat. Gas Sci. Eng., 10, 14 (2013)
Haghbakhsh R, Adib H, Keshavarz P, Koolivand M, Keshtkari S, Thermochim. Acta, 551, 124 (2013)
