Separation of organic molecules from water by design of membrane using mass transfer model analysis and computational machine learning
Abstract This work investigates the utilization of ensemble machine learning methods in forecasting the distribution of chemical concentrations in membrane separation system for removal of an impurity from water. Mass transfer was evaluated using CFD and machine learning performed numerical simulati...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-07-01
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| Series: | Scientific Reports |
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| Online Access: | https://doi.org/10.1038/s41598-025-09156-y |
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| author | Suranjana V. Mayani Hessan Mohammad Soumya V. Menon Rishabh Thakur Abdulqader Faris Abdulqader S. Supriya Prabhat Kumar Sahu Kamal Kant Joshi |
| author_facet | Suranjana V. Mayani Hessan Mohammad Soumya V. Menon Rishabh Thakur Abdulqader Faris Abdulqader S. Supriya Prabhat Kumar Sahu Kamal Kant Joshi |
| author_sort | Suranjana V. Mayani |
| collection | DOAJ |
| description | Abstract This work investigates the utilization of ensemble machine learning methods in forecasting the distribution of chemical concentrations in membrane separation system for removal of an impurity from water. Mass transfer was evaluated using CFD and machine learning performed numerical simulations. A membrane contactor was employed for the separation and mass transfer analysis for the removal of organic molecules from water. The process is simulated via computational fluid dynamics and machine learning. Utilizing a dataset of over 25,000 data points with r(m) and z(m) as inputs, four tree-based learning algorithms were employed: Decision Tree (DT), Extremely Randomized Trees (ET), Random Forest (RF), and Histogram-based Gradient Boosting Regression (HBGB). Hyper-parameter optimization was conducted using Successive Halving, a method aimed at efficiently allocating computational resources to optimize model performance. The ET model emerged as the top performer, with R² of 0.99674. The ET model exhibited a RMSE of 37.0212 mol/m³ and a MAE of 19.6784 mol/m³. The results emphasize the capability of ensemble machine learning techniques to accurately estimate solute concentration profiles in membrane engineering applications. |
| format | Article |
| id | doaj-art-6a832dfbf5544e9cbd44637aab1cf1cb |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-6a832dfbf5544e9cbd44637aab1cf1cb2025-08-20T04:01:35ZengNature PortfolioScientific Reports2045-23222025-07-0115111210.1038/s41598-025-09156-ySeparation of organic molecules from water by design of membrane using mass transfer model analysis and computational machine learningSuranjana V. Mayani0Hessan Mohammad1Soumya V. Menon2Rishabh Thakur3Abdulqader Faris Abdulqader4S. Supriya5Prabhat Kumar Sahu6Kamal Kant Joshi7Department of Chemistry, Faculty of Science, Marwadi University Research Center, Marwadi UniversityDepartment of Computers Techniques Engineering, College of Technical Engineering, The Islamic UniversityDepartment of Chemistry and Biochemistry, School of Sciences, JAIN (Deemed to be University)Centre for Research Impact & Outcome, Chitkara University Institute of Engineering and Technology, Chitkara UniversityCollege of Pharmacy, Alnoor UniversityDepartment of Chemistry, Sathyabama Institute of Science and TechnologyDepartment of Computer Science and Information Technology, Siksha ’O’ Anusandhan (Deemed to be University)Department of Allied Science, Graphic Era Hill UniversityAbstract This work investigates the utilization of ensemble machine learning methods in forecasting the distribution of chemical concentrations in membrane separation system for removal of an impurity from water. Mass transfer was evaluated using CFD and machine learning performed numerical simulations. A membrane contactor was employed for the separation and mass transfer analysis for the removal of organic molecules from water. The process is simulated via computational fluid dynamics and machine learning. Utilizing a dataset of over 25,000 data points with r(m) and z(m) as inputs, four tree-based learning algorithms were employed: Decision Tree (DT), Extremely Randomized Trees (ET), Random Forest (RF), and Histogram-based Gradient Boosting Regression (HBGB). Hyper-parameter optimization was conducted using Successive Halving, a method aimed at efficiently allocating computational resources to optimize model performance. The ET model emerged as the top performer, with R² of 0.99674. The ET model exhibited a RMSE of 37.0212 mol/m³ and a MAE of 19.6784 mol/m³. The results emphasize the capability of ensemble machine learning techniques to accurately estimate solute concentration profiles in membrane engineering applications.https://doi.org/10.1038/s41598-025-09156-yNumerical simulationRemovalCFDModelMachine learningMass transfer |
| spellingShingle | Suranjana V. Mayani Hessan Mohammad Soumya V. Menon Rishabh Thakur Abdulqader Faris Abdulqader S. Supriya Prabhat Kumar Sahu Kamal Kant Joshi Separation of organic molecules from water by design of membrane using mass transfer model analysis and computational machine learning Scientific Reports Numerical simulation Removal CFD Model Machine learning Mass transfer |
| title | Separation of organic molecules from water by design of membrane using mass transfer model analysis and computational machine learning |
| title_full | Separation of organic molecules from water by design of membrane using mass transfer model analysis and computational machine learning |
| title_fullStr | Separation of organic molecules from water by design of membrane using mass transfer model analysis and computational machine learning |
| title_full_unstemmed | Separation of organic molecules from water by design of membrane using mass transfer model analysis and computational machine learning |
| title_short | Separation of organic molecules from water by design of membrane using mass transfer model analysis and computational machine learning |
| title_sort | separation of organic molecules from water by design of membrane using mass transfer model analysis and computational machine learning |
| topic | Numerical simulation Removal CFD Model Machine learning Mass transfer |
| url | https://doi.org/10.1038/s41598-025-09156-y |
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