Current scenario of machine learning applications to hydrothermal liquefaction via bibliometric analysis DOI Creative Commons
Tossapon Katongtung, Somboon Sukpancharoen,

Sakprayut Sinthupinyo

et al.

F1000Research, Journal Year: 2025, Volume and Issue: 13, P. 1131 - 1131

Published: March 11, 2025

Background Energy shortages and global warming have been significant issues throughout history. Therefore, the search for environmentally friendly renewable energy sources is crucial achieving sustainability. Biomass gaining attention as a option, particularly through process of hydrothermal liquefaction, which converts wet biomass into bio-crude oil. Methods Hydrothermal liquefaction complex that challenging to explain, leading research on machine learning models this process. These aim predict values investigate impact variables However, development in still limited due its novelty time required comprehensive study. Thus, objective study was analyze relevant publications Scopus database, focusing indexed ML HTL keywords, understand keyword associations co-citations. Results The results reveal an increasing trend process, with growing interest from various countries. Conclusion Notably, China currently holds largest share processes, most published works falling within field engineering. “liquefaction” emerges popular term these publications.

Language: Английский

Machine learning modeling of supercritical water gasification for predictive hydrogen production from waste biomass DOI
Kapil Khandelwal, Sonil Nanda, Ajay K. Dalai

et al.

Biomass and Bioenergy, Journal Year: 2025, Volume and Issue: 197, P. 107816 - 107816

Published: March 22, 2025

Language: Английский

Citations

1
Current scenario of machine learning applications to hydrothermal liquefaction via bibliometric analysis DOI Creative Commons
Tossapon Katongtung, Somboon Sukpancharoen,

Sakprayut Sinthupinyo

et al.

F1000Research, Journal Year: 2025, Volume and Issue: 13, P. 1131 - 1131

Published: Jan. 2, 2025

Background Energy shortages and global warming have been significant issues throughout history. Therefore, the search for environmentally friendly renewable energy sources is crucial achieving sustainability. Biomass gaining attention as a option, particularly through process of hydrothermal liquefaction, which converts wet biomass into bio-crude oil. Methods Hydrothermal liquefaction complex that challenging to explain, leading research on machine learning models this process. These aim predict values investigate impact variables However, development in still limited due its novelty time required comprehensive study. Thus, objective study was analyze relevant publications Scopus database, focusing indexed ML HTL keywords, understand keyword associations co-citations. Results The results reveal an increasing trend process, with growing interest from various countries. Conclusion Notably, China currently holds largest share processes, most published works falling within field engineering. “liquefaction” emerges popular term these publications.

Language: Английский

Citations

0
Current scenario of machine learning applications to hydrothermal liquefaction via bibliometric analysis DOI Creative Commons
Tossapon Katongtung, Somboon Sukpancharoen,

Sakprayut Sinthupinyo

et al.

F1000Research, Journal Year: 2025, Volume and Issue: 13, P. 1131 - 1131

Published: March 11, 2025

Background Energy shortages and global warming have been significant issues throughout history. Therefore, the search for environmentally friendly renewable energy sources is crucial achieving sustainability. Biomass gaining attention as a option, particularly through process of hydrothermal liquefaction, which converts wet biomass into bio-crude oil. Methods Hydrothermal liquefaction complex that challenging to explain, leading research on machine learning models this process. These aim predict values investigate impact variables However, development in still limited due its novelty time required comprehensive study. Thus, objective study was analyze relevant publications Scopus database, focusing indexed ML HTL keywords, understand keyword associations co-citations. Results The results reveal an increasing trend process, with growing interest from various countries. Conclusion Notably, China currently holds largest share processes, most published works falling within field engineering. “liquefaction” emerges popular term these publications.

Language: Английский

Citations

0