Application of artificial neural networks for estimating Cd, Zn, Pb removal efficiency from wastewater using complexation-microfiltration process
Само за регистроване кориснике
2017
Чланак у часопису (Објављена верзија)
Метаподаци
Приказ свих података о документуАпстракт
Complexation-microfiltration process for removal of heavy metal ions such as lead, cadmium and zinc from water had been investigated. Two soluble derivates of cellulose was selected as complexing agents. The dependence of the removal efficiency from the operating parameters (pH value, pressure, concentration of metal ion, concentration of complexing agent and type of counter ion) was established. Two approaches of preparation of input data and two different artificial neural network architectures, general regression neural network and back-propagation neural network have been used for modeling of experimental data. The extrapolation ability of selected architectures, i.e., the prediction of rejection coefficient with inputs beyond the calibration range of original model, was also determined. The predictions were successful, and after evaluation of performances, the models that were developed gave relatively good results of mean absolute percentage error from 4 to 14% and R-squared from... 0.717 to 0.852 for general regression neural network and from 0.897 to 0.955 for back-propagation neural network.
Кључне речи:
Back propagation / Heavy metals / Microfiltration / Modeling of rejection coefficientИзвор:
International Journal of Environmental Science and Technology, 2017, 14, 7, 1383-1396Издавач:
- Springer, New York
Финансирање / пројекти:
- Развој и примена метода и материјала за мониторинг нових загађујућих и токсичних органских материја и тешких метала (RS-MESTD-Basic Research (BR or ON)-172007)
DOI: 10.1007/s13762-017-1248-8
ISSN: 1735-1472
WoS: 000403068500002
Scopus: 2-s2.0-85020516875
Институција/група
Tehnološko-metalurški fakultetTY - JOUR AU - Sekulić, Zoran AU - Antanasijević, Davor AU - Stevanović, S. AU - Trivunac, Katarina PY - 2017 UR - http://TechnoRep.tmf.bg.ac.rs/handle/123456789/3722 AB - Complexation-microfiltration process for removal of heavy metal ions such as lead, cadmium and zinc from water had been investigated. Two soluble derivates of cellulose was selected as complexing agents. The dependence of the removal efficiency from the operating parameters (pH value, pressure, concentration of metal ion, concentration of complexing agent and type of counter ion) was established. Two approaches of preparation of input data and two different artificial neural network architectures, general regression neural network and back-propagation neural network have been used for modeling of experimental data. The extrapolation ability of selected architectures, i.e., the prediction of rejection coefficient with inputs beyond the calibration range of original model, was also determined. The predictions were successful, and after evaluation of performances, the models that were developed gave relatively good results of mean absolute percentage error from 4 to 14% and R-squared from 0.717 to 0.852 for general regression neural network and from 0.897 to 0.955 for back-propagation neural network. PB - Springer, New York T2 - International Journal of Environmental Science and Technology T1 - Application of artificial neural networks for estimating Cd, Zn, Pb removal efficiency from wastewater using complexation-microfiltration process EP - 1396 IS - 7 SP - 1383 VL - 14 DO - 10.1007/s13762-017-1248-8 ER -
@article{ author = "Sekulić, Zoran and Antanasijević, Davor and Stevanović, S. and Trivunac, Katarina", year = "2017", abstract = "Complexation-microfiltration process for removal of heavy metal ions such as lead, cadmium and zinc from water had been investigated. Two soluble derivates of cellulose was selected as complexing agents. The dependence of the removal efficiency from the operating parameters (pH value, pressure, concentration of metal ion, concentration of complexing agent and type of counter ion) was established. Two approaches of preparation of input data and two different artificial neural network architectures, general regression neural network and back-propagation neural network have been used for modeling of experimental data. The extrapolation ability of selected architectures, i.e., the prediction of rejection coefficient with inputs beyond the calibration range of original model, was also determined. The predictions were successful, and after evaluation of performances, the models that were developed gave relatively good results of mean absolute percentage error from 4 to 14% and R-squared from 0.717 to 0.852 for general regression neural network and from 0.897 to 0.955 for back-propagation neural network.", publisher = "Springer, New York", journal = "International Journal of Environmental Science and Technology", title = "Application of artificial neural networks for estimating Cd, Zn, Pb removal efficiency from wastewater using complexation-microfiltration process", pages = "1396-1383", number = "7", volume = "14", doi = "10.1007/s13762-017-1248-8" }
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. in International Journal of Environmental Science and Technology Springer, New York., 14(7), 1383-1396. https://doi.org/10.1007/s13762-017-1248-8
Sekulić Z, Antanasijević D, Stevanović S, Trivunac K. Application of artificial neural networks for estimating Cd, Zn, Pb removal efficiency from wastewater using complexation-microfiltration process. in International Journal of Environmental Science and Technology. 2017;14(7):1383-1396. doi:10.1007/s13762-017-1248-8 .
Sekulić, Zoran, Antanasijević, Davor, Stevanović, S., Trivunac, Katarina, "Application of artificial neural networks for estimating Cd, Zn, Pb removal efficiency from wastewater using complexation-microfiltration process" in International Journal of Environmental Science and Technology, 14, no. 7 (2017):1383-1396, https://doi.org/10.1007/s13762-017-1248-8 . .