Articles | Volume 1, issue 1
https://doi.org/10.5194/dwes-1-7-2008
© Author(s) 2008. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
https://doi.org/10.5194/dwes-1-7-2008
© Author(s) 2008. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
17 Jul 2008
Modeling of RO/NF membrane rejections of PhACs and organic compounds: a statistical analysis
V. Yangali-Quintanilla
UNESCO-IHE Institute for Water Education, Westvest 7, 2611 AX Delft, The Netherlands
Delft University of Technology, Stevinweg 1, Delft, The Netherlands
T.-U. Kim
Pennsylvania State University at Harrisburg, Middletown, PA 17057, USA
M. Kennedy
UNESCO-IHE Institute for Water Education, Westvest 7, 2611 AX Delft, The Netherlands
G. Amy
UNESCO-IHE Institute for Water Education, Westvest 7, 2611 AX Delft, The Netherlands
Delft University of Technology, Stevinweg 1, Delft, The Netherlands
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26 citations as recorded by crossref.
- Reactive membranes for groundwater remediation of chlorinated aliphatic hydrocarbons: Competitive dechlorination and cost aspects H. Wan et al. 10.1016/j.seppur.2023.123955
- Membrane assisted technology appraisal for water reuse applications in South Africa S. Sadr et al. 10.1080/1573062X.2014.994008
- Investigation of polar mobile organic compounds (PMOC) removal by reverse osmosis and nanofiltration: rejection mechanism modelling using decision tree B. Teychene et al. 10.2166/ws.2020.020
- Evaluation of multivariate statistical analyses for monitoring and prediction of processes in an seawater reverse osmosis desalination plant S. Kolluri et al. 10.1007/s11814-014-0356-0
- Prediction of Nanofiltration and Reverse-Osmosis-Membrane Rejection of Organic Compounds Using Random Forest Model S. Lee & J. Kim 10.1061/(ASCE)EE.1943-7870.0001806
- Group Contribution Method to Predict the Mass Transfer Coefficients of Organics through Various RO Membranes R. Kibler et al. 10.1021/acs.est.9b06170
- Hydrogel surface modification of reverse osmosis membranes D. Nikolaeva et al. 10.1016/j.memsci.2014.11.051
- An artificial intelligence approach for modeling the rejection of anti-inflammatory drugs by nanofiltration and reverse osmosis membranes using kernel support vector machine and neural networks Y. Ammi et al. 10.5802/crchim.76
- Stacked neural networks for predicting the membranes performance by treating the pharmaceutical active compounds Y. Ammi et al. 10.1007/s00521-021-05876-0
- A Model Based on Bootstrapped Neural Networks for Modeling the Removal of Organic Compounds by Nanofiltration and Reverse Osmosis Membranes Y. Ammi et al. 10.1007/s13369-018-3484-8
- Biophysical study of the non-steroidal anti-inflammatory drugs (NSAID) ibuprofen, naproxen and diclofenac with phosphatidylserine bilayer membranes M. Manrique-Moreno et al. 10.1016/j.bbamem.2016.06.009
- Predicting Micropollutant Removal by Reverse Osmosis and Nanofiltration Membranes: Is Machine Learning Viable? N. Jeong et al. 10.1021/acs.est.1c04041
- The role of electrostatic interactions on the rejection of organic solutes in aqueous solutions with nanofiltration A. Verliefde et al. 10.1016/j.memsci.2008.05.022
- A holistic framework for improving the prediction of reverse osmosis membrane performance using machine learning A. Mohammed et al. 10.1016/j.desal.2023.117253
- A QSAR (quantitative structure-activity relationship) approach for modelling and prediction of rejection of emerging contaminants by NF membranes V. Yangali-Quintanilla et al. 10.5004/dwt.2010.987
- Modeling the Retention of Organic Compounds by Nanofiltration and Reverse Osmosis Membranes Using Bootstrap Aggregated Neural Networks L. Khaouane et al. 10.1007/s13369-016-2320-2
- Prediction of the rejection of organic compounds (neutral and ionic) by nanofiltration and reverse osmosis membranes using neural networks Y. Ammi et al. 10.1007/s11814-015-0086-y
- Removal of organic micro-pollutants (phenol, aniline and nitrobenzene) via forward osmosis (FO) process: Evaluation of FO as an alternative method to reverse osmosis (RO) Y. Cui et al. 10.1016/j.watres.2016.01.001
- Development of a predictive model to determine micropollutant removal using granular activated carbon D. de Ridder et al. 10.5194/dwes-2-57-2009
- Concentration of Diclofenac Sodium Using the Nanofiltration Combined with Laccase Degradation from Trametes Versicolor R. Haouche et al. 10.1007/s13369-018-3371-3
- Artificial neural network models based on QSAR for predicting rejection of neutral organic compounds by polyamide nanofiltration and reverse osmosis membranes V. Yangali-Quintanilla et al. 10.1016/j.memsci.2009.06.048
- Modeling equilibrium adsorption of organic micropollutants onto activated carbon D. de Ridder et al. 10.1016/j.watres.2010.02.034
- Quantification and modelling of organic micropollutant removal by reverse osmosis (RO) drinking water treatment S. Ebrahimzadeh et al. 10.1016/j.jwpe.2021.102164
- Removal of Trace Organic Contaminants by Parallel Operation of Reverse Osmosis and Granular Activated Carbon for Drinking Water Treatment N. Konradt et al. 10.3390/membranes11010033
- Adsorption of clofibric acid and ketoprofen onto powdered activated carbon: Effect of natural organic matter Y. Gao & M. Deshusses 10.1080/09593330.2011.554888
- Processing of pharmaceutical effluent condensate by nanofiltration and reverse osmosis membrane techniques Y. Ravikumar et al. 10.1016/j.jtice.2013.09.021
24 citations as recorded by crossref.
- Reactive membranes for groundwater remediation of chlorinated aliphatic hydrocarbons: Competitive dechlorination and cost aspects H. Wan et al. 10.1016/j.seppur.2023.123955
- Membrane assisted technology appraisal for water reuse applications in South Africa S. Sadr et al. 10.1080/1573062X.2014.994008
- Investigation of polar mobile organic compounds (PMOC) removal by reverse osmosis and nanofiltration: rejection mechanism modelling using decision tree B. Teychene et al. 10.2166/ws.2020.020
- Evaluation of multivariate statistical analyses for monitoring and prediction of processes in an seawater reverse osmosis desalination plant S. Kolluri et al. 10.1007/s11814-014-0356-0
- Prediction of Nanofiltration and Reverse-Osmosis-Membrane Rejection of Organic Compounds Using Random Forest Model S. Lee & J. Kim 10.1061/(ASCE)EE.1943-7870.0001806
- Group Contribution Method to Predict the Mass Transfer Coefficients of Organics through Various RO Membranes R. Kibler et al. 10.1021/acs.est.9b06170
- Hydrogel surface modification of reverse osmosis membranes D. Nikolaeva et al. 10.1016/j.memsci.2014.11.051
- An artificial intelligence approach for modeling the rejection of anti-inflammatory drugs by nanofiltration and reverse osmosis membranes using kernel support vector machine and neural networks Y. Ammi et al. 10.5802/crchim.76
- Stacked neural networks for predicting the membranes performance by treating the pharmaceutical active compounds Y. Ammi et al. 10.1007/s00521-021-05876-0
- A Model Based on Bootstrapped Neural Networks for Modeling the Removal of Organic Compounds by Nanofiltration and Reverse Osmosis Membranes Y. Ammi et al. 10.1007/s13369-018-3484-8
- Biophysical study of the non-steroidal anti-inflammatory drugs (NSAID) ibuprofen, naproxen and diclofenac with phosphatidylserine bilayer membranes M. Manrique-Moreno et al. 10.1016/j.bbamem.2016.06.009
- Predicting Micropollutant Removal by Reverse Osmosis and Nanofiltration Membranes: Is Machine Learning Viable? N. Jeong et al. 10.1021/acs.est.1c04041
- The role of electrostatic interactions on the rejection of organic solutes in aqueous solutions with nanofiltration A. Verliefde et al. 10.1016/j.memsci.2008.05.022
- A holistic framework for improving the prediction of reverse osmosis membrane performance using machine learning A. Mohammed et al. 10.1016/j.desal.2023.117253
- A QSAR (quantitative structure-activity relationship) approach for modelling and prediction of rejection of emerging contaminants by NF membranes V. Yangali-Quintanilla et al. 10.5004/dwt.2010.987
- Modeling the Retention of Organic Compounds by Nanofiltration and Reverse Osmosis Membranes Using Bootstrap Aggregated Neural Networks L. Khaouane et al. 10.1007/s13369-016-2320-2
- Prediction of the rejection of organic compounds (neutral and ionic) by nanofiltration and reverse osmosis membranes using neural networks Y. Ammi et al. 10.1007/s11814-015-0086-y
- Removal of organic micro-pollutants (phenol, aniline and nitrobenzene) via forward osmosis (FO) process: Evaluation of FO as an alternative method to reverse osmosis (RO) Y. Cui et al. 10.1016/j.watres.2016.01.001
- Development of a predictive model to determine micropollutant removal using granular activated carbon D. de Ridder et al. 10.5194/dwes-2-57-2009
- Concentration of Diclofenac Sodium Using the Nanofiltration Combined with Laccase Degradation from Trametes Versicolor R. Haouche et al. 10.1007/s13369-018-3371-3
- Artificial neural network models based on QSAR for predicting rejection of neutral organic compounds by polyamide nanofiltration and reverse osmosis membranes V. Yangali-Quintanilla et al. 10.1016/j.memsci.2009.06.048
- Modeling equilibrium adsorption of organic micropollutants onto activated carbon D. de Ridder et al. 10.1016/j.watres.2010.02.034
- Quantification and modelling of organic micropollutant removal by reverse osmosis (RO) drinking water treatment S. Ebrahimzadeh et al. 10.1016/j.jwpe.2021.102164
- Removal of Trace Organic Contaminants by Parallel Operation of Reverse Osmosis and Granular Activated Carbon for Drinking Water Treatment N. Konradt et al. 10.3390/membranes11010033
2 citations as recorded by crossref.
- Adsorption of clofibric acid and ketoprofen onto powdered activated carbon: Effect of natural organic matter Y. Gao & M. Deshusses 10.1080/09593330.2011.554888
- Processing of pharmaceutical effluent condensate by nanofiltration and reverse osmosis membrane techniques Y. Ravikumar et al. 10.1016/j.jtice.2013.09.021
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