Advances on hybrid modelling for bioprocesses engineering: Insights into research trends and future directions from a bibliometric approach
Hybrid modeling in bioprocess engineering has emerged as a promising approach to strengthen process system engineering applications. However, understanding evolution of the field structure is a challenge. To address this gap, we conducted a comprehensive bibliometric analysis of the field. This stud...
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| Format: | Article |
| Language: | English |
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Elsevier
2024-12-01
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| Series: | Results in Engineering |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2590123024017912 |
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| _version_ | 1846115740284878848 |
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| author | Juan Federico Herrera-Ruiz Javier Fontalvo Oscar Andrés Prado-Rubio |
| author_facet | Juan Federico Herrera-Ruiz Javier Fontalvo Oscar Andrés Prado-Rubio |
| author_sort | Juan Federico Herrera-Ruiz |
| collection | DOAJ |
| description | Hybrid modeling in bioprocess engineering has emerged as a promising approach to strengthen process system engineering applications. However, understanding evolution of the field structure is a challenge. To address this gap, we conducted a comprehensive bibliometric analysis of the field. This study aims to assess publications metadata quantitatively and qualitatively to map the research landscape. Through a systematic review of Scopus and Web of Science databases, 360 contributions have been identified within chemical or biochemical engineering. Using Bibliometrix®, Tree of Science®, VantagePoint®, VOSViewer®, and Python, metadata was analyzed and visualized, revealing ''hybrid model'' and ''neural networks'' are the central keywords on the field, with notable contributions from countries like Portugal and the United States of America. Thematic analysis unveiled three clusters: one dealing with control applications and other two that combine machine learning terminology with bioprocesses concepts. Furthermore, the field exhibits a high level of collaboration, with leading researchers such as Rui Oliveira and Moritz von Stosch making significant contributions. Based on these findings, insights into the research trends and future directions are presented. |
| format | Article |
| id | doaj-art-b51d797876694d879e1709f42990b418 |
| institution | Kabale University |
| issn | 2590-1230 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Results in Engineering |
| spelling | doaj-art-b51d797876694d879e1709f42990b4182024-12-19T10:59:55ZengElsevierResults in Engineering2590-12302024-12-0124103548Advances on hybrid modelling for bioprocesses engineering: Insights into research trends and future directions from a bibliometric approachJuan Federico Herrera-Ruiz0Javier Fontalvo1Oscar Andrés Prado-Rubio2Grupo de Investigación en Aplicación de Nuevas Tecnologías (GIANT) Departamento de Ingeniería Química, Universidad Nacional de Colombia sede Manizales, Campus La Nubia, Manizales, 170003, ColombiaGrupo de Investigación en Aplicación de Nuevas Tecnologías (GIANT) Departamento de Ingeniería Química, Universidad Nacional de Colombia sede Manizales, Campus La Nubia, Manizales, 170003, ColombiaCorresponding author.; Grupo de Investigación en Aplicación de Nuevas Tecnologías (GIANT) Departamento de Ingeniería Química, Universidad Nacional de Colombia sede Manizales, Campus La Nubia, Manizales, 170003, ColombiaHybrid modeling in bioprocess engineering has emerged as a promising approach to strengthen process system engineering applications. However, understanding evolution of the field structure is a challenge. To address this gap, we conducted a comprehensive bibliometric analysis of the field. This study aims to assess publications metadata quantitatively and qualitatively to map the research landscape. Through a systematic review of Scopus and Web of Science databases, 360 contributions have been identified within chemical or biochemical engineering. Using Bibliometrix®, Tree of Science®, VantagePoint®, VOSViewer®, and Python, metadata was analyzed and visualized, revealing ''hybrid model'' and ''neural networks'' are the central keywords on the field, with notable contributions from countries like Portugal and the United States of America. Thematic analysis unveiled three clusters: one dealing with control applications and other two that combine machine learning terminology with bioprocesses concepts. Furthermore, the field exhibits a high level of collaboration, with leading researchers such as Rui Oliveira and Moritz von Stosch making significant contributions. Based on these findings, insights into the research trends and future directions are presented.http://www.sciencedirect.com/science/article/pii/S2590123024017912Hybrid modelingSemi-parametric modelingBibliometricsBioprocesses engineeringScopus, Web of Science (WoS) |
| spellingShingle | Juan Federico Herrera-Ruiz Javier Fontalvo Oscar Andrés Prado-Rubio Advances on hybrid modelling for bioprocesses engineering: Insights into research trends and future directions from a bibliometric approach Results in Engineering Hybrid modeling Semi-parametric modeling Bibliometrics Bioprocesses engineering Scopus, Web of Science (WoS) |
| title | Advances on hybrid modelling for bioprocesses engineering: Insights into research trends and future directions from a bibliometric approach |
| title_full | Advances on hybrid modelling for bioprocesses engineering: Insights into research trends and future directions from a bibliometric approach |
| title_fullStr | Advances on hybrid modelling for bioprocesses engineering: Insights into research trends and future directions from a bibliometric approach |
| title_full_unstemmed | Advances on hybrid modelling for bioprocesses engineering: Insights into research trends and future directions from a bibliometric approach |
| title_short | Advances on hybrid modelling for bioprocesses engineering: Insights into research trends and future directions from a bibliometric approach |
| title_sort | advances on hybrid modelling for bioprocesses engineering insights into research trends and future directions from a bibliometric approach |
| topic | Hybrid modeling Semi-parametric modeling Bibliometrics Bioprocesses engineering Scopus, Web of Science (WoS) |
| url | http://www.sciencedirect.com/science/article/pii/S2590123024017912 |
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