Bibliometric analysis of artificial intelligence applied to waste-to-syngas

The use of Artificial Intelligence techniques has grown rapidly in the field of waste-to- energy, with gasification processes benefiting from their ability to model and optimize syngas production. However, literature specifically focused on Artificial Intelligence applications in waste-to-syngas sys...

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Detalles Bibliográficos
Autores: Fornieles, Aleix, Etxegarai, Maddi, Gibert, Daniel, Planes Cid, Jordi
Tipo de recurso: artículo
Estado:Versión publicada
Fecha de publicación:2025
País:España
Institución:Varias* (Consorci de Biblioteques Universitáries de Catalunya, Centre de Serveis Científics i Acadèmics de Catalunya)
Repositorio:Recercat. Dipósit de la Recerca de Catalunya
OAI Identifier:oai:recercat.cat:10459.1/469270
Acceso en línea:https://doi.org/10.1016/j.nexus.2025.100581
https://hdl.handle.net/10459.1/469270
http://hdl.handle.net/10459.1/469270
Access Level:acceso abierto
Palabra clave:Artificial intelligence
Bibliometric Analysis
Biomass:Gasification
Syngas
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spelling Bibliometric analysis of artificial intelligence applied to waste-to-syngasFornieles, AleixEtxegarai, MaddiGibert, DanielPlanes Cid, JordiArtificial intelligenceBibliometric AnalysisBiomass:GasificationSyngasThe use of Artificial Intelligence techniques has grown rapidly in the field of waste-to- energy, with gasification processes benefiting from their ability to model and optimize syngas production. However, literature specifically focused on Artificial Intelligence applications in waste-to-syngas systems remains fragmented. This article presents a bibliometric analysis that maps the development of Artificial Intelligence driven research in this domain, focusing mainly on prediction tasks. The analysis is based on data extracted from Scopus and Web of Science, filtered through specific criteria to ensure relevance. Results show a clear rise in publications since 2020, with a dominant contribution from China and a strong focus on hydrogen prediction and neural network models. Despite the progress, this study highlights major gaps in real-time control strategies, data standardization, and industrial-scale implementation. These findings point to emerging opportunities in hybrid modeling, explainable Artificial Intelligence, and underexplored feedstocks should guide future research efforts.lean Hydrogen Partnership provided financial support for this study through the Project 101137792 – HYEILD. Aleix Fornieles is a fellow of Eurecat’s ‘‘Vicente López’’ PhD grant program. D. Gibert was supported by grant RYC2023-043607-I funded by MICIU/AEI/10.13039/ 501100011033 and FSE+. This work was funded by the project PID2022-139835NB-C22 funded by MCIN/AEI/10.13039/501100011033/ FEDER, UEElsevier2025info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttps://doi.org/10.1016/j.nexus.2025.100581https://hdl.handle.net/10459.1/469270http://hdl.handle.net/10459.1/469270reponame:Recercat. Dipósit de la Recerca de Catalunyainstname:Varias* (Consorci de Biblioteques Universitáries de Catalunya, Centre de Serveis Científics i Acadèmics de Catalunya)Inglésinfo:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2021-2023/PID2022-139835NB-C22Reproducció del document publicat a https://doi.org/10.1016/j.nexus.2025.100581Energy Nexus, 2025, vol. 20, 100581cc-by (c) Aleix Fornieles et al., 2025Attribution 4.0 Internationalinfo:eu-repo/semantics/openAccesshttp://creativecommons.org/licenses/by/4.0/oai:recercat.cat:10459.1/4692702026-05-29T05:05:01Z
dc.title.none.fl_str_mv Bibliometric analysis of artificial intelligence applied to waste-to-syngas
title Bibliometric analysis of artificial intelligence applied to waste-to-syngas
spellingShingle Bibliometric analysis of artificial intelligence applied to waste-to-syngas
Fornieles, Aleix
Artificial intelligence
Bibliometric Analysis
Biomass:Gasification
Syngas
title_short Bibliometric analysis of artificial intelligence applied to waste-to-syngas
title_full Bibliometric analysis of artificial intelligence applied to waste-to-syngas
title_fullStr Bibliometric analysis of artificial intelligence applied to waste-to-syngas
title_full_unstemmed Bibliometric analysis of artificial intelligence applied to waste-to-syngas
title_sort Bibliometric analysis of artificial intelligence applied to waste-to-syngas
dc.creator.none.fl_str_mv Fornieles, Aleix
Etxegarai, Maddi
Gibert, Daniel
Planes Cid, Jordi
author Fornieles, Aleix
author_facet Fornieles, Aleix
Etxegarai, Maddi
Gibert, Daniel
Planes Cid, Jordi
author_role author
author2 Etxegarai, Maddi
Gibert, Daniel
Planes Cid, Jordi
author2_role author
author
author
dc.subject.none.fl_str_mv Artificial intelligence
Bibliometric Analysis
Biomass:Gasification
Syngas
topic Artificial intelligence
Bibliometric Analysis
Biomass:Gasification
Syngas
description The use of Artificial Intelligence techniques has grown rapidly in the field of waste-to- energy, with gasification processes benefiting from their ability to model and optimize syngas production. However, literature specifically focused on Artificial Intelligence applications in waste-to-syngas systems remains fragmented. This article presents a bibliometric analysis that maps the development of Artificial Intelligence driven research in this domain, focusing mainly on prediction tasks. The analysis is based on data extracted from Scopus and Web of Science, filtered through specific criteria to ensure relevance. Results show a clear rise in publications since 2020, with a dominant contribution from China and a strong focus on hydrogen prediction and neural network models. Despite the progress, this study highlights major gaps in real-time control strategies, data standardization, and industrial-scale implementation. These findings point to emerging opportunities in hybrid modeling, explainable Artificial Intelligence, and underexplored feedstocks should guide future research efforts.
publishDate 2025
dc.date.none.fl_str_mv 2025
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv https://doi.org/10.1016/j.nexus.2025.100581
https://hdl.handle.net/10459.1/469270
http://hdl.handle.net/10459.1/469270
url https://doi.org/10.1016/j.nexus.2025.100581
https://hdl.handle.net/10459.1/469270
http://hdl.handle.net/10459.1/469270
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2021-2023/PID2022-139835NB-C22
Reproducció del document publicat a https://doi.org/10.1016/j.nexus.2025.100581
Energy Nexus, 2025, vol. 20, 100581
dc.rights.none.fl_str_mv cc-by (c) Aleix Fornieles et al., 2025
Attribution 4.0 International
info:eu-repo/semantics/openAccess
http://creativecommons.org/licenses/by/4.0/
rights_invalid_str_mv cc-by (c) Aleix Fornieles et al., 2025
Attribution 4.0 International
http://creativecommons.org/licenses/by/4.0/
eu_rights_str_mv openAccess
dc.publisher.none.fl_str_mv Elsevier
publisher.none.fl_str_mv Elsevier
dc.source.none.fl_str_mv reponame:Recercat. Dipósit de la Recerca de Catalunya
instname:Varias* (Consorci de Biblioteques Universitáries de Catalunya, Centre de Serveis Científics i Acadèmics de Catalunya)
instname_str Varias* (Consorci de Biblioteques Universitáries de Catalunya, Centre de Serveis Científics i Acadèmics de Catalunya)
reponame_str Recercat. Dipósit de la Recerca de Catalunya
collection Recercat. Dipósit de la Recerca de Catalunya
repository.name.fl_str_mv
repository.mail.fl_str_mv
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