A deep dive into membrane distillation literature with data analysis, bibliometric methods, and machine learning
Membrane 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 carr...
| Autores: | , |
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| Tipo de recurso: | artículo |
| Fecha de publicación: | 2023 |
| País: | España |
| Institución: | Universidad Complutense de Madrid (UCM) |
| Repositorio: | Docta Complutense |
| Idioma: | inglés |
| OAI Identifier: | oai:docta.ucm.es:20.500.14352/102987 |
| Acceso en línea: | https://hdl.handle.net/20.500.14352/102987 |
| Access Level: | acceso abierto |
| Palabra clave: | 536 Bibliometrix Machine learning Membrane distillation Sentiment analysis Text mining Upset graph Venn diagram Word cloud Termodinámica 2213 Termodinámica |
| Sumario: | Membrane 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. |
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