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Applicability and Trend of the Artificial Intelligence (AI) on Bioenergy Research between 1991–2021: A Bibliometric Analysis

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  • Yi Cheng

    (Department of Chemical Engineering and Applied Chemistry, Aston University, Birmingham B4 7ET, UK
    Energy and Bioproducts Research Institute (EBRI), Aston University, Birmingham B4 7ET, UK)

  • Chuzhi Zhao

    (Department of Chemistry, Imperial College London, London SW7 2BX, UK)

  • Pradeep Neupane

    (Department of Chemical Engineering and Applied Chemistry, Aston University, Birmingham B4 7ET, UK)

  • Bradley Benjamin

    (Department of Chemical Engineering and Applied Chemistry, Aston University, Birmingham B4 7ET, UK)

  • Jiawei Wang

    (Department of Chemical Engineering and Applied Chemistry, Aston University, Birmingham B4 7ET, UK
    Energy and Bioproducts Research Institute (EBRI), Aston University, Birmingham B4 7ET, UK)

  • Tongsheng Zhang

    (School of Materials Science and Engineering, South China University of Technology, Guangzhou 510640, China)

Abstract

The bibliometric analysis investigated the impact of publications on trends in the literature and bioenergy research using artificial intelligence (AI) from 1991 to 2021. In this study, 1721 publications were extracted from the Web of Science, and an analysis of the countries, authorship, institutions, journals, and keywords was visualised. In the recent decades, this field has entered an outbreak phase. India was the most productive country in this area, followed by China, Iran, and the US. It also noted several notable differences between trends and subjects in developed and developing countries. The former led this field at the initial stage and later attached importance to using AI for research feedstock and impact assessment. Developing countries encouraged the advancement of this area and emphasised the feedstock usage of phase treatment and process optimisation. In addition, a co-authorship and institutes study revealed that authors and institutes in distant regions rarely collaborated. The journal analysis shows strong links between Energy , Fuel , and Energy Conversion and Management . Machine learning is by far the most common application of artificial intelligence (AI) technology in bioenergy research, with 53% of the articles using it. In these AI-related publications, the keyword artificial neural network (ANN) appeared most frequently in the articles.

Suggested Citation

  • Yi Cheng & Chuzhi Zhao & Pradeep Neupane & Bradley Benjamin & Jiawei Wang & Tongsheng Zhang, 2023. "Applicability and Trend of the Artificial Intelligence (AI) on Bioenergy Research between 1991–2021: A Bibliometric Analysis," Energies, MDPI, vol. 16(3), pages 1-15, January.
    • Handle: RePEc:gam:jeners:v:16:y:2023:i:3:p:1235-:d:1044889
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    References listed on IDEAS

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