27 citations as recorded by crossref.

1. Adsorption of clofibric acid and ketoprofen onto powdered activated carbon: Effect of natural organic matter Y. Gao & M. Deshusses
2. Reactive membranes for groundwater remediation of chlorinated aliphatic hydrocarbons: Competitive dechlorination and cost aspects H. Wan et al.
3. Membrane assisted technology appraisal for water reuse applications in South Africa S. Sadr et al.
4. Investigation of polar mobile organic compounds (PMOC) removal by reverse osmosis and nanofiltration: rejection mechanism modelling using decision tree B. Teychene et al.
5. Evaluation of multivariate statistical analyses for monitoring and prediction of processes in an seawater reverse osmosis desalination plant S. Kolluri et al.
6. Prediction of Nanofiltration and Reverse-Osmosis-Membrane Rejection of Organic Compounds Using Random Forest Model S. Lee & J. Kim
7. Group Contribution Method to Predict the Mass Transfer Coefficients of Organics through Various RO Membranes R. Kibler et al.
8. Hydrogel surface modification of reverse osmosis membranes D. Nikolaeva et al.
9. 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. Stacked neural networks for predicting the membranes performance by treating the pharmaceutical active compounds Y. Ammi et al.
11. A Model Based on Bootstrapped Neural Networks for Modeling the Removal of Organic Compounds by Nanofiltration and Reverse Osmosis Membranes Y. Ammi et al.
12. Biophysical study of the non-steroidal anti-inflammatory drugs (NSAID) ibuprofen, naproxen and diclofenac with phosphatidylserine bilayer membranes M. Manrique-Moreno et al.
13. Predicting Micropollutant Removal by Reverse Osmosis and Nanofiltration Membranes: Is Machine Learning Viable? N. Jeong et al.
14. The role of electrostatic interactions on the rejection of organic solutes in aqueous solutions with nanofiltration A. Verliefde et al.
15. A holistic framework for improving the prediction of reverse osmosis membrane performance using machine learning A. Mohammed et al.
16. A QSAR (quantitative structure-activity relationship) approach for modelling and prediction of rejection of emerging contaminants by NF membranes V. Yangali-Quintanilla et al.
17. Modeling the Retention of Organic Compounds by Nanofiltration and Reverse Osmosis Membranes Using Bootstrap Aggregated Neural Networks L. Khaouane et al.
18. Prediction of the rejection of organic compounds (neutral and ionic) by nanofiltration and reverse osmosis membranes using neural networks Y. Ammi et al.
19. Nanofiltration and reverse osmosis technologies for disinfection by-product removal L. Wang et al.
20. 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.
21. Development of a predictive model to determine micropollutant removal using granular activated carbon D. de Ridder et al.
22. Concentration of Diclofenac Sodium Using the Nanofiltration Combined with Laccase Degradation from Trametes Versicolor R. Haouche et al.
23. 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.
24. Modeling equilibrium adsorption of organic micropollutants onto activated carbon D. de Ridder et al.
25. Customizing interpretable machine learning strategy to unravel causal mechanisms in organic micropollutant rejection by polyamide membranes H. Wang et al.
26. Quantification and modelling of organic micropollutant removal by reverse osmosis (RO) drinking water treatment S. Ebrahimzadeh et al.
27. Removal of Trace Organic Contaminants by Parallel Operation of Reverse Osmosis and Granular Activated Carbon for Drinking Water Treatment N. Konradt et al.