The Prediction of Heavy Metal Permeate Flux in Complexation-Microfiltration Process: Polynomial Neural Network Approach

  • Zoran SekulićEmail author
  • Davor Antanasijević
  • Slavica Stevanović
  • Katarina Trivunac


Membrane filtration techniques are distinguished among methods for wastewater treatment and fully correspond to the requirements of the green concept of chemistry and production. The limiting factor for greater application of these methods is the phenomenon of fouling and the decline of the permeate flux. In this study, polynomial neural network based on group method data handling (GMDH) algorithm was applied to predict the performance of the complexation-microfiltration process for the removal of Pb(II), Zn(II), and Cd(II) from synthetic wastewater. The influence of working parameters such as pH, initial concentration of metal ions, type of complexing agent, and pressure on flux was experimentally determined. The data obtained were used as input parameters for the GMDH model as well as for the multiple linear regression (MLR) model. Root mean square error (RMSE), mean absolute error (MAE), and mean absolute percent error (MAPE) were used for evaluation purposes. Results showed that the developed model has excellent performance in flux prediction with R2 of 0.9648.


Microfiltration Heavy metals Modeling of flux Artificial neural network Group method data handling 


Funding Information

The authors are grateful to the Ministry of Education, Science and Technological Development of the Republic of Serbia, Project No. 172007 for financial support.

Supplementary material

11270_2018_4072_MOESM1_ESM.pdf (367 kb)
ESM 1 (PDF 366 kb)


  1. Akpor, O. B., Onolunose Ohiobor, G., & Olaolu, T. D. (2014). Heavy metal pollutants in wastewater effluents: sources, effects and remediation. Advances in Bioscience and Bioengineering, 2, 37–43.CrossRefGoogle Scholar
  2. Antanasijević, D., Antanasijević, J., Pocajt, V., & Ušćumlić, G. (2016). A GMDH-type neural network with multi-filter feature selection for the prediction of transition temperatures of bent-core liquid crystals. RSC Advances, 6, 99676–99684.CrossRefGoogle Scholar
  3. Badrnezhad, R., & Mirza, B. (2014). Modelling and optimization of cross-flow ultrafiltration using hybrid neural network-genetic algorithm approach. Journal of Industrial and Engineering Chemistry, 20, 528–543.CrossRefGoogle Scholar
  4. Barakat, M. A., & Schmidt, E. (2010). Polymer-enhanced ultrafiltration process for heavy metals removal from industrial wastewater. Desalination, 256, 90–93.CrossRefGoogle Scholar
  5. Camarillo, R., Llanos, J., García-Fernández, L., Pérez, Á., & Cañizares, P. (2010). Treatment of copper (II)-loaded aqueous nitrate solutions by polymer enhanced ultrafiltration and electrodeposition. Separation and Purification Technology, 70, 320–328.CrossRefGoogle Scholar
  6. Carolin, C. F., Kumar, P. S., Saravanan, A., Joshiba, G. J., & Naushad, M. (2017). Efficient techniques for the removal of toxic heavy metals from aquatic environment: a review. Journal of Environmental Chemical Engineering, 5, 2782–2799.CrossRefGoogle Scholar
  7. Chen, H., & Kim, A. S. (2006). Prediction of permeate flux decline in crossflow membrane filtration of colloidal suspension: a radial basis function neural network approach. Desalination, 192, 415–428.CrossRefGoogle Scholar
  8. Cheng, L. H., Cheng, Y. F., & Chen, J. (2008). Predicting effect of Interparticle interactions on permeate flux decline in CMF of colloidal suspensions: an overlapped type of local neural network. Journal of Membrane Science, 308, 54–65.CrossRefGoogle Scholar
  9. Chew, C. M., Aroua, M. K., & Hussain, M. A. (2017). A practical hybrid modelling approach for the prediction of potential fouling parameters in ultrafiltration membrane water treatment plant. Journal of Industrial and Engineering Chemistry, 45, 145–155.CrossRefGoogle Scholar
  10. Chew, C. M., Aroua, M. K., & Hussain, M. A. (2018). Advanced process control for ultrafiltration membrane water treatment system. Journal of Cleaner Production, 179, 63–80.CrossRefGoogle Scholar
  11. Choi, Y. J., Oh, H., Lee, S., Nam, S. H., & Hwang, T. M. (2012). Investigation of the filtration characteristics of pilot-scale hollow fiber submerged MF system using cake formation model and artificial neural networks model. Desalination, 297, 20–29.CrossRefGoogle Scholar
  12. Crini, G., Morin-Crini, N., Fatin-Rouge, N., Déon, S., & Fievet, P. (2017). Metal removal from aqueous media by polymer-assisted ultrafiltration with chitosan. Arabian Journal of Chemistry, 10, S3826–S3839.CrossRefGoogle Scholar
  13. Dasgupta, J., Sikder, J., & Mandal, D. (2017). Modelling and optimization of polymer enhanced ultrafiltration using hybrid neural-genetic algorithm based evolutionary approach. Applied Soft Computing, 55, 108–126.CrossRefGoogle Scholar
  14. Ennigrou, D. J., Ben Sik Ali, M., & Dhahbi, M. (2014). Copper and zinc removal from aqueous solutions by polyacrylic acid assisted-ultrafiltration. Desalination, 343, 82–87.CrossRefGoogle Scholar
  15. Farlow, S. J. (1981). The GMDH algorithm of Ivakhnenko. The American Statistician, 35, 210–215.Google Scholar
  16. Flora, G., Gupta, D., & Tiwari, A. (2012). Toxicity of lead: a review with recent updates. Interdisciplinary Toxicology, 5, 47–58.CrossRefGoogle Scholar
  17. Fu, F., & Wang, Q. (2011). Removal of heavy metal ions from wastewaters: a review. Journal of Environmental Management, 92, 407–418.CrossRefGoogle Scholar
  18. Gao, J., Qiu, Y., Hou, B., Zhang, Q., & Zhang, X. (2018). Treatment of wastewater containing nickel by complexation- ultrafiltration using sodium polyacrylate and the stability of PAA-Ni complex in the shear field. Chemical Engineering Journal, 334, 1878–1885.CrossRefGoogle Scholar
  19. Giwa, A., Daer, S., Ahmed, I., Marpu, P. R., & Hasan, S. W. (2016). Experimental investigation and artificial neural networks ANNs modelling of electrically-enhanced membrane bioreactor for wastewater treatment. Journal of Water Process Engineering, 11, 88–97.CrossRefGoogle Scholar
  20. Godt, J., Scheidig, F., Grosse-Siestrup, C., Esche, V., Brandenburg, P., Reich, A., & Groneberg, D. A. (2006). The toxicity of cadmium and resulting hazards for human health. Journal of Occupational Medicine and Toxicology, 1, 1–6.CrossRefGoogle Scholar
  21. Guo, W., Ngo, H. H., & Li, J. (2012). A mini-review on membrane fouling. Bioresource Technology, 122, 27–34.CrossRefGoogle Scholar
  22. Hankins, N., Hilal, N., Ogunbiyi, O. O., & Azzopardi, B. (2005). Inverted polarity micellar enhanced ultrafiltration for the treatment of heavy metal polluted wastewater. Desalination, 185, 185–202.CrossRefGoogle Scholar
  23. Huang, Y., Wu, D., Wang, X., Huang, W., Lawless, D., & Feng, X. (2016). Removal of heavy metals from water using polyvinylamine by polymer-enhanced ultrafiltration and flocculation. Separation and Purification Technology, 158, 124–136.CrossRefGoogle Scholar
  24. Huang, J., Yuan, F., Zeng, G., Li, X., Gu, Y., Shi, L., Liu, W., & Shi, Y. (2017). Influence of Ph on heavy metal speciation and removal from wastewater using micellar-enhanced ultrafiltration. Chemosphere, 173, 199–206.CrossRefGoogle Scholar
  25. Ivakhnenko, A. G., & Ivakhnenko, G. A. (1995). The review of problems solvable by algorithms of the group method of data handling (GMDH). Pattern Recognition and Image Analysis, 5(4), 527–535.Google Scholar
  26. Kalogirou, S. A. (2003). Artificial intelligence for the modeling and control of combustion processes: a review. Progress in Energy and Combustion Science, 29, 515–566.CrossRefGoogle Scholar
  27. Khosa, M. A., Shah, S. S., & Feng, X. (2014). Metal sericin complexation and ultrafiltration of heavy metals from aqueous solution. Chemical Engineering Journal, 244, 446–456.CrossRefGoogle Scholar
  28. Klimkiewicz, A., Cervera-Padrell, A. E., & Van Den Berg, F. (2016). Modeling of the flux decline in a continuous ultrafiltration system with multiblock partial least squares. Industrial and Engineering Chemistry Research, 55, 10690–10698.CrossRefGoogle Scholar
  29. Labanda, J., Khaidar, M. S., Sabaté, J., & Llorens, J. (2011). Study of Cr (III) desorption process from a water-soluble polymer by ultrafiltration. Desalination, 281, 165–171.CrossRefGoogle Scholar
  30. Lam, B., Déon, S., Morin-Crini, N., Crini, G., & Fievet, P. (2018). Polymer-enhanced ultrafiltration for heavy metal removal: influence of chitosan and carboxymethyl cellulose on filtration performances. Journal of Cleaner Production, 171, 927–933.CrossRefGoogle Scholar
  31. Landaburu-Aguirre, J., Pongrácz, E., & Keiski, R. L. (2011). Separation of cadmium and copper from phosphorous rich synthetic waters by micellar-enhanced ultrafiltration. Separation and Purification Technology, 81, 41–48.CrossRefGoogle Scholar
  32. Liu, Q. F., Kim, S. H., & Lee, S. (2009). Prediction of microfiltration membrane fouling using artificial neural network models. Separation and Purification Technology, 70, 96–102.CrossRefGoogle Scholar
  33. Molinari, R., & Argurio, P. (2017). Arsenic removal from water by coupling photocatalysis and complexation-ultrafiltration processes: a preliminary study. Water Research, 109, 327–336.CrossRefGoogle Scholar
  34. Moosavi, V., Talebi, A., & Hadian, M. R. (2017). Development of a Hybrid Wavelet Packet- Group Method of Data Handling (WPGMDH) Model for Runoff Forecasting. Water Resources Management, 31, 43–59.Google Scholar
  35. Moriasi, D. N., Arnold, J. G., Van Liew, M. W., Bingner, R. L., Harmel, R. D., & Veith, T. L. (2007). Model evaluation guidelines for systematic quantification of accuracy in watershed simulations. Transactions of the ASABE, 50, 885–900.CrossRefGoogle Scholar
  36. Nandi, B. K., Moparthi, A., Uppaluri, R., & Purkait, M. K. (2010). Treatment of oily wastewater using low cost ceramic membrane: comparative assessment of pore blocking and artificial neural network models. Chemical Engineering Research and Design, 88, 881–892.CrossRefGoogle Scholar
  37. Oh, S. K., & Pedrycz, W. (2002). The design of self-organizing polynomial neural networks. Information Sciences, 141, 237–258.CrossRefGoogle Scholar
  38. Oh, S. K., Pedrycz, W., & Park, B. J. (2003). Polynomial neural networks architecture: analysis and design. Computers and Electrical Engineering, 29, 703–725.CrossRefGoogle Scholar
  39. Palencia, M., Rivas, B. L., & Pereira, E. (2009). Metal ion recovery by polymer-enhanced ultrafiltration using poly (vinyl sulfonic acid): fouling description and membrane-metal ion interaction. Journal of Membrane Science, 345, 191–200.CrossRefGoogle Scholar
  40. Pao, H. T., Fu, H. C., & Tseng, C. L. (2012). Forecasting of CO2 emissions, energy consumption and economic growth in China using an improved Grey model. Energy, 40, 400–409.CrossRefGoogle Scholar
  41. Plum, L. M., Rink, L., & Haase, H. (2010). The essential toxin: impact of zinc on human health. International Journal of Environmental Research and Public Health, 7, 1342–1365.CrossRefGoogle Scholar
  42. Qiu, Y. R., Mao, L. J., & Wang, W. H. (2014). Removal of manganese from waste water by complexation-ultrafiltration using copolymer of maleic acid and acrylic acid. Transactions of Nonferrous Metals Society of China (English Edition), 24, 1196–1201.CrossRefGoogle Scholar
  43. Rahmanian, B., Pakizeh, M., Mansoori, S. A. A., & Abedini, R. (2011). Application of experimental design approach and artificial neural network (ANN) for the determination of potential micellar-enhanced ultrafiltration process. Journal of Hazardous Materials, 187, 67–74.CrossRefGoogle Scholar
  44. Sánchez, J., Espinosa, C., Pooch, F., Tenhu, H., Pizarro, G. d. C., & Oyarzún, D. P. (2018). Poly(N,N-dimethylaminoethyl methacrylate) for removing chromium (VI) through polymer-enhanced ultrafiltration technique. Reactive and Functional Polymers, 127, 67–73.CrossRefGoogle Scholar
  45. Schmitt, F., Banu, R., Yeom, I. T., & Do, K. U. (2018). Development of artificial neural networks to predict membrane fouling in an anoxic-aerobic membrane bioreactor treating domestic wastewater. Biochemical Engineering Journal, 133, 47–58.CrossRefGoogle Scholar
  46. Schwarze, M. (2017). Micellar-enhanced ultrafiltration (MEUF) – state of the art. Environmental Science: Water Research & Technology, 3, 598–624.Google Scholar
  47. Schwarze, M., Groß, M., Moritz, M., Buchner, G., Kapitzki, L., Chiappisi, L., & Gradzielski, M. (2015). Micellar enhanced ultrafiltration (MEUF) of metal cations with oleylethoxycarboxylate. Journal of Membrane Science, 478, 140–147.CrossRefGoogle Scholar
  48. Sekulić, Z., Antanasijević, D., Stevanović, S., & Trivunac, K. (2017). Application of artificial neural networks for estimating Cd, Zn, Pb removal efficiency from wastewater using complexation-microfiltration process. International journal of Environmental Science and Technology, 14, 1383–1396.CrossRefGoogle Scholar
  49. Shao, J., Qin, S., Davidson, J., Li, W., He, Y., & Zhou, H. S. (2013). Recovery of nickel from aqueous solutions by complexation-ultrafiltration process with sodium polyacrylate and polyethylenimine. Journal of Hazardous Materials, 244–245, 472–477.CrossRefGoogle Scholar
  50. Shi, X., Tal, G., Hankins, N. P., & Gitis, V. (2014). Fouling and cleaning of ultrafiltration membranes: a review. Journal of Water Process Engineering, 1, 121–138.CrossRefGoogle Scholar
  51. Šiljić Tomić, A., Antanasijević, D., Ristić, M., Perić-Grujić, A., & Pocajt, V. (2018). A linear and non-linear polynomial neural network modeling of dissolved oxygen content in surface water: inter- and extrapolation performance with inputs’ significance analysis. Science of the Total Environment, 610–611, 1038–1046.CrossRefGoogle Scholar
  52. Singh, K. P., Basant, A., Malik, A., & Jain, G. (2009). Artificial neural network modeling of the river water quality-a case study. Ecological Modelling, 220, 888–895.CrossRefGoogle Scholar
  53. Soleimani, R., Shoushtari, N. A., Mirza, B., & Salahi, A. (2013). Experimental investigation, modeling and optimization of membrane separation using artificial neural network and multi-objective optimization using genetic algorithm. Chemical Engineering Research and Design, 91, 883–903.CrossRefGoogle Scholar
  54. Tetko, I. V., Aksenova, T. I., Volkovich, V. V., Kasheva, T. N., Filipov, D. V., Welsh, W. J., Livingstone, D. J., & Villa, A. E. P. (2000). Polynomial neural network for linear and non-linear model selection in quantitative-structure activity relationship studies on the Internet. SAR and QSAR in Environmental Research, 11, 263–280.CrossRefGoogle Scholar
  55. Trivunac, K., Sekulić, Z., & Stevanović, S. (2012). Zinc removal from wastewater by a complexation-microfiltration process. Journal of the Serbian Chemical Society, 77, 1661–1670.CrossRefGoogle Scholar
  56. Willmott, C. J., Robeson, S. M., & Matsuura, K. (2012). Short communication a refined index of model performance. International Journal of Climatology, 32, 2088–2094.CrossRefGoogle Scholar
  57. Xi, X., Cui, Y., Wang, Z., Qian, J., Wang, J., Yang, L., & Zhao, S. (2011). Study of dead-end microfiltration features in sequencing batch reactor (SBR) by optimized neural networks. Desalination, 272, 27–35.CrossRefGoogle Scholar
  58. Yu, S., Zhang, X., Li, F., & Zhao, X. (2017). Influence of trace cobalt (II) on surfactant fouling of PVDF ultrafiltration membrane. Colloids and Surfaces A: Physicochemical and Engineering Aspects, 518, 130–138.CrossRefGoogle Scholar
  59. Yürüm, A., Taralp, A., Biçak, N., Özbelge, H. Ö., & Yilmaz, L. (2013). High performance ligands for the removal of aqueous boron species by continuous polymer enhanced ultrafiltration. Desalination, 320, 33–39.CrossRefGoogle Scholar
  60. Zeng, J., Ye, H., & Hu, Z. (2009). Application of the hybrid complexation-ultrafiltration process for metal ion removal from aqueous solutions. Journal of Hazardous Materials, 161, 1491–1498.CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Institute of Public Health of BelgradeBelgradeSerbia
  2. 2.Innovation Center of the Faculty of Technology and MetallurgyBelgradeSerbia
  3. 3.Faculty of Technology and MetallurgyUniversity of BelgradeBelgradeSerbia

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