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

  • Zoran SekulićEmail author
  • Davor Antanasijević
  • Slavica Stevanović
  • Katarina Trivunac
Article
  • 94 Downloads

Abstract

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.

Keywords

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

Notes

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)

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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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