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A deep dive into membrane distillation literature with data analysis, bibliometric methods, and machine learning

dc.contributor.authorAytaç, Ersin
dc.contributor.authorKhayet Souhaimi, Mohamed
dc.date.accessioned2024-04-11T08:18:46Z
dc.date.available2024-04-11T08:18:46Z
dc.date.issued2023-02-19
dc.description2023 Acuerdos transformativos CRUE
dc.description.abstractMembrane distillation (MD) is a non-isothermal separation process applied mainly in desalination for the treatment of saline aqueous solutions including brines for distilled water production by different technological configurations. Various experimental and theoretical investigations have been carried out in practically all related MD fields. However, no research study has been conducted yet evaluating the MD literature with data analysis, bibliometric methods, and machine learning approaches. This study includes an in-depth review of MD published papers in refereed international journals. Interesting statistical and graphical information on MD is presented. By using different indexes of bibliometric analysis, significant papers, authors more active in MD research, and the corresponding institutions and countries that have contributed most to the progress of MD technology are presented together with the collaborations made between research groups. The most used MD configurations, combined separation processes and types of treated water are revealed with the most considered materials in MD membrane engineering. With text mining approaches, the most commonly used words, keywords, and trending topics are analyzed highlighting those MD aspects that merit further investigation helping MD advance towards its industrial implementation. Sentiment analysis of papers abstracts indicates that 75.3 % of authors have optimistic views on MD technology.
dc.description.departmentDepto. de Estructura de la Materia, Física Térmica y Electrónica
dc.description.facultyFac. de Ciencias Físicas
dc.description.refereedTRUE
dc.description.sponsorshipScientific and Technological Research Council of Turkey (TUBITAK)
dc.description.statuspub
dc.identifier.citationErsin Aytaç, Mohamed Khayet, A deep dive into membrane distillation literature with data analysis, bibliometric methods, and machine learning, Desalination, Volume 553, 2023, 116482, ISSN 0011-9164, https://doi.org/10.1016/j.desal.2023.116482.
dc.identifier.doi10.1016/j.desal.2023.116482
dc.identifier.essn1873-4464
dc.identifier.issn0011-9164
dc.identifier.officialurlhttps://www.sciencedirect.com/science/article/pii/S0011916423001145
dc.identifier.urihttps://hdl.handle.net/20.500.14352/102987
dc.journal.titleDesalination
dc.language.isoeng
dc.page.final116482-23
dc.page.initial116482-1
dc.publisherElsevier
dc.relation.projectID1059B191900618
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 Internationalen
dc.rights.accessRightsopen access
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subject.cdu536
dc.subject.keywordBibliometrix
dc.subject.keywordMachine learning
dc.subject.keywordMembrane distillation
dc.subject.keywordSentiment analysis
dc.subject.keywordText mining
dc.subject.keywordUpset graph
dc.subject.keywordVenn diagram
dc.subject.keywordWord cloud
dc.subject.ucmTermodinámica
dc.subject.unesco2213 Termodinámica
dc.titleA deep dive into membrane distillation literature with data analysis, bibliometric methods, and machine learning
dc.typejournal article
dc.type.hasVersionVoR
dc.volume.number553
dspace.entity.typePublication
relation.isAuthorOfPublication8e32e718-0959-4e6c-9e04-891d3d43d640
relation.isAuthorOfPublication.latestForDiscovery8e32e718-0959-4e6c-9e04-891d3d43d640

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