Machine learning-driven identification and predictive mapping of clogging regimes in porous media
<p>Clogging in porous media critically limits the performance of subsurface and filtration systems, yet conventional models often rely on oversimplified, single-parameter thresholds to predict its behavior. This study develops a unified, machine learning–based framework to identify, characteri...
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| مؤلفون آخرون: | , , |
| منشور في: |
2025
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| _version_ | 1864513541025824768 |
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| author | Ahmed Elrahmani (17128837) |
| author2 | Riyadh I. Al-Raoush (2366107) Harris Sajjad Rabbani (14489205) Thomas D. Seers (8759187) |
| author2_role | author author author |
| author_facet | Ahmed Elrahmani (17128837) Riyadh I. Al-Raoush (2366107) Harris Sajjad Rabbani (14489205) Thomas D. Seers (8759187) |
| author_role | author |
| dc.creator.none.fl_str_mv | Ahmed Elrahmani (17128837) Riyadh I. Al-Raoush (2366107) Harris Sajjad Rabbani (14489205) Thomas D. Seers (8759187) |
| dc.date.none.fl_str_mv | 2025-08-20T15:00:00Z |
| dc.identifier.none.fl_str_mv | 10.1016/j.jhydrol.2025.134106 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/journal_contribution/Machine_learning-driven_identification_and_predictive_mapping_of_clogging_regimes_in_porous_media/30018754 |
| dc.rights.none.fl_str_mv | CC BY 4.0 info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Engineering Chemical engineering Environmental engineering Information and computing sciences Machine learning Fine particle migration Permeability reduction Clogging regimes Machine learning Dimensionless analysis Predictive mapping |
| dc.title.none.fl_str_mv | Machine learning-driven identification and predictive mapping of clogging regimes in porous media |
| dc.type.none.fl_str_mv | Text Journal contribution info:eu-repo/semantics/publishedVersion text contribution to journal |
| description | <p>Clogging in porous media critically limits the performance of subsurface and filtration systems, yet conventional models often rely on oversimplified, single-parameter thresholds to predict its behavior. This study develops a unified, machine learning–based framework to identify, characterize, and predict clogging behavior using dimensionless parameters representing pore structure, hydrodynamics, and particle–surface interactions. A total of 2,500 pore-scale realizations, generated with a pre-trained model informed by CFD-DEM simulations, were analyzed using four key metrics: permeability reduction, clogged fraction of throats, clogging zone length, and critical throat size of clogging. Three distinct clogging regimes emerged statistically (namely, surface, deep distributed, and sparse) each with its distinguished features. The framework further introduces high-resolution Phase Diagram and Clogging Diagnostic Maps that link input conditions to spatial clogging patterns and severity. These tools provide a scalable, interpretable foundation for optimizing system performance in managed aquifer recharge, enhanced oil recovery, groundwater remediation, and filtration system design.</p><h2>Other Information</h2> <p> Published in: Journal of Hydrology<br> License: <a href="http://creativecommons.org/licenses/by/4.0/" target="_blank">http://creativecommons.org/licenses/by/4.0/</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.1016/j.jhydrol.2025.134106" target="_blank">https://dx.doi.org/10.1016/j.jhydrol.2025.134106</a></p> |
| eu_rights_str_mv | openAccess |
| id | Manara2_ef9ebdff7d9fd7fd8cd86b49284e512a |
| identifier_str_mv | 10.1016/j.jhydrol.2025.134106 |
| network_acronym_str | Manara2 |
| network_name_str | Manara2 |
| oai_identifier_str | oai:figshare.com:article/30018754 |
| publishDate | 2025 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | Machine learning-driven identification and predictive mapping of clogging regimes in porous mediaAhmed Elrahmani (17128837)Riyadh I. Al-Raoush (2366107)Harris Sajjad Rabbani (14489205)Thomas D. Seers (8759187)EngineeringChemical engineeringEnvironmental engineeringInformation and computing sciencesMachine learningFine particle migrationPermeability reductionClogging regimesMachine learningDimensionless analysisPredictive mapping<p>Clogging in porous media critically limits the performance of subsurface and filtration systems, yet conventional models often rely on oversimplified, single-parameter thresholds to predict its behavior. This study develops a unified, machine learning–based framework to identify, characterize, and predict clogging behavior using dimensionless parameters representing pore structure, hydrodynamics, and particle–surface interactions. A total of 2,500 pore-scale realizations, generated with a pre-trained model informed by CFD-DEM simulations, were analyzed using four key metrics: permeability reduction, clogged fraction of throats, clogging zone length, and critical throat size of clogging. Three distinct clogging regimes emerged statistically (namely, surface, deep distributed, and sparse) each with its distinguished features. The framework further introduces high-resolution Phase Diagram and Clogging Diagnostic Maps that link input conditions to spatial clogging patterns and severity. These tools provide a scalable, interpretable foundation for optimizing system performance in managed aquifer recharge, enhanced oil recovery, groundwater remediation, and filtration system design.</p><h2>Other Information</h2> <p> Published in: Journal of Hydrology<br> License: <a href="http://creativecommons.org/licenses/by/4.0/" target="_blank">http://creativecommons.org/licenses/by/4.0/</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.1016/j.jhydrol.2025.134106" target="_blank">https://dx.doi.org/10.1016/j.jhydrol.2025.134106</a></p>2025-08-20T15:00:00ZTextJournal contributioninfo:eu-repo/semantics/publishedVersiontextcontribution to journal10.1016/j.jhydrol.2025.134106https://figshare.com/articles/journal_contribution/Machine_learning-driven_identification_and_predictive_mapping_of_clogging_regimes_in_porous_media/30018754CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/300187542025-08-20T15:00:00Z |
| spellingShingle | Machine learning-driven identification and predictive mapping of clogging regimes in porous media Ahmed Elrahmani (17128837) Engineering Chemical engineering Environmental engineering Information and computing sciences Machine learning Fine particle migration Permeability reduction Clogging regimes Machine learning Dimensionless analysis Predictive mapping |
| status_str | publishedVersion |
| title | Machine learning-driven identification and predictive mapping of clogging regimes in porous media |
| title_full | Machine learning-driven identification and predictive mapping of clogging regimes in porous media |
| title_fullStr | Machine learning-driven identification and predictive mapping of clogging regimes in porous media |
| title_full_unstemmed | Machine learning-driven identification and predictive mapping of clogging regimes in porous media |
| title_short | Machine learning-driven identification and predictive mapping of clogging regimes in porous media |
| title_sort | Machine learning-driven identification and predictive mapping of clogging regimes in porous media |
| topic | Engineering Chemical engineering Environmental engineering Information and computing sciences Machine learning Fine particle migration Permeability reduction Clogging regimes Machine learning Dimensionless analysis Predictive mapping |