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권영남

Kwon, Young-Nam
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dc.citation.startPage 116221 -
dc.citation.title DESALINATION -
dc.citation.volume 546 -
dc.contributor.author Ray, Saikat Sinha -
dc.contributor.author Verma, Rohit Kumar -
dc.contributor.author Singh, Ashutosh -
dc.contributor.author Ganesapillai, Mahesh -
dc.contributor.author Kwon, Young-Nam -
dc.date.accessioned 2023-12-21T13:10:42Z -
dc.date.available 2023-12-21T13:10:42Z -
dc.date.created 2022-11-10 -
dc.date.issued 2023-01 -
dc.description.abstract In the modern era, deep learning (DL), and machine learning (ML), have emerged as potential technologies that are widely applied in the fields of science, engineering, and technology. These tools have been extensively used to optimize seawater desalination and water treatment processes to achieve efficient performance. Indeed, automation has played a key role in redefining the issues of water treatment and seawater desalination. Artificial intelligence (AI) has been developed as a versatile tool for processing data and optimizing smart water services while addressing the issues of monitoring, management, and labor costs. Recently, specific AI tools, such as artificial neural networks (ANNs) and genetic algorithms, have been implemented for self-monitoring and modeling applications in the field of water treatment and seawater desalination. In the present article, the application of AI in the water treatment and seawater desalination sectors is thoroughly reviewed. Additionally, conventional modeling approaches are compared with ANN modeling. Furthermore, the challenges and shortcomings are discussed, along with future prospects. Moreover, the applications of AI mechanisms in data processing, optimization, modeling, prediction, and decision-making during water treatment and seawater desalination processes are underscored. Finally, innovative trends in seawater desalination and water treatment with AI tools are summarized. -
dc.identifier.bibliographicCitation DESALINATION, v.546, pp.116221 -
dc.identifier.doi 10.1016/j.desal.2022.116221 -
dc.identifier.issn 0011-9164 -
dc.identifier.scopusid 2-s2.0-85141539881 -
dc.identifier.uri https://scholarworks.unist.ac.kr/handle/201301/59986 -
dc.identifier.wosid 000900750300006 -
dc.language 영어 -
dc.publisher Elsevier BV -
dc.title A holistic review on how artificial intelligence has redefined water treatment and seawater desalination processes -
dc.type Article -
dc.description.isOpenAccess FALSE -
dc.relation.journalWebOfScienceCategory Engineering, Chemical;Water Resources -
dc.relation.journalResearchArea Engineering;Water Resources -
dc.type.docType Article -
dc.description.journalRegisteredClass scie -
dc.description.journalRegisteredClass scopus -
dc.subject.keywordAuthor Machine learning (ML) -
dc.subject.keywordAuthor Artificial neural networks (ANNs) -
dc.subject.keywordAuthor Desalination -
dc.subject.keywordAuthor Water treatment -
dc.subject.keywordAuthor Artificial intelligence (AI) -
dc.subject.keywordPlus NEURAL-NETWORK MODEL -
dc.subject.keywordPlus WASTE-WATER -
dc.subject.keywordPlus TREATMENT-PLANT -
dc.subject.keywordPlus OSMOSIS -
dc.subject.keywordPlus PERFORMANCE -
dc.subject.keywordPlus PREDICTION -
dc.subject.keywordPlus REMOVAL -
dc.subject.keywordPlus SYSTEMS -
dc.subject.keywordPlus OPTIMIZATION -
dc.subject.keywordPlus SIMULATION -

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