Prediction of attachment efficiency using machine learning on a comprehensive database and its validation
DC Field | Value | Language |
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dc.contributor.author | Gomez-Flores, Allan | - |
dc.contributor.author | Bradford, Scott A. | - |
dc.contributor.author | Cai, Li | - |
dc.contributor.author | Urik, Martin | - |
dc.contributor.author | Kim, Hyunjung | - |
dc.date.accessioned | 2023-02-21T05:31:21Z | - |
dc.date.available | 2023-02-21T05:31:21Z | - |
dc.date.created | 2023-02-08 | - |
dc.date.issued | 2023-02 | - |
dc.identifier.issn | 0043-1354 | - |
dc.identifier.uri | https://scholarworks.bwise.kr/hanyang/handle/2021.sw.hanyang/182346 | - |
dc.description.abstract | Colloidal particles can attach to surfaces during transport, but the attachment depends on particle size, hydro-dynamics, solid and water chemistry, and particulate matter. The attachment is quantified in filtration theory by measuring attachment or sticking efficiency (Alpha). A comprehensive Alpha database (2538 records) was built from experiments in the literature and used to develop a machine learning (ML) model to predict Alpha. The training (r-squared: 0.86) was performed using two random forests capable of handling missing data. A holdout dataset was used to validate the training (r-squared: 0.98), and the variable importance was explored for training and validation. Finally, an additional validation dataset was built from quartz crystal microbalance experiments using surface-modified polystyrene, poly (methyl methacrylate), and polyethylene. The experiments were per -formed in the absence or presence of humic acid. Full database regression (r-squared: 0.90) predicted Alpha for the additional validation with an r-squared of 0.23. Nevertheless, when the original database and the additional validation dataset were combined into a new database, both the training (r-squared: 0.95) and validation (r-squared: 0.70) increased. The developed ML model provides a data-driven prediction of Alpha over a big database and evaluates the significance of 22 input variables. | - |
dc.language | 영어 | - |
dc.language.iso | en | - |
dc.publisher | PERGAMON-ELSEVIER SCIENCE LTD | - |
dc.title | Prediction of attachment efficiency using machine learning on a comprehensive database and its validation | - |
dc.type | Article | - |
dc.contributor.affiliatedAuthor | Kim, Hyunjung | - |
dc.identifier.doi | 10.1016/j.watres.2022.119429 | - |
dc.identifier.scopusid | 2-s2.0-85145492091 | - |
dc.identifier.wosid | 000904168800004 | - |
dc.identifier.bibliographicCitation | WATER RESEARCH, v.229, pp.1 - 11 | - |
dc.relation.isPartOf | WATER RESEARCH | - |
dc.citation.title | WATER RESEARCH | - |
dc.citation.volume | 229 | - |
dc.citation.startPage | 1 | - |
dc.citation.endPage | 11 | - |
dc.type.rims | ART | - |
dc.type.docType | Article | - |
dc.description.journalClass | 1 | - |
dc.description.isOpenAccess | N | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
dc.relation.journalResearchArea | Engineering | - |
dc.relation.journalResearchArea | Environmental Sciences & Ecology | - |
dc.relation.journalResearchArea | Water Resources | - |
dc.relation.journalWebOfScienceCategory | Engineering, Environmental | - |
dc.relation.journalWebOfScienceCategory | Environmental Sciences | - |
dc.relation.journalWebOfScienceCategory | Water Resources | - |
dc.subject.keywordPlus | DISSOLVED ORGANIC-MATTER | - |
dc.subject.keywordPlus | SATURATED POROUS-MEDIA | - |
dc.subject.keywordPlus | ENGINEERED NANOPARTICLES | - |
dc.subject.keywordPlus | SOLUTION CHEMISTRY | - |
dc.subject.keywordPlus | TRANSPORT | - |
dc.subject.keywordPlus | FATE | - |
dc.subject.keywordPlus | DEPOSITION | - |
dc.subject.keywordPlus | MODEL | - |
dc.subject.keywordPlus | NANOMATERIALS | - |
dc.subject.keywordPlus | BEHAVIOR | - |
dc.subject.keywordAuthor | Attachment efficiency | - |
dc.subject.keywordAuthor | Machine learning | - |
dc.subject.keywordAuthor | Missing data | - |
dc.subject.keywordAuthor | Colloid deposition | - |
dc.identifier.url | https://linkinghub.elsevier.com/retrieve/pii/S0043135422013744 | - |
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