Support Vector Machines for Evaluating the Impact of the Forward Osmosis Membrane Characteristics on the Rejection of the Organic Molecules
The forward osmosis (FO) process is currently being studied more despite other energy-consuming processes. In addition, several works show the performance of FO membranes as its major challenges, the study of the rejection of different molecules, energy consumption, and modelling of different object...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Croatian Society of Chemical Engineers
2023-07-01
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| Series: | Kemija u Industriji |
| Subjects: | |
| Online Access: | http://silverstripe.fkit.hr/kui/assets/Uploads/1-417-431-KUI-7-8-2023.pdf |
| _version_ | 1797780817114562560 |
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| author | Fouad Kratbi Yamina Ammi Salah Hanini |
| author_facet | Fouad Kratbi Yamina Ammi Salah Hanini |
| author_sort | Fouad Kratbi |
| collection | DOAJ |
| description | The forward osmosis (FO) process is currently being studied more despite other energy-consuming processes. In addition, several works show the performance of FO membranes as its major challenges, the study of the rejection of different molecules, energy consumption, and modelling of different objectives related to this process. The main purpose of our study was to evaluate the impact of the FO membranes characteristics on the rejection of organic molecules (neutral) by modelling of the latter. However, the current work deals with the application of Support Vector Machines (SVM) for predicting the rejection of organic molecules (53) by the FO membranes. In addition, the SVM model was compared with two other models: Artificial Neural Network (ANN) and Multiple Linear Regression (MLR). The coefficient of correlation (R) for the testing data was applied to display the best SVM model. The SVM model generated with Radial Basis Function (RBF) as the kernel function showed the best R value equal to 0.8526. MLR and ANN models had R values of 0.7630 and 0.8723, respectively. |
| first_indexed | 2024-03-12T23:49:11Z |
| format | Article |
| id | doaj.art-40c8f8b6b8c14772a5eacc47c8e77d57 |
| institution | Directory Open Access Journal |
| issn | 0022-9830 1334-9090 |
| language | English |
| last_indexed | 2024-03-12T23:49:11Z |
| publishDate | 2023-07-01 |
| publisher | Croatian Society of Chemical Engineers |
| record_format | Article |
| series | Kemija u Industriji |
| spelling | doaj.art-40c8f8b6b8c14772a5eacc47c8e77d572023-07-13T19:13:48ZengCroatian Society of Chemical EngineersKemija u Industriji0022-98301334-90902023-07-01727-841743110.15255/KUI.2022.081Support Vector Machines for Evaluating the Impact of the Forward Osmosis Membrane Characteristics on the Rejection of the Organic MoleculesFouad Kratbi0Yamina Ammi1Salah Hanini2Laboratory of Biomaterials and Transport Phenomena (LBMPT), University of Médéa, AlgeriaLaboratory of Biomaterials and Transport Phenomena (LBMPT), University of Médéa, AlgeriaLaboratory of Biomaterials and Transport Phenomena (LBMPT), University of Médéa, AlgeriaThe forward osmosis (FO) process is currently being studied more despite other energy-consuming processes. In addition, several works show the performance of FO membranes as its major challenges, the study of the rejection of different molecules, energy consumption, and modelling of different objectives related to this process. The main purpose of our study was to evaluate the impact of the FO membranes characteristics on the rejection of organic molecules (neutral) by modelling of the latter. However, the current work deals with the application of Support Vector Machines (SVM) for predicting the rejection of organic molecules (53) by the FO membranes. In addition, the SVM model was compared with two other models: Artificial Neural Network (ANN) and Multiple Linear Regression (MLR). The coefficient of correlation (R) for the testing data was applied to display the best SVM model. The SVM model generated with Radial Basis Function (RBF) as the kernel function showed the best R value equal to 0.8526. MLR and ANN models had R values of 0.7630 and 0.8723, respectively.http://silverstripe.fkit.hr/kui/assets/Uploads/1-417-431-KUI-7-8-2023.pdfsupport vector machinesforward osmosismembranesrejectionorganic molecules |
| spellingShingle | Fouad Kratbi Yamina Ammi Salah Hanini Support Vector Machines for Evaluating the Impact of the Forward Osmosis Membrane Characteristics on the Rejection of the Organic Molecules Kemija u Industriji support vector machines forward osmosis membranes rejection organic molecules |
| title | Support Vector Machines for Evaluating the Impact of the Forward Osmosis Membrane Characteristics on the Rejection of the Organic Molecules |
| title_full | Support Vector Machines for Evaluating the Impact of the Forward Osmosis Membrane Characteristics on the Rejection of the Organic Molecules |
| title_fullStr | Support Vector Machines for Evaluating the Impact of the Forward Osmosis Membrane Characteristics on the Rejection of the Organic Molecules |
| title_full_unstemmed | Support Vector Machines for Evaluating the Impact of the Forward Osmosis Membrane Characteristics on the Rejection of the Organic Molecules |
| title_short | Support Vector Machines for Evaluating the Impact of the Forward Osmosis Membrane Characteristics on the Rejection of the Organic Molecules |
| title_sort | support vector machines for evaluating the impact of the forward osmosis membrane characteristics on the rejection of the organic molecules |
| topic | support vector machines forward osmosis membranes rejection organic molecules |
| url | http://silverstripe.fkit.hr/kui/assets/Uploads/1-417-431-KUI-7-8-2023.pdf |
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