Advertisement

Support vector machine-based modeling of grafting hyperbranched polyethylene glycol on polyethersulfone ultrafiltration membrane for separation of oil–water emulsion

  • Hooman Adib
  • Ahmadreza RaisiEmail author
  • Behzad Salari
Article

Abstract

In present study, a new method based on the support vector machine (SVM) approach was employed to calculate the oil–water permeation flux and grafting yield of maleic anhydride and hyperbranched polyethylene glycol (PEG) on the polyethersulfone (PES) membrane surface. A set of 7 input/output experimental data was applied for training and testing the results of the model. The results of the developed SVM model showed good compliance with the experimental data, as the value of correlation index for the developed model is 0.96, 0.95 and 0.98 for the maleic anhydride and hyperbranched PEG grafting and oil–water permeation flux, respectively. The model was also validated against the kinetic reaction of benzophenone formation on the PES membrane which derives from combination of benzophenone formation reactions and the Lambert–Beer law. Also, to validate the SVM results, the oil–water permeation flux and antifouling characteristics of the modified and neat membranes were investigated, and based on the results, the SVM can be considered as a strong modeling approach in membrane process technologies.

Keywords

Hyperbranched polyethylene glycol (PEG) Benzophenone Membrane fouling Support vector machine (SVM) 

List of symbols

Symbols

B

Offset

Cf

Feed oil concentration

Cp

Permeate oil concentration

J

Permeate flux

K

Kernel function

N

Number of data

W

Normal vector

W

Weight of permeate

X

Input parameter

Yi

Simulation value

\(\hat{Y}_{i}\)

Experimental value

Greeks

αi

Lagrangian multiplier

Ψ

Vector dot product

Γ

Regularization parameter

Ε

Precision threshold

\(\xi_{i}^{*}\)

Slack variable

σ2

Kernel parameter

Abbreviations

AAD

Average absolute deviation

ANN

Artificial neural networks

BP

Benzophenone

BPHS

Benzophenone formation rate

DMF

N,N-dimethyl formamide

COD

Chemical oxygen demand

FIS

Fuzzy inference system

GA

Genetic algorithm

MAE

Mean absolute error

MF

Microfiltration

NF

Nanofiltration

PEG

Polyethylene glycol

PES

Polyethersulfone

R2

Coefficient of determination

UF

Ultrafiltration

Notes

References

  1. 1.
    M. Padaki, R.S. Murali, M.S. Abdullah, N. Misdan, A. Moslehyani, M.A. Kassim, N. Hilal, A.F. Ismai, Desalination 357, 197 (2015)CrossRefGoogle Scholar
  2. 2.
    H. Huang, J. Sheng, F. Qian, F. Zhou, S. Gao, X. Shen, Res. Chem. Intermed. 45, 533 (2019)CrossRefGoogle Scholar
  3. 3.
    J. Ju, M. Cheng, F. Shi, NPG Asia Mater. 6, 111 (2014)CrossRefGoogle Scholar
  4. 4.
    G. Ju, J. Liu, D. Li, M. Cheng, F. Shi, Langmuir 33, 2477 (2017)CrossRefGoogle Scholar
  5. 5.
    J. Jiang, Q. Zhang, X. Zhan, F. Chen, A.C.S. Sustain, Chem. Eng. 5, 9527 (2017)Google Scholar
  6. 6.
    B. Chen, G. Ju, E. Sakai, J. Qiu, RSC Adv. 5, 87055 (2015)CrossRefGoogle Scholar
  7. 7.
    H. Abadikhah, F. Zokaee Ashtiani, A. Fouladi, Desalination Water Treat. 56, 2783 (2015)Google Scholar
  8. 8.
    X. Zhu, A. Dudchenko, X. Gu, D. Jassby, J. Membr. Sci. 529, 159 (2017)CrossRefGoogle Scholar
  9. 9.
    H. Adib, S. Hassanajili, M.R. Sheikhi-Kouhsar, A. Salahi, T. Mohammadi, Korean J. Chem. Eng. 32, 159 (2015)CrossRefGoogle Scholar
  10. 10.
    M. Malmali, J. Askegaard, K. Sardari, S. Eswaranandam, A. Sengupta, S. Ranil, S.R. Wickramasingh, J. Water Process. Eng. 22, 218 (2018)CrossRefGoogle Scholar
  11. 11.
    K. Masoudnia, A.R. Raisi, A. Aroujaliana, M. Fathizadeh, Desalination Water Treat. 4, 901 (2014)Google Scholar
  12. 12.
    G. Zhang, F. Gao, Q. Zhang, X. Zhan, F. Chen, RSC Adv. 6, 7532 (2016)CrossRefGoogle Scholar
  13. 13.
    E. Karimi, A.R. Raisi, A. Aroujalian, Polymer 99, 642 (2016)CrossRefGoogle Scholar
  14. 14.
    K. Masoudnia, A. Raisi, A. Aroujalian, M. Fathizadeh, Sep. Sci. Technol. 48, 1544 (2013)CrossRefGoogle Scholar
  15. 15.
    Y. Yang, A. Raza, F. Banat, K. Wang, J. Water Process. Eng. 25, 113 (2018)CrossRefGoogle Scholar
  16. 16.
    Y. Fu, B. Jin, Q. Zhang, X. Zhan, F. Chen, A.C.S. Appl, Mater. Interfaces 9, 29387 (2017)CrossRefGoogle Scholar
  17. 17.
    I. Sadeghi, A. Aroujalian, A. Raisi, B. Dabir, M. Fathizadeh, J. Membr. Sci. 430, 24 (2013)CrossRefGoogle Scholar
  18. 18.
    C. Zhao, J. Xue, F. Ran, S. Sun, Prog. Mater Sci. 58, 76 (2013)CrossRefGoogle Scholar
  19. 19.
    A. Moradi, V. Mojarradi, M. Sarcheshmehpour, Res. Chem. Intermed. 39, 3235 (2013)CrossRefGoogle Scholar
  20. 20.
    V. Kochkodan, N. Hilal, Desalination 356, 187 (2015)CrossRefGoogle Scholar
  21. 21.
    W. Bhanthumnavin, P. Wanichapichart, W. Taweepreeda, S. Sirijarukula, B. Paosawatyanyong, Surf. Coat. Tech. 306, 272 (2016)CrossRefGoogle Scholar
  22. 22.
    L.A. Can-Herrera, A. Ávila-Ortega, S. de la Rosa-García, A.I. Oliva, J.V. Cauich-Rodríguez, J.M. Cervantes-Uc, Eur. Polym. J. 84, 502 (2016)CrossRefGoogle Scholar
  23. 23.
    T.S.M. Mui, R.P. Mota, A. Quade, L.R. de Oliveira Hein, K.G. Kostov, Surf. Coat. Tech. 352, 338 (2018)CrossRefGoogle Scholar
  24. 24.
    A. Vesel, J. Kovac, R. Zaplotnik, M. Modic, M. Mozetic, Appl. Surf. Sci. 357, 1325 (2015)CrossRefGoogle Scholar
  25. 25.
    C. Hoffmann, H. Silau, M. Pinelo, J.M. Woodley, A.E. Daugaard, Mater. Today Commun. 14, 160 (2018)CrossRefGoogle Scholar
  26. 26.
    S. Hou, J. Xing, X. Dong, J. Zheng, S. Li, J. Colloid Interface Sci. 500, 333 (2017)CrossRefGoogle Scholar
  27. 27.
    J. Zeng, Z. Dong, Z. Zhang, Y. Liu, J. Hazard. Mater. 333, 128 (2017)CrossRefGoogle Scholar
  28. 28.
    S. Afkham, A.R. Raisi, A. Aroujalian, Desalination Water Treat. 57, 26976 (2016)CrossRefGoogle Scholar
  29. 29.
    L. Bai, Y. Liu, A. Ding, N. Ren, G. Li, H. Liang, Chemosphere 217, 76 (2019)CrossRefGoogle Scholar
  30. 30.
    V. Moghimifar, A.R. Raisi, A. Aroujalian, J. Membr. Sci. 461, 69 (2014)CrossRefGoogle Scholar
  31. 31.
    W. Zhao, Y.L. Su, C. Li, Q. Shi, X. Ning, Z.Y. Jiang, J. Membr. Sci. 318, 405 (2008)CrossRefGoogle Scholar
  32. 32.
    L.P. Zhu, Z. Yi, F. Liu, X.Z. Wei, B.K. Zhu, Y.Y. Xu, Eur. Polym. J. 44, 1907 (2008)CrossRefGoogle Scholar
  33. 33.
    G. Zhang, J. Jiang, Q. Zhang, F. Gao, X. Zhan, F. Cheng, Langmuir 32, 1195 (2016)CrossRefGoogle Scholar
  34. 34.
    Q. Zhan, J. Jiang, F. Gao, G. Zhan, X. Zhan, F. Chen, Chem. Eng. J. 321, 412 (2017)CrossRefGoogle Scholar
  35. 35.
    G. Zhan, J. Jiang, Q. Zhan, X. Zhan, F. Chen, AIChE 63, 739 (2017)CrossRefGoogle Scholar
  36. 36.
    W. Xiuzhen, F. Yaowei, S. Yingying, C. Jinyuan, L. Bosheng, C. Yongsheng, H. Xiang, Polym. Adv. Technol. 27, 1569 (2016)CrossRefGoogle Scholar
  37. 37.
    S. Zulaikha, W. Lau, A.F. Ismail, J. Jaafar, J. Water Process. Eng. 2, 58 (2014)CrossRefGoogle Scholar
  38. 38.
    L.J. Mu, W.Z. Zhao, Appl. Surf. Sci. 225, 7273 (2009)CrossRefGoogle Scholar
  39. 39.
    Z.L. Xu, F.A. Qusay, J. Appl. Polym. Sci. 91, 3398 (2004)CrossRefGoogle Scholar
  40. 40.
    V.R. Pereira, A.M. Isloor, U.K. Bhat, A.F. Ismail, Desalination 351, 220 (2014)CrossRefGoogle Scholar
  41. 41.
    D.E. Bergbreiter, J.G. Franchina, K. Kabza, Macromolecules 32, 4993 (1999)CrossRefGoogle Scholar
  42. 42.
    T.M. Hwang, H. Oh, Y.K. Choung, S. Oh, M. Jeon, J.H. Kim, H.N. Nam, S. Lee, Desalination 247, 210 (2009)CrossRefGoogle Scholar
  43. 43.
    Q.F. Liu, S.H. Kim, S. Lee, Sep. Purif. Technol. 70, 96 (2009)CrossRefGoogle Scholar
  44. 44.
    S.S. Madaeni, A.R. Kurdian, Chem. Eng. Res. Des. 89, 456 (2011)CrossRefGoogle Scholar
  45. 45.
    H. Ma, R.H. Davis, C.N. Bowman, Macromolecules 33, 331 (2000)CrossRefGoogle Scholar
  46. 46.
    A.D. Eaton, L.S. Clesceri, A.E. Greenberg, M.A.H. Franson, Standard Methods for Examination of Water and Waste Water (The American Public Health Association, New York, 1995)Google Scholar
  47. 47.
    H. Adib, F. Sharifi, N. Mehranbod, N. Moradi, M. Koolivand, J. Nat. Gas Sci. Eng. 14, 121 (2013)CrossRefGoogle Scholar
  48. 48.
    V.N. Vapnik, Statistical Learning Theory (Wiley, New York, 1998)Google Scholar
  49. 49.
    M. Curilem, G. Acuna, F. Cubillos, E. Vyhmeister, Chem. Eng. Trans. 25, 761 (2011)Google Scholar
  50. 50.
    K. Pelckmans, J.A.K. Suykens, T. Van Gestel, D. De Brabanter, L. Lukas, B. Hamers, B. De Moor, J. Vandewalle, LS-SVMlab: A Matlab/C Toolbox for Least Squares Support Vector Mchines (Leu-ven, Leuven, 2002)Google Scholar
  51. 51.
    J.A.K. Suykens, T.V. Gestel, J.D. Brabanter, B.D. Moor, J. Vandewalle, Least Squares Support Vector Machines (World Scientific, Singapore, 2002)CrossRefGoogle Scholar
  52. 52.
    S. Zaidi, Chem. Eng. Res. Des. 98, 44 (2015)CrossRefGoogle Scholar
  53. 53.
    S. Agarwal, V.V. Saradhi, H. Karnick, Neurocomputing 71, 1230 (2008)CrossRefGoogle Scholar
  54. 54.
    Z. Li, X. Yang, W. Gu, H. Zhang, Appl. Math. Comput. 219, 8876 (2013)Google Scholar
  55. 55.
    L.H. Chiang, M.E. Kotanchek, A.K. Kordon, Comput. Chem. Eng. 28, 1389 (2004)CrossRefGoogle Scholar
  56. 56.
    X. Zhang, J. Zhou, C. Wang, C. Li, L. Song, Appl. Math. Comput. 218, 4973 (2012)Google Scholar
  57. 57.
    A. Eslamimanesh, F. Gharagheizib, F. Illbeigi, A.H. Mohammadi, A. Fazlali, D. Richon, Fluid Phase Equilib. 316, 34 (2012)CrossRefGoogle Scholar
  58. 58.
    A. Kulkarni, V.K. Jayaraman, B.D. Kulkarni, Comput. Chem. Eng. 29, 2128 (2005)CrossRefGoogle Scholar
  59. 59.
    J. Terzica, C.R. Nagarajahb, M. Alamgira, Sens. Actuators 161, 278 (2010)CrossRefGoogle Scholar
  60. 60.
    Y.C. Chen, C.T. Su, Appl. Math. Comput. 283, 141 (2016)Google Scholar
  61. 61.
    M. Moradi, G. Moussavi, Chem. Eng. J. 358, 1038 (2019)CrossRefGoogle Scholar

Copyright information

© Springer Nature B.V. 2019

Authors and Affiliations

  1. 1.Department of Chemical EngineeringAmirkabir University of Technology (Tehran Polytechnic)TehranIran
  2. 2.National Iranian Gas Company (NIGC), South Pars Gas Complex (SPGC)AsaluyehIran

Personalised recommendations