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 th...e developed model has excellent performance in flux prediction with R-2 of 0.9648.
Кључне речи:
Microfiltration / Heavy metals / Modeling of flux / Artificial neural network / Group method data handling
TY - JOUR
AU - Sekulić, Zoran
AU - Antanasijević, Davor
AU - Stevanović, Slavica
AU - Trivunac, Katarina
PY - 2019
UR - http://TechnoRep.tmf.bg.ac.rs/handle/123456789/4323
AB - 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 R-2 of 0.9648.
PB - Springer International Publishing Ag, Cham
T2 - Water Air and Soil Pollution
T1 - The Prediction of Heavy Metal Permeate Flux in Complexation-Microfiltration Process: Polynomial Neural Network Approach
IS - 1
VL - 230
DO - 10.1007/s11270-018-4072-y
UR - conv_5752
ER -
@article{
author = "Sekulić, Zoran and Antanasijević, Davor and Stevanović, Slavica and Trivunac, Katarina",
year = "2019",
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 R-2 of 0.9648.",
publisher = "Springer International Publishing Ag, Cham",
journal = "Water Air and Soil Pollution",
title = "The Prediction of Heavy Metal Permeate Flux in Complexation-Microfiltration Process: Polynomial Neural Network Approach",
number = "1",
volume = "230",
doi = "10.1007/s11270-018-4072-y",
url = "conv_5752"
}
Sekulić, Z., Antanasijević, D., Stevanović, S.,& Trivunac, K.. (2019). The Prediction of Heavy Metal Permeate Flux in Complexation-Microfiltration Process: Polynomial Neural Network Approach. in Water Air and Soil Pollution
Springer International Publishing Ag, Cham., 230(1).
https://doi.org/10.1007/s11270-018-4072-y
conv_5752
Sekulić Z, Antanasijević D, Stevanović S, Trivunac K. The Prediction of Heavy Metal Permeate Flux in Complexation-Microfiltration Process: Polynomial Neural Network Approach. in Water Air and Soil Pollution. 2019;230(1).
doi:10.1007/s11270-018-4072-y
conv_5752 .
Sekulić, Zoran, Antanasijević, Davor, Stevanović, Slavica, Trivunac, Katarina, "The Prediction of Heavy Metal Permeate Flux in Complexation-Microfiltration Process: Polynomial Neural Network Approach" in Water Air and Soil Pollution, 230, no. 1 (2019),
https://doi.org/10.1007/s11270-018-4072-y .,
conv_5752 .
