Advancing computational evaluation of adsorption via porous materials by artificial intelligence and computational fluid dynamics
Abstract A combination of artificial intelligence (AI) and computational fluid dynamics was carried out to advance the modeling of adsorption separation processes. A comparative examination of three AI-based regression models including Gaussian Process Regression (GPR), Multi-layer Perceptron (MLP),...
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Nature Portfolio
2025-08-01
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| Series: | Scientific Reports |
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| Online Access: | https://doi.org/10.1038/s41598-025-15538-z |
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| author | Heyder Mhohamdi Usama S. Altimari Krunal Vaghela V. Vivek Sarbeswara Hota Devendra Singh Mahesh Manchanda Shirin Shomurotova Prakhar Tomar Mohammad Mahtab Alam |
| author_facet | Heyder Mhohamdi Usama S. Altimari Krunal Vaghela V. Vivek Sarbeswara Hota Devendra Singh Mahesh Manchanda Shirin Shomurotova Prakhar Tomar Mohammad Mahtab Alam |
| author_sort | Heyder Mhohamdi |
| collection | DOAJ |
| description | Abstract A combination of artificial intelligence (AI) and computational fluid dynamics was carried out to advance the modeling of adsorption separation processes. A comparative examination of three AI-based regression models including Gaussian Process Regression (GPR), Multi-layer Perceptron (MLP), and Polynomial Regression (PR) was carried out to predict chemical concentrations of solute in a dataset with two input variables (x and y) and one output feature (C in mol/m3). Employing gradient-based hyperparameter optimization, the results reveal that MLP outperforms GPR and PR with a significantly higher R2 score (MLP: 0.999, GPR: 0.966, PR: 0.980) and lower RMSE (MLP: 0.583, GPR: 3.022, PR: 2.370). Moreover, MLP demonstrates the lowest Average Absolute Relative Deviation (AARD%) at 2.564%, compared to GPR’s 18.733% and PR’s 11.327%. Five-fold cross-validation confirms MLP’s reliability (R² = 0.998 ± 0.001, RMSE = 0.590 ± 0.015). These findings underscore the practical utility of machine learning models, especially MLP, for accurate chemical concentration in environmental monitoring and process optimization with particular application for adsorption process. |
| format | Article |
| id | doaj-art-bb236e40d2274878a6fc0e8ec0551451 |
| institution | DOAJ |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-08-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-bb236e40d2274878a6fc0e8ec05514512025-08-20T03:04:29ZengNature PortfolioScientific Reports2045-23222025-08-0115111010.1038/s41598-025-15538-zAdvancing computational evaluation of adsorption via porous materials by artificial intelligence and computational fluid dynamicsHeyder Mhohamdi0Usama S. Altimari1Krunal Vaghela2V. Vivek3Sarbeswara Hota4Devendra Singh5Mahesh Manchanda6Shirin Shomurotova7Prakhar Tomar8Mohammad Mahtab Alam9Department of Computers Techniques Engineering, College of Technical Engineering, The Islamic UniversityDepartment of Medical Laboratories Technology, AL-Nisour University CollegeDepartment of Computer Engineering, Faculty of Engineering & Technology, Marwadi University Research Center, Marwadi UniversityDepartment of Computer Science and Engineering, School of Engineering and Technology, JAIN (Deemed to be University)Department of Computer Application, Siksha ’O’ Anusandhan (Deemed to be University)Department of Computer Science & Engineering, Uttaranchal Institute of Technology, Uttaranchal UniversityComputer Science and Engineering, Graphic Era Hill UniversityDepartment of Chemistry Teaching Methods, Tashkent State Pedagogical University named after NizamiCentre for Research Impact & Outcome, Chitkara University Institute of Engineering and Technology, Chitkara UniversityDepartment of Basic Medical Sciences, College of Applied Medical Science, King Khalid UniversityAbstract A combination of artificial intelligence (AI) and computational fluid dynamics was carried out to advance the modeling of adsorption separation processes. A comparative examination of three AI-based regression models including Gaussian Process Regression (GPR), Multi-layer Perceptron (MLP), and Polynomial Regression (PR) was carried out to predict chemical concentrations of solute in a dataset with two input variables (x and y) and one output feature (C in mol/m3). Employing gradient-based hyperparameter optimization, the results reveal that MLP outperforms GPR and PR with a significantly higher R2 score (MLP: 0.999, GPR: 0.966, PR: 0.980) and lower RMSE (MLP: 0.583, GPR: 3.022, PR: 2.370). Moreover, MLP demonstrates the lowest Average Absolute Relative Deviation (AARD%) at 2.564%, compared to GPR’s 18.733% and PR’s 11.327%. Five-fold cross-validation confirms MLP’s reliability (R² = 0.998 ± 0.001, RMSE = 0.590 ± 0.015). These findings underscore the practical utility of machine learning models, especially MLP, for accurate chemical concentration in environmental monitoring and process optimization with particular application for adsorption process.https://doi.org/10.1038/s41598-025-15538-zAdsorptionPorous materialsSeparationMachine learningMLP model |
| spellingShingle | Heyder Mhohamdi Usama S. Altimari Krunal Vaghela V. Vivek Sarbeswara Hota Devendra Singh Mahesh Manchanda Shirin Shomurotova Prakhar Tomar Mohammad Mahtab Alam Advancing computational evaluation of adsorption via porous materials by artificial intelligence and computational fluid dynamics Scientific Reports Adsorption Porous materials Separation Machine learning MLP model |
| title | Advancing computational evaluation of adsorption via porous materials by artificial intelligence and computational fluid dynamics |
| title_full | Advancing computational evaluation of adsorption via porous materials by artificial intelligence and computational fluid dynamics |
| title_fullStr | Advancing computational evaluation of adsorption via porous materials by artificial intelligence and computational fluid dynamics |
| title_full_unstemmed | Advancing computational evaluation of adsorption via porous materials by artificial intelligence and computational fluid dynamics |
| title_short | Advancing computational evaluation of adsorption via porous materials by artificial intelligence and computational fluid dynamics |
| title_sort | advancing computational evaluation of adsorption via porous materials by artificial intelligence and computational fluid dynamics |
| topic | Adsorption Porous materials Separation Machine learning MLP model |
| url | https://doi.org/10.1038/s41598-025-15538-z |
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