Exploring the landscape of machine learning-aided research in biofuels and biodiesel: A bibliometric analysis

Abstract

This bibliometric analysis explores machine learning applications in biofuels and biodiesel research using Elsevier's Scopus database from 2013 to 2023. The research employs co-authorship, co-occurrence, citation, and co-citation analyses with fractional counting. Results indicate a significant rise in publications. Prominent funding agencies along this field include the National Natural Science Foundation of China, Brazil's Conselho Nacional de Desenvolvimento Científico e Tecnológico and the U.S. Department of Energy. Co-authorship analysis reveals contributions from 268 authors across 951 organizations in 71 countries, with strong collaboration in Asia. Citation analysis shows that 95% of articles have received at least one citation, with China and the United States leading in citation counts. This study highlights the interdisciplinary and collaborative nature of machine learning research in biofuels and biodiesel, driven by substantial contributions from key funding bodies and researchers worldwide.

Keywords

Machine learning / Biofuel / Biodiesel / Bibliometric analysis / Sustainability

Funding source

This work was supported by the Natural Sciences and Engineering Research Council of Canada (NSERC) under Grant RGPIN/05322-2019 and Grant ALLRP/565222-2021, and by Kara Technologies.

CRediT authorship contribution statement

Avinash Alagumalai: Writing - original draft, Visualization, Software, Methodology, Formal analysis, Data curation, Conceptualization. Hua Song: Writing - review & editing, Validation, Supervision, Project administration, Funding acquisition.

Declaration of competing interest

The authors declare the following financial interests/personal relationships which may be considered as potential competing interests.

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